Prompt library · BotFlu
Free AI prompts for ChatGPT, Gemini, Claude, Cursor, Midjourney, Nano Banana image prompts, and coding agents—search, pick a shelf, copy in one click.
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Choose a tab for the kind of prompts you want, search or filter, then copy any entry. Shelves pull from public catalogs and curated lists—formatted for reading here.
Act as an expert strategist and executive coach. I am going to provide you with a piece of work (text, article, or report). Your goal is to extract the most valuable, high-impact insights and turn them into a structured list of actionable takeaways. Please format your response using the following structure: 1. Core Essence (1-2 sentences max): What is the single most important message or paradigm shift from this text? 2. Top 20 Actionable Takeaways: For each takeaway, provide: - What to do: A clear, action-oriented directive starting with a verb. - Why it matters: The brief rationale or expected benefit based on the text. - How to implement: A concrete first step or a "Next Action" that can be done immediately. 3. What to Stop Doing: What common habit, mindset, or process does this text argue against? Please keep the language sharp, direct, and free of fluff.
# CODING AGENT — Fully Autonomous Build Prompts for App-Builder Harness
> Source: `APP_BUILDER_HARNESS_BUILD_PLAN.md` (35 sections, V1 scope)
> Generated via MCP `prompts.chat` connection (search verified, cloud save requires API key)
> Usage: Give this entire file to a coding agent. It must build top-to-bottom with ZERO questions.
---
## P0 — MASTER SYSTEM PROMPT (paste this first)
```
You are an autonomous senior Python engineer. Build the App-Builder Harness exactly as specified below. NEVER ask questions. NEVER stop for clarification. If anything is ambiguous, use the DEFAULTS section and continue.
PROJECT GOAL:
Build a reliability-first autonomous software engineering harness that turns natural-language app requests into working, verified projects with full evidence traceability:
User Intent -> Spec Agent -> Spec Validation -> Planner -> Task DAG (sequential) -> Builder -> Verification (real execution) -> Failure Classification -> Repair (max 3) -> Reverification -> Requirement Review -> Final Deliverable
CENTRAL PRINCIPLE (non-negotiable):
Every requirement must be traceable: REQ-ID -> TASK-ID -> file(s) -> TEST/CMD -> PASS evidence. Never claim "done" without evidence. Never say "AI says finished". Every PASS must have files + command output + exit_code 0.
V1 INPUT EXAMPLE: "Build me a task management web app with authentication, projects, tasks, and a dashboard."
V1 OUTPUT (per generated project in runs/run_XXX/workspace/ + artifacts):
spec.json, tasks.json, verification.json, review.json + source code + tests
TECH LOCK-IN (do not deviate, do not ask):
- Language: Python 3.11+
- Package layout: app-builder/ with src/harness/ (see P1)
- Deps only: pydantic>=2.0, pytest>=7.0. Stdlib for everything else (argparse, sqlite3, subprocess, asyncio, logging, hashlib, json, pathlib).
- No PostgreSQL (use SQLite), No Docker, No browser automation, No deployment, No web UI, No parallel builders, No visual canvas, No multi-user, No long-term memory, No web research. Sequential DAG only for V1.
- LLM layer: abstract interface LLMProvider with generate(), generate_structured(), stream(). Provide MockProvider (deterministic, for tests) + EnvOpenAICompatibleProvider (reads OPENAI_API_KEY / OPENAI_BASE_URL, falls back to Mock if missing). Agents depend ONLY on interface.
- Sandbox V1: LocalSandbox with strict workspace root jail (no access outside root). Docker later, not now.
- Observability from day 1: JSONL runs/run_XXX/events.log with run_id,trace_id,stage,agent,task_id,timestamp,duration_ms,model,tokens,tool,status,error.
- Checkpoints: runs/run_XXX/state.json + checkpoints/001-spec.json,002-plan.json,003-task-TASKxxx.json,004-verification.json etc. Must support resume after crash.
- CLI: python -m harness.cli new "request" using argparse only. Output 5-stage progress as in spec.
- All models = Pydantic v2 BaseModel. All enums = str Enum. All IDs: REQ-XXX, TASK-XXX, TEST-XXX, run_XXX.
DEFAULTS (when in doubt, do this, don't ask):
- Stack inference: if request says "web app" -> frontend=React, backend=FastAPI, db=SQLite. If "API" -> FastAPI+SQLite. If "todo" -> FastAPI+SQLite+minimal HTML. If unspecified -> FastAPI+SQLite.
- App type: default "web". Pages: infer from nouns (dashboard, login, projects, tasks). Data model: infer entities. Must_have = all explicit user nouns. Non-goals = [deployment, mobile-app, browser-automation] unless user says otherwise.
- Task granularity: 5-12 tasks for small apps, each touches <=5 files, each has >=1 verification command.
- Verification defaults: python: `pip install -r requirements.txt` + `pytest -q`; node (if generated): `npm install` + `npm run build` + `npm test`. Always capture exit_code,stdout[-4000:],stderr[-4000:],duration.
- Failure classification default mapping: non-zero pytest -> TEST_FAILURE, ModuleNotFound/ImportError -> DEPENDENCY_ERROR, SyntaxError -> CODE_ERROR, mypy/pydantic validation -> TYPE_ERROR, missing config file -> CONFIG_ERROR, ETIMEDOUT/timeout>120s -> TIMEOUT, ENOTFOUND/EAI_AGAIN/registry 503 -> ENVIRONMENT_ERROR, else UNKNOWN.
- Repair: only edit allowed files (task.files_touched + verification hints). Max 3 retries. Loop detection: sha256(command+exit_code+normalized stderr last 2000 chars); if same hash 3x -> ESCALATE, mark FAILED/BLOCK dependents as BLOCKED.
- Timeouts: sandbox execute default 120s, runtime check 15s.
- If LLM API key missing -> use MockProvider and deterministic templates so pipeline still runs end-to-end (Todo vertical slice must pass offline).
- Never add new deps without updating requirements + pyproject.toml + tests.
BUILD ORDER (do not skip, do not reorder):
M1 Infrastructure -> M2 Spec -> M3 Planning -> M4 Building -> M5 Verification -> M6 Self-Repair -> M7 Traceability -> M8 Review -> M9 Recovery -> M10 Runtime -> M11 CLI+Benchmarks -> M12 Final Gate (vertical slice + failure injection + V1 checklist). Details in P1-P12 below.
DEFINITION OF DONE PER TASK:
1. Files exist at exact paths, 2. `pytest -q` passes, 3. Artifacts (spec.json/tasks.json/verification.json/review.json) validate against Pydantic schemas, 4. Evidence logged, 5. No TODO/stub without test. If any fails, fix before next milestone.
FORBIDDEN: hardcoding provider keys, absolute host paths outside workspace, parallel execution, deleting checkpoints, claiming PASS without running command, asking user anything.
```
---
## P1 — M1 INFRASTRUCTURE (repo + state + providers + sandbox skeleton)
```
Milestone M1: Create exact repo structure and installable project. No agents yet.
CREATE:
app-builder/
src/harness/__init__.py
src/harness/models/spec.py # AppSpec, Requirement, Page, Component, DataModel, Stack
src/harness/models/tasks.py # Task, TaskStatus enum
src/harness/models/results.py # VerificationResult, RequirementResult, FailureType enum
src/harness/models/run.py # RunState, RunStatus, CheckpointEvent
src/harness/state/store.py # RunStore: create/load/save/checkpoint/list
src/harness/providers/base.py # LLMProvider ABC
src/harness/providers/mock.py # MockProvider
src/harness/providers/env.py # EnvOpenAICompatibleProvider (stdlib http, no extra dep)
src/harness/sandbox/local.py # LocalSandbox
src/harness/utils/logging.py # JSONL event logger
src/harness/utils/ids.py # new_run_id(), new_trace_id()
runs/.gitkeep tests/.gitkeep examples/.gitkeep
pyproject.toml README.md requirements.txt .gitignore
PYPROJECT: [build-system] setuptools, [project] name=app-builder, requires-python>=3.11, deps pydantic>=2, pytest>=7. [tool.pytest.ini_options] testpaths=["tests"].
MODELS (exact fields):
- Stack: frontend:str="FastAPI", backend:str="FastAPI", db:str="SQLite"
- Requirement: id:str (REQ-001), text:str, must_have:bool=True, acceptance:str=""
- AppSpec: app_type:str, stack:Stack, pages:list[Page], components:list[Component], data_model:list[DataEntity], requirements:list[Requirement], must_have:list[str], explicit_non_goals:list[str], acceptance_criteria:list[str]
- Task: id:str, description:str, depends_on:list[str]=[], files_touched:list[str]=[], requirements:list[str]=[], verification:list[str]=[], status:TaskStatus=PENDING
- TaskStatus: PENDING,READY,RUNNING,COMPLETED,FAILED,TIMED_OUT,BLOCKED
- VerificationResult: task_id:str, status:str (PASS/FAIL), command:str, exit_code:int, stdout:str, stderr:str, duration_ms:int, failure_type:FailureType|None
- FailureType: CODE_ERROR,TEST_FAILURE,TYPE_ERROR,DEPENDENCY_ERROR,CONFIG_ERROR,ENVIRONMENT_ERROR,TIMEOUT,UNKNOWN
- RequirementResult: requirement_id:str, status:str, evidence:list[str]=[], missing:list[str]=[]
- RunState: run_id, trace_id, user_request:str, workspace:str, spec:AppSpec|None, plan:list[Task]=[], task_results:dict[str,Any]= {}, verification_results:list[VerificationResult]=[], review:dict|None, checkpoints:list[str]=[], status:RunStatus
- RunStatus: CREATED,SPEC_DONE,PLAN_DONE,BUILDING,VERIFYING,REPAIRING,REVIEW_DONE,PASS,FAIL
SANDBOX LocalSandbox(root:Path):
create_workspace(run_id)->Path; write_file(rel:str,content:str)->Path (reject ../ escape); read_file(rel)->str; list_files(rel=".")->list[str]; delete_file(rel); execute(cmd:list[str]|str, cwd:Path|None, timeout_s:int=120)->dict{exit_code,stdout,stderr,duration_ms} via subprocess.run(shell=False if list, True only if str + log warning).
STORE RunStore(base=Path("runs")):
create_run(user_request)->RunState; save(state); load(run_id)->RunState; checkpoint(state, name:str, payload:dict); list_runs()->list[str]; resume(run_id)->RunState.
LOGGING emit(base, run_id, trace_id, stage, agent, task_id, status, duration_ms=0, model="", tokens=0, tool="", error="") appends JSON line to runs/{run_id}/events.log.
LLMProvider ABC: generate(prompt:str, system:str="")->str; generate_structured(prompt:str, schema:type[BaseModel])->BaseModel; stream(prompt:str)->Iterator[str] (Mock yields words).
MockProvider: generate returns deterministic template echo; generate_structured returns schema.model_validate({minimal valid}) for AppSpec/TaskList used in tests.
Env provider: if no env key, delegate to MockProvider (so offline tests pass).
ACCEPTANCE:
- `pip install -e .` succeeds, `pytest -q` collects (even if 0 tests, add tests/test_models.py validating AppSpec + Task roundtrip + sandbox jail rejects ../ + store save/load).
- No network needed. No questions.
DO NOT: build agents, DAG, verifier yet.
```
## P2 — M2 SPEC AGENT + VALIDATOR
```
Milestone M2: User request -> validated spec.json. No code generation.
FILES:
src/harness/agents/spec.py # SpecAgent(llm:LLMProvider).generate(user_request:str)->AppSpec (prompt template + parse + fallback deterministic parser if LLM fails)
src/harness/validation/spec_validator.py # SpecValidator.validate(spec)->list[str] errors (empty=PASS)
tests/test_spec.py
SPEC AGENT LOGIC:
System: "You output ONLY valid JSON matching AppSpec schema. No prose. Infer stack/pages/data_model/requirements/must_have/non_goals/acceptance."
User template includes: request + stack defaults from P0 + require REQ-001..N, acceptance per requirement.
Post-process: assign REQ-001.. sequential, ensure must_have non-empty, acceptance_criteria non-empty, explicit_non_goals default 3 items.
If LLM JSON invalid -> fallback rule-based parser (keyword scan for auth/projects/tasks/dashboard -> entities) so offline PASS.
VALIDATOR CHECKS (each returns error string):
Structural: app_type non-empty, stack.* non-empty, requirements>=1, acceptance_criteria>=1, IDs unique matching REQ-\\d{3}.
Logical: every page.entity (if field) in data_model names; no requirement text substring in explicit_non_goals; if any requirement mentions API/backend then stack.backend non-empty; duplicate requirement text (case-insensitive) error; every must_have maps to >=1 requirement (substring or explicit link).
Pipeline helper: spec_stage(store, run_id, llm, max_attempts=2): generate->validate; if FAIL retry with error feedback; if still FAIL raise; else save checkpoint 001-spec.json + state.spec.
ACCEPTANCE:
- tests/test_spec.py: valid request -> PASS; conflicting non-goal (req "login" + non-goal "login") -> FAIL detected; missing acceptance -> FAIL; retry loop succeeds on 2nd attempt (mock failing once).
- Example artifact examples/todo_spec.json (Todo app, 4 REQs).
DO NOT: plan or build.
```
## P3 — M3 PLANNER + DAG ENGINE (sequential)
```
Milestone M3: AppSpec -> tasks.json DAG + sequential executor. No parallel.
FILES:
src/harness/agents/planner.py # PlannerAgent(llm).plan(spec)->list[Task]
src/harness/orchestration/dag.py # DagEngine + PlanValidator
tests/test_planner.py tests/test_dag.py
PLANNER RULES (enforce in code, not just prompt):
1. DB/schema task(s) first (files containing model/schema/db/migration).
2. Shared components before pages.
3. Auth infra before authenticated routes (files with auth).
4. Every REQ -> >=1 task.requirements; every task -> >=1 verification string + >=1 requirements + >=1 files_touched.
5. IDs TASK-001.. sequential, depends_on only earlier IDs (acyclic). If LLM violates, auto-fix: sort + repair deps deterministically.
6. LLM prompt: include spec JSON + ordering rules + output JSON list only. Fallback template: [schema/setup, auth, core entities CRUD, pages/API, tests/verify] if LLM fails.
7. files_touched must be relative POSIX paths inside workspace (no absolute, no ..).
DAG ENGINE (sequential V1):
class DagEngine(tasks:list[Task]): statuses dict; get_ready()->list[Task] (PENDING + all deps COMPLETED); mark(task_id,status); is_done(); blocked_propagation(): if dep FAILED/TIMED_OUT -> dependents BLOCKED; topological_order() raises on cycle.
statuses: PENDING,READY (computed, not stored — store PENDING until dispatched),RUNNING,COMPLETED,FAILED,TIMED_OUT,BLOCKED. FAILED != TIMED_OUT preserved.
Executor (in orchestration/runner.py skeleton, full impl M4/M5 but DAG part now): loop find READY -> RUNNING -> (placeholder hook) -> COMPLETED; if none READY and not done -> deadlock error listing BLOCKED.
PlanValidator: duplicate IDs, unknown dep, cycle, orphan REQ (no task), task without verification, overlapping files warning (not error in V1, but log for future parallel).
ACCEPTANCE:
- Todo spec (4 REQs) -> 5-8 tasks, all rules hold (assert in test).
- Cycle fixture -> validator error + engine raises.
- TASK-001 FAIL -> dependents BLOCKED (test).
- Checkpoint 002-plan.json saved via store.
DO NOT: run builders, no concurrency, no file-conflict scheduler yet.
```
## P4 — M4 BUILDER + TOOLS (sandbox-gated)
```
Milestone M4: Task -> real files. Minimal context only.
FILES:
src/harness/agents/builder.py # BuilderAgent(llm, sandbox, store)
src/harness/tools/files.py # read_file, write_file, list_files wrappers enforcing allowlist
tests/test_builder.py
BUILDER INPUT (only this, never full dump):
task:Task, requirements:list[Requirement] (filtered to task.requirements), spec_summary:dict (app_type,stack,data_model names), dependency_results:list[VerificationResult], allowed_files:list[str] (=task.files_touched), verification:list[str].
BUILDER PROMPT TEMPLATE:
"Implement TASK-{id}: {description}. Requirements: {req texts+acceptance}. Allowed files (ONLY these): {list}. Prior results: {dep summary}. Output file contents as JSON map {{relpath: content}}. Use FastAPI+SQLite if backend. Include imports, no stubs. If test file required, include pytest tests."
TOOL ENFORCEMENT:
Builder may call only sandbox.write_file/read_file/list_files within workspace + within allowed_files (write) — any other path -> PermissionError logged, task FAILED (TYPE_ERROR? use CONFIG_ERROR).
Never subprocess directly — only via sandbox.execute in M5. Builder writes files then returns {written:[...]}.
SCAFFOLD DEFAULTS (offline-safe):
If LLM returns invalid map -> fallback writes minimal FastAPI app: workspace/src/main.py (health GET /health), workspace/src/models.py, workspace/requirements.txt (fastapi,uvicorn,pydantic,pytest), workspace/tests/test_health.py asserting /health via TestClient or file exists check if fastapi missing. This guarantees vertical slice passes offline.
ACCEPTANCE:
- Given Todo TASK-001 (schema) in temp workspace -> files created under allowlist, outside-write rejected.
- Mock LLM offline still produces runnable scaffold + pytest passes.
- Store checkpoint 003-task-{id}.json after each task.
DO NOT: verify (M5) or repair yet — just write files.
```
## P5 — M5 VERIFICATION (real execution) + FAILURE CLASSIFICATION
```
Milestone M5: Actually run commands, capture structured results, classify.
FILES:
src/harness/verification/verifier.py # Verifier(sandbox).run(task, workspace, commands)->list[VerificationResult]
src/harness/verification/classifier.py # FailureClassifier.classify(result)->FailureType
tests/test_verifier.py tests/test_classifier.py
VERIFIER:
def verify_task(task:Task, workspace:Path)->list[VerificationResult]:
for cmd_str in task.verification (default if empty: ["pytest -q"]):
parse cmd_str via shlex.split (POSIX) -> sandbox.execute(cmd, cwd=workspace, timeout 120s)
capture exit_code,stdout[-4000:],stderr[-4000:],duration_ms; status PASS if 0 else FAIL; failure_type=None if PASS else classify().
persist 004-verification-{task_id}.json via store.
Also suite-level: verify_workspace(workspace, extra=["pip install -r requirements.txt" if exists]) helper.
Must NOT do static inspection only — must execute. Timeout -> TIMED_OUT + failure_type TIMEOUT.
CLASSIFIER (regex + exit_code, deterministic):
- TIMEOUT if duration>=timeout or "timed out"/"TimeoutExpired".
- ENVIRONMENT_ERROR if "EAI_AGAIN|ENOTFOUND|503|registry.*unavailable|Network is unreachable|pip.*Could not fetch".
- DEPENDENCY_ERROR if "ModuleNotFound|ImportError|No module named|npm ERR.*404|Could not resolve dependency".
- TYPE_ERROR if "mypy|TypeError:.*expected|pydantic.*ValidationError|TS2322|Property.*does not exist".
- TEST_FAILURE if "FAILED|AssertionError|1 failed|FAIL tests/" and exit!=0 and not above.
- CONFIG_ERROR if "FileNotFound.*config|missing.*pyproject|requirements.*not found|PORT in use" etc.
- CODE_ERROR if "SyntaxError|IndentationError|NameError|ReferenceError".
- else UNKNOWN.
Unit-test each with fixtures from spec (e.g., "npm registry unavailable" -> ENVIRONMENT_ERROR).
ACCEPTANCE:
- Create broken workspace (syntax error) -> verifier returns FAIL + CODE_ERROR, exit!=0, stderr captured.
- Good scaffold from M4 -> PASS.
- Timeout fixture (sleep 3 with timeout 1) -> TIMED_OUT.
DO NOT: auto-repair yet.
```
## P6 — M6 REPAIR LOOP + LOOP DETECTION
```
Milestone M6: FAIL -> classify -> repair -> reverify, max 3, loop guard.
FILES:
src/harness/orchestration/repair.py # RepairLoop + LoopDetector
src/harness/agents/repair_agent.py # RepairAgent(llm, sandbox)
tests/test_repair.py
LOOP DETECTOR:
def sig(cmd, exit_code, stderr): return sha256(f"{cmd}|{exit_code}|{normalize(stderr[-2000:])}".encode()).hexdigest()
normalize: lowercase, strip numbers/paths/timestamps (regex), collapse whitespace.
LoopDetector(seen:dict[sig,count]): add(sig)-> (is_loop:bool, count:int); is_loop True if count>=3 same sig.
REPAIR AGENT:
Input: task, spec_slice, failing VerificationResult + classifier label + last file contents (read via sandbox, truncated 8000 chars).
Prompt: "Fix {FailureType} in {files}. Error: {stderr}. Do NOT rewrite unrelated files. Output JSON map {{relpath: full corrected content}}."
Strategy by type: ENVIRONMENT_ERROR -> DO NOT rewrite code, retry once after 2s, if persists mark FAILED (env, not code); DEPENDENCY_ERROR -> fix requirements.txt/pyproject; TYPE_ERROR/CODE_ERROR/TEST_FAILURE -> patch code; CONFIG_ERROR -> fix config; TIMEOUT -> reduce scope/increase timeout once, else FAIL.
REPAIR LOOP:
def run_with_repair(task, workspace, verifier, repair_agent, max_retries=3):
attempt=0; while True: results=verifier.verify_task(...); if all PASS return PASS; classify; sig check -> if loop: log WARNING, one final repair try, then ESCALATE FAILED; if attempt>=max_retries: mark FAILED; else repair (write patched files via sandbox), checkpoint repair_started, attempt+=1, reverify.
Update DagEngine statuses + store.task_results.
ACCEPTANCE:
- Inject TypeScript/Python syntax error -> loop detects, repairs, PASS within <=3 (test with Mock LLM returning fixed content).
- Identical failure 3x (mock repair returns same broken file) -> loop stops, ESCALATE, task FAILED, dependents BLOCKED.
- ENVIRONMENT_ERROR does NOT trigger code rewrite (assert files unchanged, 1 retry only).
DO NOT: checkpoints beyond repair_started/task_completed yet (M9).
```
## P7 — M7 TRACEABILITY + M8 REVIEW AGENT
```
Milestone M7+M8: Requirement -> Task -> File -> Test -> Evidence + final audit gate.
FILES:
src/harness/trace/matrix.py # build_matrix(spec, tasks, verification_results, workspace)->dict[REQ, {tasks, files, tests, status}]
src/harness/agents/reviewer.py # ReviewAgent(llm|rule-based).review(spec, matrix, workspace)->dict{requirements:[{id,status,evidence,missing}], overall_status}
tests/test_trace.py tests/test_review.py
MATRIX:
For each REQ: tasks = [t for t in plan if REQ in t.requirements]; files = union files_touched; tests/commands = union verification; evidence = [f for f in files if workspace/f exists] + [v.command for v in results if v.status PASS and v.task_id in tasks]; status PASS only if >=1 file exists AND >=1 PASS result covering it, else FAIL with missing=[reasons].
No evidence -> FAIL (never PASS on prose).
REVIEWER (rule-based default, LLM optional):
Rule pass: check file exists + PASS verification + (if acceptance mentions keyword, grep file for keyword, else warn). LLM may add rationale but cannot override FAIL->PASS without evidence.
Output JSON exactly: {"requirements": [{"id","status":"PASS|FAIL","evidence":[paths+commands],"missing":[]}], "overall_status":"PASS|FAIL"} saved as review.json.
Overall PASS only if all REQs PASS.
ACCEPTANCE:
- Todo run with all PASS -> matrix shows 4/4, review overall PASS with evidence paths.
- Delete one implementation file -> that REQ FAIL with missing=["file src/... not found"], overall FAIL.
- review.json validates against RequirementResult list schema.
DO NOT: runtime/browser yet.
```
## P8 — M9 CHECKPOINTING + RECOVERY
```
Milestone M9: Crash-safe resume.
FILES:
src/harness/state/checkpoints.py # CheckpointManager (thin over RunStore)
tests/test_recovery.py
EVENTS (must emit via store.checkpoint):
spec_created, plan_created, task_started, task_completed, verification_completed, repair_started, review_completed.
Layout: runs/{run_id}/state.json (latest RunState), checkpoints/{seq:03d}-{event}-{task?}.json, workspace/ (generated app), events.log.
RESUME LOGIC in orchestration/pipeline.py::resume(run_id):
load state.json; find last completed checkpoint; recompute DAG statuses from task_results+verification_results; requeue PENDING/READY/RUNNING->PENDING (RERUN), keep COMPLETED/FAILED/BLOCKED; continue pipeline without redoing COMPLETED tasks (assert file hashes unchanged).
ACCEPTANCE:
- Start Todo run, kill after TASK-002 (simulate by saving partial state), resume() completes remaining without redoing TASK-001 (assert events.log shows TASK-001 once).
- Corrupt state.json -> resume raises clear error (not silent).
- tests cover checkpoint file naming + sequence.
DO NOT: parallel yet.
```
## P9 — M10 RUNTIME VERIFICATION (no browser in V1)
```
Milestone M10: Prove built app actually starts and serves.
FILES:
src/harness/verification/runtime.py # RuntimeVerifier
tests/test_runtime.py
RUNTIME VERIFIER STEPS (FastAPI default, Node fallback):
1. Detect entry: src/main.py:app or app.py:app or package.json main. If none, FAIL (CONFIG_ERROR).
2. Start: `python -m uvicorn src.main:app --port {free_port}` or `npm run dev -- --port {port}` via subprocess.Popen (through sandbox root), wait up to 15s.
3. Checks: process alive, TCP port open (socket.connect), GET /health or / returns 2xx, GET /docs or /api/health if exists 2xx, SQLite file exists/connects if expected.
4. Capture logs, kill process, return {status PASS/FAIL, checks:[{name, ok, detail}], evidence:[log snippet, http status]}.
5. On FAIL classify (CONFIG/DEPENDENCY/ENVIRONMENT) for repair loop reuse.
BROWSER (V1: STUB ONLY): create src/harness/verification/browser.py with `def verify_acceptance(...): raise NotImplementedError("Browser verification deferred post-V1")` + test asserting skip. Do NOT implement Playwright/Selenium now.
ACCEPTANCE:
- M4 scaffold app -> runtime PASS (health 200).
- Broken port (app exits) -> FAIL with diagnostics, repair hint.
DO NOT: Docker, parallel.
```
## P10 — M11 OBSERVABILITY + CLI + BENCHMARKS
```
Milestone M11: Operable harness.
OBSERVABILITY (src/harness/utils/logging.py finalize):
Every stage emits JSONL with all fields: run_id,trace_id,stage,agent,task_id,timestamp,duration_ms,model,tokens,tool,status,error. Provide `python -m harness.cli logs <run_id>` to pretty-print + `stats` (counts, retries, token sum, duration). Test asserts required keys on every line.
CLI (src/harness/cli.py, argparse only):
`builder new "request" [--run-id X --workspace Y --max-retries 3]` prints:
[1/5] Generating specification... ✓
[2/5] Planning N tasks... ✓
[3/5] Building... ✓ TASK-001 ... (⚠ fail + ↻ repairing)
[4/5] Verifying... ✓ Build ✓ Tests ✓ Runtime
[5/5] Reviewing requirements... ✓ 14/14 satisfied
BUILD COMPLETE / BUILD FAILED with paths to spec/tasks/verification/review.json
Also `builder resume <run_id>`, `builder review <run_id>`, `builder logs <run_id>`.
Map to orchestration/pipeline.py::run_new() orchestrating M2-M10 sequentially.
BENCHMARKS (benchmarks/*.json + src/harness/bench/runner.py):
5 fixed cases: todo, crud-dashboard, auth-app, api-db, ecommerce-prototype (each: request string + min REQs + expected tasks range). Runner executes pipeline with MockProvider, records {build_success, test_success, req_completion, repair_count, duration_s, tokens}. `pytest benchmarks/` or `builder bench --quick` (runs todo only). Do NOT judge by looks — assert metrics JSON written.
ACCEPTANCE:
- `builder new "Build a Todo app"` offline -> BUILD COMPLETE, artifacts exist, 5-stage output matches regex.
- events.log has all keys, bench quick passes.
DO NOT: web UI, Docker prod.
```
## P11 — M12 FINAL GATE: VERTICAL SLICE + FAILURE INJECTION + V1 CHECKLIST
```
Milestone M12: Prove reliability, then freeze V1.
TASKS (do in order, all must PASS):
1. VERTICAL SLICE: `builder new "Build a Simple Todo App with add/list/complete"` -> expect SPEC(>=3 REQs)->PLAN(>=3 tasks)->BUILD(files)->VERIFY(pytest PASS)->RUNTIME PASS->REVIEW PASS. Save under runs/demo_todo/. If any step FAILs, fix harness, do not proceed.
2. FAILURE INJECTION: introduce SyntaxError into workspace/src/main.py, rerun verify -> must DETECT (FAIL+CODE_ERROR), LOCALIZE (task_id), CAPTURE diagnostics, SEND to repair, REPAIR, REVERIFY PASS. Then inject identical failure 3x with no-op repair mock -> must WARNING->ESCALATE->FAILED+BLOCKED (assert).
3. V1 CHECKLIST (all ✓): valid spec, validated spec, valid DAG, sequential execution, real files, real verification, localized failures, repaired failures, loop detection, checkpoints+resume, traceability matrix, final review, working project in runs/.
4. DOCS: README.md (quickstart builder new/resume/logs, architecture diagram ASCII from plan §34, evidence principle), examples/todo_run/ (spec/tasks/verification/review JSON copies).
5. `pytest -q` entire repo green, `builder bench --quick` green.
FORBIDDEN IN V1 (assert not present): docker/, web_ui/, parallel workers, browser automation beyond stub, postgres, network research.
If all green -> tag V1 DONE. If not, loop M1-M11 fixes, never ship red.
```
---
## GLOBAL RULES APPENDIX (coding agent must obey)
1. NEVER ask questions. Use P0 defaults.
2. Build sequentially M1->M12. Do not start M(N+1) if M(N) tests red. Run `pytest -q` after each milestone.
3. Keep diffs small, files focused (<400 lines each, split if larger).
4. Every new module needs a test file. Every bug fix needs a regression test.
5. No secrets in repo. No absolute paths. No `shell=True` except verifier with logged warning.
6. Evidence over claims: every status change logs to events.log + checkpoint.
7. If LLM call fails/timeouts -> fallback deterministic path so pipeline never blocks.
8. Final deliverable per run: workspace/ + spec.json + tasks.json + verification.json + review.json + events.log + state.json.
9. End-of-run summary must list per REQ: Implemented? Tested? Runtime? Evidence? PASS/FAIL — never "AI says finished".
## prompts.chat MCP NOTE
- Searched via `prompts-chat_search_prompts/skills` (public, OK, 0 hits for niche harness queries — expected).
- `improve_prompt` / `save_prompt` require API key (`Authentication required`). To publish: set `PROMPTS_CHAT_API_KEY` in env, then call `prompts-chat_save_prompt(title="App-Builder Harness Autonomous Prompts", content=<this file>)`. Local file is authoritative until then.
- Suggested tags if publishing: `coding-agent, autonomous, python, harness, spec-driven, verification`.# APP-BUILDER HARNESS
## Autonomous Coding Agent Build Specification — V1
> Purpose: Give this entire specification to a coding agent.
>
> The agent must implement the project sequentially from M1 through M12.
>
> **ZERO clarification questions. ZERO unverified completion claims.**
---
# 0. OPERATING CONTRACT
You are an autonomous senior Python engineer.
Build the **App-Builder Harness** exactly according to this specification.
You MUST:
1. Work sequentially from **M1 → M12**.
2. Never ask the user for clarification.
3. Resolve ambiguity using the defaults defined here.
4. Run real tests and commands before declaring PASS.
5. Stop milestone progression whenever its acceptance tests are red.
6. Repair failures before proceeding.
7. Record evidence for every completed requirement.
8. Keep the implementation inside the V1 scope.
9. Never claim success based solely on LLM output.
10. Finish only when the complete V1 Final Gate passes.
---
# 1. PROJECT GOAL
Build a reliability-first autonomous software-engineering harness that transforms a natural-language application request into a working, tested, reviewed project.
Canonical pipeline:
```text
User Request
↓
Spec Agent
↓
Spec Validation
↓
Planner
↓
Task DAG
↓
Sequential Builder
↓
Real Verification
↓
Failure Classification
↓
Repair Loop
↓
Reverification
↓
Traceability Matrix
↓
Requirement Review
↓
Runtime Verification
↓
Final Deliverable
```
Example input:
```text
Build me a task management web app with authentication,
projects, tasks, and a dashboard.
```
---
# 2. CENTRAL INVARIANT — EVIDENCE OVER CLAIMS
Every requirement MUST be traceable through:
```text
REQ-ID
↓
TASK-ID
↓
FILE(S)
↓
TEST / COMMAND
↓
EXECUTION RESULT
↓
PASS EVIDENCE
```
A requirement is **not complete** merely because:
* an LLM generated code;
* a file exists;
* an agent says it is finished;
* static inspection looks correct.
A PASS requires real evidence.
Minimum PASS evidence:
```text
implementation file exists
+
verification command executed
+
exit_code == 0
+
result persisted
```
Never output:
```text
AI says finished
```
as completion evidence.
---
# 3. V1 SCOPE LOCK
## Harness runtime
* Python 3.11+
* Pydantic >= 2.0
* pytest >= 7.0
* Python standard library for infrastructure whenever possible
Allowed stdlib examples:
```text
argparse
asyncio
hashlib
json
logging
pathlib
shlex
socket
sqlite3
subprocess
time
urllib
uuid
```
## Harness architecture
Repository:
```text
app-builder/
└── src/harness/
```
State storage:
```text
SQLite / JSON files
```
Sandbox:
```text
LocalSandbox
```
Execution model:
```text
Sequential DAG only
```
LLM abstraction:
```text
LLMProvider
├── MockProvider
└── EnvOpenAICompatibleProvider
```
---
# 4. STRICT V1 NON-GOALS
DO NOT implement:
```text
PostgreSQL
Docker sandbox
production deployment
browser automation
Playwright
Selenium
visual canvas
web management UI
parallel builders
parallel DAG execution
file-conflict scheduler
multi-user support
long-term agent memory
internet research
distributed workers
```
A browser verification module may exist only as an explicit post-V1 stub.
---
# 5. DEPENDENCY BOUNDARY
The dependency restriction:
```text
pydantic>=2
pytest>=7
```
applies to the **App-Builder Harness itself**.
Generated applications may contain their own:
```text
requirements.txt
package.json
```
according to their inferred stack.
Do not silently add dependencies to the harness.
Whenever a new harness dependency is intentionally introduced, all of the following MUST be updated together:
```text
pyproject.toml
requirements.txt
tests
documentation
```
---
# 6. OFFLINE BEHAVIOR
"No API key" MUST never block the pipeline.
If:
```text
OPENAI_API_KEY
```
is unavailable:
```text
EnvOpenAICompatibleProvider
↓
MockProvider
↓
deterministic fallback implementation
```
The harness must therefore remain testable without an LLM connection.
"Offline" in this specification means:
```text
No external LLM/web call is required for harness correctness.
```
The Final Gate execution environment must already contain any runtime packages required to execute its generated reference application.
Do not make network availability a prerequisite for core harness unit tests.
---
# 7. DEFAULTS
When ambiguity exists, DO NOT ask.
Use these defaults.
## Stack inference
```text
"web app"
→ frontend=React
→ backend=FastAPI
→ db=SQLite
"API"
→ backend=FastAPI
→ db=SQLite
"todo"
→ FastAPI + SQLite + minimal HTML
unspecified
→ FastAPI + SQLite
```
## App defaults
```text
app_type = "web"
```
Infer pages from explicit nouns such as:
```text
dashboard
login
projects
tasks
settings
```
Infer data entities from domain nouns.
Every explicit feature noun becomes a `must_have` candidate.
Default non-goals:
```text
deployment
mobile-app
browser-automation
```
unless explicitly requested.
## Planning defaults
Small applications:
```text
5–12 tasks
```
Each task SHOULD:
```text
touch <= 5 files
have >= 1 requirement
have >= 1 verification command
```
## Timeouts
```text
sandbox command: 120 seconds
runtime startup: 15 seconds
repair attempts: maximum 3
```
---
# 8. IDENTIFIER CONTRACT
Use deterministic ID formats.
```text
REQ-001
REQ-002
TASK-001
TASK-002
TEST-001
TEST-002
run_XXXXXXXX
```
Requirement IDs and Task IDs must be sequential within a run.
Never reuse an ID for a different object.
---
# 9. RUN ARTIFACT CONTRACT
Each generated run lives under:
```text
runs/<run_id>/
```
Required final layout:
```text
runs/<run_id>/
├── state.json
├── spec.json
├── tasks.json
├── verification.json
├── review.json
├── events.log
├── checkpoints/
│ ├── 001-spec_created.json
│ ├── 002-plan_created.json
│ └── ...
└── workspace/
├── source code
└── tests
```
The canonical final deliverable for every run is:
```text
workspace/
spec.json
tasks.json
verification.json
review.json
events.log
state.json
```
---
# 10. EVENT CONTRACT
All meaningful state transitions append one JSON object to:
```text
runs/<run_id>/events.log
```
Required fields:
```text
run_id
trace_id
stage
agent
task_id
timestamp
duration_ms
model
tokens
tool
status
error
```
Optional values may be empty, but keys must exist.
---
# 11. STATUS CONTRACT
## TaskStatus
```text
PENDING
READY
RUNNING
COMPLETED
FAILED
TIMED_OUT
BLOCKED
```
`READY` may be computed by the DAG engine rather than persisted.
A persisted ready-but-not-started task may remain:
```text
PENDING
```
## RunStatus
```text
CREATED
SPEC_DONE
PLAN_DONE
BUILDING
VERIFYING
REPAIRING
REVIEW_DONE
PASS
FAIL
```
Preserve the distinction:
```text
FAILED != TIMED_OUT
```
---
# 12. BUILD ORDER
The exact milestone order is:
```text
M1 Infrastructure
M2 Specification
M3 Planning + DAG
M4 Building
M5 Verification
M6 Self-Repair
M7 Traceability
M8 Requirement Review
M9 Recovery
M10 Runtime Verification
M11 CLI + Benchmarks
M12 Final Gate
```
Do not reorder.
Do not start milestone `M(N+1)` while `M(N)` has failing acceptance tests.
Run:
```bash
pytest -q
```
after every milestone.
---
# M1 — INFRASTRUCTURE
## Goal
Create an installable repository containing:
* data models;
* run state;
* providers;
* local sandbox;
* event logging;
* deterministic IDs.
No agents yet.
## Required structure
```text
app-builder/
├── src/harness/__init__.py
├── src/harness/models/spec.py
├── src/harness/models/tasks.py
├── src/harness/models/results.py
├── src/harness/models/run.py
├── src/harness/state/store.py
├── src/harness/providers/base.py
├── src/harness/providers/mock.py
├── src/harness/providers/env.py
├── src/harness/sandbox/local.py
├── src/harness/utils/logging.py
├── src/harness/utils/ids.py
├── tests/
├── runs/
├── examples/
├── pyproject.toml
├── requirements.txt
├── README.md
└── .gitignore
```
---
## Core models
### Stack
```python
frontend: str = "FastAPI"
backend: str = "FastAPI"
db: str = "SQLite"
```
### Requirement
```python
id: str
text: str
must_have: bool = True
acceptance: str = ""
```
### AppSpec
```python
app_type: str
stack: Stack
pages: list[Page]
components: list[Component]
data_model: list[DataEntity]
requirements: list[Requirement]
must_have: list[str]
explicit_non_goals: list[str]
acceptance_criteria: list[str]
```
The source specification does not fully define the fields of `Page`, `Component`, and `DataEntity`.
Use minimal Pydantic models sufficient for the specified validation rules; do not add unrelated domain complexity.
### Task
```python
id: str
description: str
depends_on: list[str] = []
files_touched: list[str] = []
requirements: list[str] = []
verification: list[str] = []
status: TaskStatus = PENDING
```
### VerificationResult
```python
task_id: str
status: str
command: str
exit_code: int
stdout: str
stderr: str
duration_ms: int
failure_type: FailureType | None
```
### FailureType
```text
CODE_ERROR
TEST_FAILURE
TYPE_ERROR
DEPENDENCY_ERROR
CONFIG_ERROR
ENVIRONMENT_ERROR
TIMEOUT
UNKNOWN
```
### RequirementResult
```python
requirement_id: str
status: str
evidence: list[str] = []
missing: list[str] = []
```
### RunState
```python
run_id: str
trace_id: str
user_request: str
workspace: str
spec: AppSpec | None
plan: list[Task]
task_results: dict[str, Any]
verification_results: list[VerificationResult]
review: dict | None
checkpoints: list[str]
status: RunStatus
```
All models MUST use Pydantic v2 `BaseModel`.
All enums MUST derive from:
```python
str, Enum
```
---
## LocalSandbox
```python
LocalSandbox(root: Path)
```
Required methods:
```python
create_workspace(run_id) -> Path
write_file(rel, content) -> Path
read_file(rel) -> str
list_files(rel=".") -> list[str]
delete_file(rel)
execute(cmd, cwd=None, timeout_s=120) -> dict
```
Every path MUST be resolved against the configured sandbox root.
Reject:
```text
../
absolute paths
symlink escape
resolved paths outside root
```
Prefer command execution as:
```python
subprocess.run(list_args, shell=False)
```
Verification commands must be parsed with `shlex.split()`.
Do not use `shell=True` in V1.
Return:
```json
{
"exit_code": 0,
"stdout": "",
"stderr": "",
"duration_ms": 0
}
```
---
## RunStore
```python
RunStore(base=Path("runs"))
```
Methods:
```python
create_run(user_request) -> RunState
save(state)
load(run_id) -> RunState
checkpoint(state, event_name, payload)
list_runs() -> list[str]
resume(run_id) -> RunState
```
---
## LLMProvider
Abstract interface:
```python
generate(prompt: str, system: str = "") -> str
generate_structured(
prompt: str,
schema: type[BaseModel]
) -> BaseModel
stream(prompt: str) -> Iterator[str]
```
Agents depend ONLY on this abstraction.
### MockProvider
Must be:
```text
deterministic
offline-safe
test-friendly
```
### EnvOpenAICompatibleProvider
Read:
```text
OPENAI_API_KEY
OPENAI_BASE_URL
```
If API key is absent:
```text
delegate to MockProvider
```
---
## Acceptance
Must pass:
```bash
pip install -e .
pytest -q
```
Tests must include:
```text
AppSpec roundtrip
Task roundtrip
sandbox ../ rejection
sandbox absolute-path rejection
RunStore save/load
MockProvider deterministic behavior
```
No network required.
## Forbidden
Do not implement:
```text
SpecAgent
Planner
Builder
Verifier
DAG execution
```
---
# M2 — SPECIFICATION AGENT + VALIDATOR
## Goal
Transform:
```text
user request
```
into:
```text
validated spec.json
```
No application code generation yet.
## Files
```text
src/harness/agents/spec.py
src/harness/validation/spec_validator.py
tests/test_spec.py
examples/todo_spec.json
```
---
## SpecAgent
Interface:
```python
SpecAgent(llm: LLMProvider)
generate(user_request: str) -> AppSpec
```
System instruction:
```text
Output ONLY JSON matching AppSpec.
No prose.
Infer stack, pages, data model, requirements,
must-have features, explicit non-goals and acceptance criteria.
```
Post-processing MUST:
```text
assign REQ-001..N
ensure must_have is non-empty
ensure acceptance_criteria is non-empty
populate default explicit_non_goals when absent
```
If LLM output is invalid:
```text
use deterministic rule-based fallback
```
Fallback must understand at minimum:
```text
auth
projects
tasks
dashboard
todo
```
---
## SpecValidator
Return:
```python
list[str]
```
Empty list means PASS.
### Structural checks
Verify:
```text
app_type non-empty
stack fields non-empty
requirements >= 1
acceptance_criteria >= 1
REQ IDs unique
REQ IDs match ^REQ-\d{3}$
```
### Logical checks
Verify:
```text
page.entity references known data entity when entity is present
requirement does not conflict with explicit non-goal
backend is defined when requirement mentions API/backend
requirement text is unique case-insensitively
every must_have maps to >=1 requirement
```
---
## Pipeline stage
Implement:
```python
spec_stage(
store,
run_id,
llm,
max_attempts=2
)
```
Flow:
```text
generate
→ validate
→ if invalid: regenerate with validation feedback
→ maximum 2 generation attempts
→ persist spec
→ checkpoint spec_created
```
Persist:
```text
runs/<run_id>/spec.json
```
---
## Acceptance
Test:
```text
valid Todo request → PASS
conflicting login non-goal → FAIL
missing acceptance → FAIL
duplicate requirement → FAIL
first generation invalid + second valid → PASS
```
Create:
```text
examples/todo_spec.json
```
with four requirements.
## Forbidden
No planning.
No code generation.
---
# M3 — PLANNER + DAG ENGINE
## Goal
Transform:
```text
AppSpec
```
into:
```text
validated sequential Task DAG
```
Persist:
```text
tasks.json
```
## Files
```text
src/harness/agents/planner.py
src/harness/orchestration/dag.py
src/harness/orchestration/runner.py
tests/test_planner.py
tests/test_dag.py
```
---
## Planner rules
Enforce these in code.
Do NOT rely only on the LLM prompt.
### Ordering
1. DB/schema before dependent application logic.
2. Shared components before pages.
3. Auth infrastructure before authenticated routes.
### Coverage
Every:
```text
REQ
```
must map to at least one Task.
Every Task must contain:
```text
>=1 requirement
>=1 file
>=1 verification command
```
### IDs
Generate:
```text
TASK-001
TASK-002
...
```
Dependencies may reference only earlier tasks.
If LLM output violates ordering:
```text
repair deterministically
```
### Paths
Every `files_touched` path must be:
```text
relative
POSIX-style
inside workspace
without ..
```
---
## Fallback planner
If LLM planning fails, generate deterministic tasks roughly covering:
```text
project/schema setup
auth if required
core entity CRUD
API/pages
tests/verification
```
For small apps:
```text
5–12 tasks
```
---
## DagEngine
Required behavior:
```python
get_ready()
mark(task_id, status)
is_done()
blocked_propagation()
topological_order()
```
A task is READY iff:
```text
status == PENDING
AND
all dependencies == COMPLETED
```
If dependency becomes:
```text
FAILED
TIMED_OUT
```
dependent tasks become:
```text
BLOCKED
```
Cycles must raise an explicit error.
---
## PlanValidator
Detect:
```text
duplicate task IDs
unknown dependency
cycle
orphan requirement
task without verification
task without files
task without requirements
invalid paths
```
Overlapping files:
```text
WARNING only in V1
```
---
## Acceptance
Todo spec with four requirements:
```text
→ 5–8 tasks
```
Tests:
```text
all requirements covered
dependency ordering valid
cycle rejected
TASK-001 failure blocks dependent task
tasks.json persisted
plan_created checkpoint persisted
```
## Forbidden
No Builder execution.
No concurrency.
---
# M4 — BUILDER + SANDBOX-GATED FILE TOOLS
## Goal
Transform one Task into real files.
## Files
```text
src/harness/agents/builder.py
src/harness/tools/files.py
tests/test_builder.py
```
---
## Builder context
Provide ONLY:
```text
current Task
requirements referenced by Task
small spec summary
dependency verification results
allowed files
verification commands
```
Do NOT dump the full run history.
Example input:
```python
task
requirements
spec_summary = {
"app_type": ...,
"stack": ...,
"data_model": ...
}
dependency_results
allowed_files
verification
```
---
## Builder output
LLM must produce:
```json
{
"relative/path.py": "full file contents"
}
```
No prose.
No markdown fences.
---
## Write restrictions
Builder may write ONLY:
```text
task.files_touched
```
Any attempted write outside the allowlist:
```text
PermissionError
+
event log
+
task failure
+
CONFIG_ERROR
```
Builder may use only sandboxed:
```text
read_file
write_file
list_files
```
Builder may NOT execute subprocesses.
---
## Deterministic fallback
If LLM output cannot be parsed, use a deterministic implementation for the current task.
The fallback MUST obey the same `files_touched` allowlist.
The planner fallback therefore MUST ensure any deterministic scaffold files are explicitly listed in the relevant task.
Reference generated application may include:
```text
src/main.py
src/models.py
requirements.txt
tests/test_health.py
```
Do not create files outside the current Task's declared set.
---
## Acceptance
Test:
```text
allowed file write succeeds
outside-allowlist write fails
../ escape fails
MockProvider creates deterministic output
checkpoint created after task
```
After milestone:
```bash
pytest -q
```
must pass.
## Forbidden
No verification execution inside Builder.
No repair.
---
# M5 — REAL VERIFICATION + FAILURE CLASSIFICATION
## Goal
Execute real verification commands and persist structured evidence.
## Files
```text
src/harness/verification/verifier.py
src/harness/verification/classifier.py
tests/test_verifier.py
tests/test_classifier.py
```
---
## Verifier
Implement:
```python
verify_task(
task: Task,
workspace: Path
) -> list[VerificationResult]
```
For every verification command:
```text
shlex.split(command)
→ LocalSandbox.execute(...)
```
Default when missing:
```bash
pytest -q
```
Capture:
```text
exit_code
stdout[-4000:]
stderr[-4000:]
duration_ms
```
PASS:
```text
exit_code == 0
```
FAIL:
```text
exit_code != 0
```
Timeout:
```text
Task status = TIMED_OUT
FailureType = TIMEOUT
```
Static inspection alone can never count as verification.
---
## Workspace verification
Provide helper:
```python
verify_workspace(workspace)
```
It may include project-level checks appropriate to files actually present.
Do not blindly require external network installation as part of offline harness unit tests.
---
## Failure classifier precedence
Classification MUST be deterministic.
Use precedence:
```text
1. TIMEOUT
2. ENVIRONMENT_ERROR
3. DEPENDENCY_ERROR
4. TYPE_ERROR
5. CODE_ERROR
6. CONFIG_ERROR
7. TEST_FAILURE
8. UNKNOWN
```
The precedence matters.
For example, a pytest session containing a Python `SyntaxError` must classify as:
```text
CODE_ERROR
```
not merely `TEST_FAILURE`.
### TIMEOUT
Patterns:
```text
timed out
TimeoutExpired
duration >= configured timeout
```
### ENVIRONMENT_ERROR
Patterns:
```text
EAI_AGAIN
ENOTFOUND
registry unavailable
Network is unreachable
HTTP 503
Could not fetch
```
### DEPENDENCY_ERROR
Patterns:
```text
ModuleNotFoundError
ImportError
No module named
Could not resolve dependency
npm ERR 404
```
### TYPE_ERROR
Patterns:
```text
mypy
Pydantic ValidationError
TypeError ... expected
TS2322
Property ... does not exist
```
### CODE_ERROR
Patterns:
```text
SyntaxError
IndentationError
NameError
ReferenceError
```
### CONFIG_ERROR
Patterns:
```text
missing configuration
missing pyproject
requirements file not found
port already in use
invalid path configuration
```
### TEST_FAILURE
Patterns:
```text
AssertionError
FAILED
1 failed
FAIL tests/
```
Only after higher-priority categories have been excluded.
---
## Acceptance
Tests:
```text
SyntaxError → CODE_ERROR
ModuleNotFoundError → DEPENDENCY_ERROR
AssertionError → TEST_FAILURE
network unavailable → ENVIRONMENT_ERROR
timeout → TIMEOUT
working scaffold → PASS
```
Persist verification evidence.
## Forbidden
No automatic repair yet.
---
# M6 — REPAIR LOOP + LOOP DETECTION
## Goal
Implement:
```text
FAIL
→ classify
→ repair
→ verify
```
with bounded retries.
## Files
```text
src/harness/orchestration/repair.py
src/harness/agents/repair_agent.py
tests/test_repair.py
```
---
## Loop signature
```python
sha256(
command
+ exit_code
+ normalize(stderr[-2000:])
)
```
Normalization:
```text
lowercase
strip changing timestamps
strip volatile numeric values
normalize paths
collapse whitespace
```
Track:
```python
seen[signature] += 1
```
If the same normalized failure signature occurs three times:
```text
ESCALATE
FAILED
BLOCK dependents
```
Total repair attempts MUST NEVER exceed:
```text
3
```
---
## Repair strategy
### ENVIRONMENT_ERROR
```text
Do not rewrite application code.
Retry once.
If still failing → FAILED.
```
### DEPENDENCY_ERROR
Repair only dependency/config files that are already allowed by the Task or explicitly listed as verification hints.
### TYPE_ERROR
Patch relevant code only.
### CODE_ERROR
Patch relevant code only.
### TEST_FAILURE
Patch implementation or tests only when justified by requirement evidence.
Do not simply weaken tests to obtain PASS.
### CONFIG_ERROR
Patch configuration files only.
### TIMEOUT
Allow one bounded adjustment/retry.
Do not create an unbounded timeout.
---
## RepairAgent context
Provide:
```text
Task
relevant requirement slice
failing VerificationResult
FailureType
allowed files
current relevant file contents
```
Truncate large source context around:
```text
8000 chars per repair context
```
Output:
```json
{
"relative/path": "full corrected contents"
}
```
---
## Acceptance
Tests:
```text
repair SyntaxError → PASS within <=3 attempts
same failure 3 times → ESCALATE
dependents become BLOCKED
ENVIRONMENT_ERROR does not modify code
files outside allowlist cannot be repaired
```
---
# M7 — TRACEABILITY MATRIX
## Goal
Construct mechanical evidence:
```text
Requirement
→ Tasks
→ Files
→ Verification
→ Result
```
## Files
```text
src/harness/trace/matrix.py
tests/test_trace.py
```
---
## Matrix structure
For every requirement:
```json
{
"REQ-001": {
"tasks": [],
"files": [],
"tests": [],
"evidence": [],
"status": "PASS|FAIL",
"missing": []
}
}
```
Compute:
```text
tasks
= tasks referencing REQ
files
= union(task.files_touched)
tests
= union(task.verification)
evidence
= existing files
+ successful verification commands
```
PASS only if:
```text
>=1 expected implementation file exists
AND
>=1 relevant verification result PASS
```
No evidence:
```text
FAIL
```
Prose can never substitute for evidence.
---
## Acceptance
Todo reference run:
```text
4/4 requirements traced
```
Delete an implementation file:
```text
associated requirement becomes FAIL
```
---
# M8 — REQUIREMENT REVIEW
## Goal
Perform the final requirement-level audit.
## Files
```text
src/harness/agents/reviewer.py
tests/test_review.py
```
---
## ReviewAgent
Default authority:
```text
rule-based evidence
```
LLM review is optional and explanatory only.
An LLM MAY:
```text
add rationale
summarize evidence
identify concerns
```
An LLM MUST NOT:
```text
turn evidence-based FAIL into PASS
```
---
## Output
Persist:
```text
runs/<run_id>/review.json
```
Shape:
```json
{
"requirements": [
{
"id": "REQ-001",
"status": "PASS",
"evidence": [],
"missing": []
}
],
"overall_status": "PASS"
}
```
Overall PASS only if:
```text
every requirement == PASS
```
---
## Acceptance
Tests:
```text
complete evidence → PASS
missing implementation file → FAIL
missing successful verification → FAIL
LLM cannot override FAIL
review JSON validates
```
---
# M9 — CHECKPOINTING + CRASH RECOVERY
## Goal
Resume interrupted runs without repeating completed work.
## Files
```text
src/harness/state/checkpoints.py
src/harness/orchestration/pipeline.py
tests/test_recovery.py
```
---
## Required checkpoint events
```text
spec_created
plan_created
task_started
task_completed
verification_completed
repair_started
review_completed
```
Filename convention:
```text
checkpoints/<sequence>-<event>-<optional-task>.json
```
Example:
```text
001-spec_created.json
002-plan_created.json
003-task_started-TASK-001.json
004-task_completed-TASK-001.json
```
---
## Resume algorithm
```text
load state.json
↓
validate state
↓
load latest checkpoint state
↓
reconstruct task statuses
↓
keep COMPLETED
keep FAILED
keep BLOCKED
convert interrupted RUNNING → PENDING
convert READY → PENDING
↓
continue unfinished pipeline
```
Completed tasks MUST NOT execute again.
Use file hashes where appropriate to prove completed output was not rewritten during resume.
---
## Corruption handling
Corrupt:
```text
state.json
checkpoint JSON
```
must produce a clear explicit error.
Never silently restart the run from scratch.
---
## Acceptance
Simulate crash after:
```text
TASK-002
```
Resume must:
```text
finish remaining tasks
not rerun TASK-001
not alter completed file hashes
```
Test checkpoint sequencing.
---
# M10 — RUNTIME VERIFICATION
## Goal
Prove that the generated application actually starts and responds.
Static tests alone are insufficient.
## Files
```text
src/harness/verification/runtime.py
src/harness/verification/browser.py
tests/test_runtime.py
```
---
## RuntimeVerifier
### Entry detection
Recognize at minimum:
```text
src/main.py:app
app.py:app
package.json
```
No recognized entry:
```text
FAIL
CONFIG_ERROR
```
### Start
FastAPI:
```bash
python -m uvicorn src.main:app --port <free_port>
```
Node fallback:
```bash
npm run dev -- --port <free_port>
```
Use an OS-assigned/free local port.
Start process with:
```python
subprocess.Popen
```
with:
```text
cwd jailed inside workspace
shell=False
```
The verification layer may own process execution; application-building agents may not.
### Startup timeout
```text
15 seconds
```
### Checks
Verify:
```text
process remains alive
TCP port accepts connection
GET /health OR / returns 2xx
GET /docs or /api/health when available
SQLite DB can be opened when expected
```
### Cleanup
Always terminate spawned process.
Use `finally` cleanup.
Never leave orphan development servers.
### Result
Return structured:
```json
{
"status": "PASS",
"checks": [
{
"name": "health",
"ok": true,
"detail": "HTTP 200"
}
],
"evidence": []
}
```
Capture useful log snippets.
---
## Browser V1 stub
Implement only:
```python
def verify_acceptance(...):
raise NotImplementedError(
"Browser verification deferred post-V1"
)
```
Test that the browser verifier remains explicitly deferred.
Do NOT install Playwright or Selenium.
---
## Acceptance
Reference application:
```text
starts
port opens
health endpoint returns 200
runtime verifier PASS
```
Broken startup:
```text
FAIL
diagnostics captured
repair hint available
```
---
# M11 — CLI + BENCHMARKS
## Goal
Expose the entire harness through a deterministic command-line interface and provide a quick regression benchmark.
## Files
```text
src/harness/cli.py
src/harness/orchestration/pipeline.py
src/harness/benchmarks.py
tests/test_cli.py
tests/test_benchmarks.py
```
Add console entrypoint:
```toml
[project.scripts]
builder = "harness.cli:main"
```
Use:
```text
argparse only
```
No Click/Typer dependency.
---
## CLI commands
### New run
```bash
builder new "Build a Todo app"
```
Equivalent module form:
```bash
python -m harness.cli new "Build a Todo app"
```
It must execute the full pipeline.
### Resume
```bash
builder resume <run_id>
```
### Logs
```bash
builder logs <run_id>
```
Print or tail the run's structured event log in readable form.
### Benchmark
```bash
builder bench --quick
```
---
## Progress output
`builder new` must emit five user-facing high-level stages:
```text
[1/5] SPEC
[2/5] PLAN
[3/5] BUILD
[4/5] VERIFY
[5/5] REVIEW
```
Detailed internal milestones remain M1–M12; the five-stage CLI view is only presentation.
On success:
```text
BUILD COMPLETE
```
On failure:
```text
BUILD FAILED
```
and return non-zero exit status.
---
## Quick benchmark
`builder bench --quick` runs a deterministic small Todo scenario.
It must validate at minimum:
```text
spec generated
plan generated
files written
verification executed
runtime checked
review produced
required artifacts exist
```
Return:
```text
0 → PASS
non-zero → FAIL
```
---
## Acceptance
Must pass:
```bash
builder new "Build a Todo app"
builder bench --quick
```
Offline LLM fallback must still function.
Verify:
```text
all required artifacts exist
five-stage output matches expected format
events.log contains required keys
quick benchmark passes
```
## Forbidden
No Web UI.
No production Docker environment.
---
# M12 — FINAL GATE
## Goal
Prove V1 reliability before declaring completion.
All gates are mandatory.
---
## Gate 1 — Vertical Slice
Run:
```bash
builder new "Build a Simple Todo App with add/list/complete"
```
Expected pipeline:
```text
SPEC
→ >=3 REQs
→ PLAN
→ >=3 tasks
→ BUILD
→ real files
→ VERIFY
→ pytest PASS
→ RUNTIME
→ PASS
→ REVIEW
→ PASS
```
Save a stable demonstration run under:
```text
runs/demo_todo/
```
If any stage fails:
```text
fix the harness
rerun
do not proceed
```
---
## Gate 2 — Failure Injection
Inject:
```python
SyntaxError
```
into:
```text
workspace/src/main.py
```
The harness must:
```text
DETECT
→ FAIL
CLASSIFY
→ CODE_ERROR
LOCALIZE
→ TASK-ID
CAPTURE
→ diagnostics
REPAIR
→ relevant file only
REVERIFY
→ PASS
```
Then simulate identical failure repeatedly using a no-op repair implementation.
Expected:
```text
same signature x3
→ LOOP DETECTED
→ ESCALATE
→ task FAILED
→ dependents BLOCKED
```
---
## Gate 3 — V1 Capability Checklist
All MUST be ✓:
```text
[ ] valid spec generated
[ ] spec validated
[ ] valid DAG generated
[ ] sequential execution
[ ] real files created
[ ] real verification executed
[ ] failures localized
[ ] failures classified
[ ] failures repaired
[ ] repair bounded <=3
[ ] loop detection works
[ ] checkpoints written
[ ] crash resume works
[ ] completed tasks not rerun
[ ] traceability matrix generated
[ ] requirement review generated
[ ] runtime verified
[ ] final working project retained
```
---
## Gate 4 — Documentation
README must document:
```text
quickstart
builder new
builder resume
builder logs
builder bench --quick
architecture
evidence principle
failure handling
checkpoint recovery
```
Include an ASCII architecture diagram.
Create:
```text
examples/todo_run/
├── spec.json
├── tasks.json
├── verification.json
└── review.json
```
---
## Gate 5 — Full Test Suite
Run:
```bash
pytest -q
builder bench --quick
```
Both must pass.
---
## V1 Forbidden-Feature Audit
Assert the implementation does NOT contain functional implementations for:
```text
docker/
web_ui/
parallel workers
parallel DAG scheduler
Playwright
Selenium
PostgreSQL backend
web research
```
The browser verification stub is allowed.
---
# FINAL DEFINITION OF DONE
V1 is complete only when:
```text
M1 PASS
M2 PASS
M3 PASS
M4 PASS
M5 PASS
M6 PASS
M7 PASS
M8 PASS
M9 PASS
M10 PASS
M11 PASS
M12 PASS
```
Every bug fix MUST include a regression test.
Every new module MUST have tests.
Keep source files focused.
Prefer:
```text
<400 lines per file
```
Split larger files when practical.
Never store:
```text
API keys
secrets
absolute host-specific paths
```
Never delete historical checkpoints to hide failures.
---
# FINAL RUN SUMMARY FORMAT
At the end of every run, print a requirement-level summary.
Example:
```text
REQ-001
Implemented: YES
Tested: YES
Runtime: YES
Evidence:
- src/main.py
- pytest -q → exit_code 0
Status: PASS
REQ-002
Implemented: YES
Tested: NO
Runtime: NO
Missing:
- successful verification result
Status: FAIL
```
Final status:
```text
PASS
```
only when every requirement has evidence-backed PASS status.
Never use:
```text
"AI says finished"
```
as evidence.
---
# EXECUTION COMMAND
Start now.
Implement:
```text
M1
```
Run its tests.
If green, continue to:
```text
M2
```
Continue sequentially until M12.
Do not ask questions.
Do not stop at intermediate milestones.
Do not skip failed gates.
Do not declare V1 complete until the Final Gate is green.create a PNG image like a stamp saying sent to $(country). Image in red, retro style, horizontal stamp
A close-up showcases the exquisite packaging of the tumeric kojic soap, displaying is vibrant orange and yellow designs.
name = "hallucination-investigator" description = "Use when a task needs root-cause analysis for factuality failures, unsupported claims, or context breakdowns in AI outputs." model = "gpt-5.6-sol" model_reasoning_effort = "high" sandbox_mode = "read-only" developer_instructions = """ Own hallucination investigation as root-cause analysis across context, retrieval, prompts, tools, and workflow design. Working mode: 1. Reconstruct the failing example and the evidence the system actually had available. 2. Determine whether the failure came from missing context, bad retrieval, prompt framing, tool misuse, or unsupported inference. 3. Recommend the smallest change that most directly reduces recurrence. 4. Note how to verify the fix with targeted cases. Focus on: - whether the answer exceeded available evidence - retrieval misses, ranking issues, or stale context effects - prompt wording that encourages overconfident completion - output formats that hide uncertainty or source gaps - detection opportunities for unsupported claims before user delivery Quality checks: - verify the diagnosis uses the actual failing path, not generic speculation - separate no-evidence failures from evidence-ignored failures - recommend fixes that address the root cause rather than only suppressing wording - include at least one targeted regression case Return: - failure reconstruction and likely root cause - highest-leverage fix and why - supporting detection or guardrail ideas - targeted verification cases - residual risk if only the recommended fix is applied Do not label every wrong answer a hallucination when the true issue is poor retrieval, stale data, or tool failure unless explicitly requested by the parent agent. """ Own hallucination investigation as root-cause analysis across context, retrieval, prompts, tools, and workflow design. Working mode: 1. Reconstruct the failing example and the evidence the system actually had available. 2. Determine whether the failure came from missing context, bad retrieval, prompt framing, tool misuse, or unsupported inference. 3. Recommend the smallest change that most directly reduces recurrence. 4. Note how to verify the fix with targeted cases. Focus on: - whether the answer exceeded available evidence - retrieval misses, ranking issues, or stale context effects - prompt wording that encourages overconfident completion - output formats that hide uncertainty or source gaps - detection opportunities for unsupported claims before user delivery Quality checks: - verify the diagnosis uses the actual failing path, not generic speculation - separate no-evidence failures from evidence-ignored failures - recommend fixes that address the root cause rather than only suppressing wording - include at least one targeted regression case Return: - failure reconstruction and likely root cause - highest-leverage fix and why - supporting detection or guardrail ideas - targeted verification cases - residual risk if only the recommended fix is applied Do not label every wrong answer a hallucination when the true issue is poor retrieval, stale data, or tool failure unless explicitly requested by the parent agent.
This is a test prompt created to verify API authentication.
MASTER AI SOFTWARE DEVELOPMENT PROMPT
District Administration — Generic Campaign Management, Field Operations, Survey, Verification, Reporting & Payment Platform
Build a production-grade, full-stack, enterprise-level web application for District Administration that can be used to create and operate large-scale government field campaigns.
The platform must support campaigns involving:
• One department.
• Multiple departments.
• Joint inter-department teams.
• Senior officers.
• Supervisors.
• Field officers.
• Reserve/backup employees.
• Institutions.
• Villages.
• Wards.
• Mohallas.
• Households.
• Other configurable target entities.
The system must allow District Administration to create a campaign, divide it into multiple phases, create different forms for each phase, assign employees and teams to geographic areas and target entities, collect field data through mobile devices, verify submissions through a configurable hierarchy, request corrections, track final results, calculate authorized duty payments/honorarium according to configurable government guidelines, and generate dashboards and reports.
The system must be generic.
Do not hard-code the application for Census only.
Census 2027 should be implemented as an example campaign type.
Other campaign types must be possible without changing the core software.
Examples:
• Census 2027.
• School Inspection.
• Hospital Inspection.
• Road Survey.
• Flood Damage Survey.
• Village Survey.
• PDS Inspection.
• Anganwadi Inspection.
• Infrastructure Survey.
• Government Scheme Verification.
• Disaster Assessment.
• Public Grievance Field Verification.
• Any future district campaign.
________________________________________
1. CORE BUSINESS MODEL
The complete system should follow this structure:
District
→ Campaign
→ Campaign Phase
→ Geographic Scope
→ Departments
→ Workforce
→ Teams
→ Target Entities
→ Tasks
→ Dynamic Forms
→ Field Submission
→ Verification
→ Correction/Re-submission
→ Final Approval
→ Phase Result
→ Payment/Honorarium
→ Reports
Every part must be configurable.
________________________________________
2. MAIN OBJECTIVE
The application must solve the real-world problem of:
"A District Administration has thousands of employees from different departments and wants to conduct multiple field campaigns simultaneously across different geographic areas, with different teams, forms, workloads, verification processes, deadlines, payment rules and reporting requirements."
The platform should reduce manual Excel/WhatsApp/paper-based coordination.
It should provide one central command system for District Administration.
________________________________________
3. MULTIPLE CAMPAIGNS
District Administration must be able to run multiple campaigns simultaneously.
Example:
• Census 2027.
• School Inspection.
• Road Survey.
• Flood Assessment.
• Drinking Water Survey.
Each campaign is independent.
Each campaign can have:
• Different departments.
• Different employees.
• Different geographic areas.
• Different forms.
• Different workflow.
• Different deadlines.
• Different payment rules.
• Different target entities.
• Different reporting structure.
________________________________________
4. MULTI-PHASE CAMPAIGN
Every campaign can have multiple phases.
Example:
Census 2027
Phase 1
House Listing
Phase 2
Household Enumeration
Phase 3
Verification
Phase 4
Correction/Re-enumeration
Each phase must be able to have:
• Different dates.
• Different forms.
• Different workforce.
• Different teams.
• Different geographic assignments.
• Different instructions.
• Different workload.
• Different verification workflow.
• Different payment rules.
• Different results.
Do not assume that all phases use the same employees or form.
________________________________________
5. CAMPAIGN TYPES
Create configurable campaign types.
Examples:
• Census.
• Inspection.
• Survey.
• Verification.
• Enumeration.
• Monitoring.
• Assessment.
• Disaster response.
• Infrastructure survey.
• Custom.
Admin can create a new campaign type.
________________________________________
6. ORGANIZATIONAL HIERARCHY
The organization must be configurable.
Example:
District Admin
→ Department Head
→ Subdivision Officer
→ Tehsil Officer
→ Block Officer
→ Supervisor
→ Field Team
→ Field Officer
But another department may have a different structure.
Therefore do not hard-code hierarchy levels.
Use:
OrganizationNode
with configurable parent/child relationships.
________________________________________
7. GEOGRAPHIC HIERARCHY
Support flexible geographic hierarchy.
Example rural:
State
→ District
→ Subdivision
→ Tehsil
→ Block
→ Village
→ Mohalla/Hamlet
→ Household
Example urban:
State
→ District
→ Municipality/Nagar Palika
→ Zone
→ Ward
→ Mohalla
→ Household
The system must support both.
Do not assume every area has the same structure.
________________________________________
8. GEOGRAPHIC MASTER DATA
Create geographic master data management.
Admin can manage:
• District.
• Subdivision.
• Tehsil.
• Block.
• Municipality.
• Ward.
• Village.
• Mohalla.
• GPS coordinates.
• Boundary/polygon where available.
Support bulk import through:
• CSV.
• Excel.
• Government master-data API where available.
Validate duplicate geographic records.
________________________________________
9. TARGET ENTITY ENGINE
Do not make "household" the only target.
Create a generic target entity system.
Possible entities:
• Household.
• Person.
• School.
• Hospital.
• Road.
• Village.
• Shop.
• Anganwadi.
• Government building.
• Water source.
• Custom entity.
Example:
Campaign:
School Inspection
Target:
School
Campaign:
Census
Target:
Household
Campaign:
Road Survey
Target:
Road segment.
________________________________________
10. ENTITY MASTER RECORD
Every target entity should have a permanent master record.
Example:
Household ID:
HH-000001
School ID:
SCH-000001
Road ID:
ROAD-000001
The master entity can have multiple campaign/phase submissions.
This prevents duplication.
________________________________________
11. HOUSEHOLD MODEL
For Census-type campaigns support:
Household
→ Household members
→ Individual person records
The household can have:
• Household ID.
• Address.
• Geographic hierarchy.
• House number.
• GPS.
• Status.
• Source.
• Phase history.
The individual/person structure must support variable number of persons.
________________________________________
12. EMPLOYEE MASTER
Create a central employee database.
Fields:
• Employee ID.
• Name.
• Mobile number.
• Designation.
• Department.
• Office.
• Posting location.
• District.
• Subdivision.
• Tehsil.
• Block.
• Role.
• Employment status.
• Availability.
• Reserve status.
• Training status.
Employee ID should be unique.
Mobile number should be unique where applicable.
________________________________________
13. BULK EMPLOYEE IMPORT
Support import of thousands of employees.
Formats:
• Excel.
• CSV.
Before import:
• Validate.
• Detect duplicate Employee IDs.
• Detect duplicate mobile numbers.
• Detect missing fields.
• Detect invalid departments.
• Show row-level errors.
Allow:
Import Valid Records
without losing valid records because of invalid rows.
________________________________________
14. EMPLOYEE AVAILABILITY
Employee status:
• Available.
• Assigned.
• On Duty.
• On Leave.
• Unavailable.
• Reserve.
• Activated Reserve.
• Released.
• Suspended.
Campaign assignment must check availability.
Prevent incompatible double assignment.
________________________________________
15. MULTI-DEPARTMENT CAMPAIGNS
A campaign can include multiple departments.
Example:
School Inspection:
• Education.
• PWD.
• Food.
• Revenue.
Each department can have different responsibilities.
________________________________________
16. JOINT TEAMS
Create a team engine.
A team can contain employees from different departments.
Example:
Team 001:
• Revenue employee.
• Education employee.
• PWD employee.
Each team has:
• Team ID.
• Team leader.
• Members.
• Department.
• Geographic responsibility.
• Campaign.
• Phase.
• Status.
________________________________________
17. TEAM FORMATION
Allow:
Manual
Admin selects employees.
Automatic
System creates teams based on configured rules.
Rules may include:
• Team size.
• Department combination.
• Geographic area.
• Designation.
• Skill.
• Availability.
• Workload.
________________________________________
18. TEAM LEADER
Team leader can:
• See team members.
• See assigned tasks.
• Monitor progress.
• Review team-level work where authorized.
• Report employee absence.
• Request replacement.
• Submit team reports.
Do not automatically grant access to all data just because someone is team leader.
Permissions must still apply.
________________________________________
19. RESERVE EMPLOYEE SYSTEM
Every large campaign should support reserve employees.
Reserve employees can replace active staff when necessary.
Reasons:
• Leave.
• Illness.
• Transfer.
• Emergency.
• Administrative requirement.
• Other authorized reasons.
Workflow:
Active Employee
→ Unavailable
→ Supervisor reports
→ Authorized officer approves
→ Reserve employee selected
→ Reserve activated
→ Assignment transferred
→ Audit record created.
________________________________________
20. RESERVE TEAM
Support reserve teams in addition to reserve individuals.
Example:
Active Team:
Team 001
Reserve Team:
Team R001
If an entire team becomes unavailable, the reserve team can be activated.
________________________________________
21. WORKLOAD MANAGEMENT
Before launching a campaign phase, show:
Total target entities.
Required workforce.
Available workforce.
Reserve workforce.
Expected workload per employee.
Expected workload per team.
Example:
Targets:
1,000,000
Teams:
10,000
Average:
100 targets/team.
Allow authorized admins to adjust distribution.
________________________________________
22. WORKLOAD BALANCING
Support:
• Equal distribution.
• Geographic distribution.
• Random distribution.
• Manual distribution.
• Skill-based distribution.
• Workload balancing.
The system should detect overloaded employees/teams.
Example:
Employee A:
300 tasks
Employee B:
80 tasks
Show warning.
________________________________________
23. RANDOM ASSIGNMENT
Support random assignment where required.
Example:
10,000 schools
1,000 officers
System randomly assigns schools.
Prevent duplicate assignments.
Allow administrators to preview before activation.
Maintain assignment history.
________________________________________
24. GEOGRAPHIC ASSIGNMENT
Tasks can be assigned based on:
• District.
• Subdivision.
• Tehsil.
• Block.
• Village.
• Ward.
• Mohalla.
• GPS boundary.
Support polygon-based geographic assignment where feasible.
________________________________________
25. CAMPAIGN CREATION WIZARD
Create a professional multi-step wizard.
Step 1
Campaign details.
Step 2
Campaign type.
Step 3
Geographic scope.
Step 4
Departments.
Step 5
Workforce.
Step 6
Teams.
Step 7
Target entities.
Step 8
Form.
Step 9
Assignment.
Step 10
Verification workflow.
Step 11
Payment rules.
Step 12
Guidelines/documents.
Step 13
Preview.
Step 14
Launch.
________________________________________
26. CAMPAIGN VALIDATION BEFORE LAUNCH
Before launch check:
• No target entities.
• No teams.
• No officers.
• Missing form.
• Missing mandatory questions.
• Missing geographic assignment.
• Missing verification workflow.
• Missing payment configuration where required.
• Employee conflicts.
• Duplicate assignments.
• Insufficient workforce.
• Invalid dates.
Show warnings and errors.
Do not allow launch when critical requirements are missing.
________________________________________
27. DYNAMIC FORM BUILDER
District Admin must create forms without coding.
Question types:
• Short text.
• Long text.
• Integer.
• Decimal.
• Percentage.
• Currency/amount.
• Date.
• Date/time.
• Yes/No.
• Radio.
• Checkbox.
• Dropdown.
• Multi-select.
• Image.
• Multiple image.
• Video.
• File.
• GPS.
• Signature.
• Rating.
• Table.
• Repeating group.
• Calculated field.
________________________________________
28. FORM SECTIONS
Forms can contain sections.
Example:
School Inspection:
1. School Information.
2. Infrastructure.
3. PWD.
4. Food.
5. Education.
6. Final Remarks.
________________________________________
29. CONDITIONAL QUESTIONS
Support rules.
Example:
IF:
Building damaged = YES
THEN:
Show:
• Damage type.
• Damage severity.
• Damage photo.
• Repair estimate.
Otherwise hide these fields.
Create a visual condition builder.
________________________________________
30. REPEATING GROUPS
For Census:
Household:
Number of members = 6
Automatically create:
Person 1
Person 2
Person 3
Person 4
Person 5
Person 6.
Support nested repeating data where required.
________________________________________
31. FORM VALIDATION
Each question can have:
• Required.
• Minimum.
• Maximum.
• Length.
• Regex.
• Allowed options.
• Dependency.
• Evidence requirement.
• GPS requirement.
Validation must happen:
Frontend + Backend
Never trust frontend validation alone.
________________________________________
32. FORM VERSIONING
Forms must be versioned.
Example:
Form v1
Form v2
Historical submissions remain associated with their original form version.
Changing a form must not change old submissions.
________________________________________
33. CAMPAIGN FORM
Each phase can have its own form.
Example:
Campaign:
Census 2027
Phase 1:
Form A
Phase 2:
Form B
Phase 3:
Form C
________________________________________
34. FIELD OFFICER MOBILE APP
Build a mobile-first PWA.
Field employee dashboard:
Campaigns
→ Active Phase
→ Assigned Area
→ Assigned Tasks
→ Completed
→ Pending
→ Corrections
→ Drafts
→ Sync
→ Notifications
________________________________________
35. FIELD TASK
Each task contains:
• Task ID.
• Campaign.
• Phase.
• Target.
• Geographic location.
• Assigned team.
• Assigned employee.
• Deadline.
• Priority.
• Status.
• Instructions.
________________________________________
36. TASK STATUS
Use:
• Not Started.
• Assigned.
• Accepted.
• In Progress.
• Draft.
• Submitted.
• Under Verification.
• Correction Required.
• Resubmitted.
• Approved.
• Rejected.
• Reassigned.
• Overdue.
• Completed.
________________________________________
37. FIELD DATA COLLECTION
Field officer should be able to:
• Open task.
• Start task.
• Fill form.
• Save draft.
• Resume later.
• Capture GPS.
• Capture image.
• Upload video.
• Add remarks.
• Submit.
________________________________________
38. GPS
When configured:
Capture:
• Latitude.
• Longitude.
• Accuracy.
• Timestamp.
Optionally calculate distance from target location.
Configurable:
• Warning.
• Supervisor review.
• Block submission.
________________________________________
39. PHOTO
Support:
• Camera.
• Gallery.
• Multiple images.
• Compression.
• Preview.
• Retake.
Associate image with:
• Campaign.
• Phase.
• Task.
• Question.
• Employee.
________________________________________
40. VIDEO
Support:
• Record.
• Select.
• Preview.
• Upload.
• Progress.
• Retry.
• Maximum size.
• Maximum duration.
Use object storage.
Do not store large videos directly in PostgreSQL.
________________________________________
41. OFFLINE MODE
Mobile application must work with poor connectivity.
Use:
• PWA.
• IndexedDB.
• Local draft.
• Offline task list.
• Sync queue.
Status:
Online
Offline
Syncing
Synced
Failed.
________________________________________
42. OFFLINE CONFLICT MANAGEMENT
If the same record changes from multiple sources:
Do not silently overwrite.
Create conflict:
Conflict requires review.
Maintain version history.
________________________________________
43. SUBMISSION
Before final submission:
Show review page.
Example:
Required fields:
✓
GPS:
✓
Required images:
✓
Validation:
✓
Then:
Submit
Server returns:
Submission ID.
Never show successful submission before server confirmation.
________________________________________
44. VERIFICATION ENGINE
Create configurable workflow.
Example:
Field Officer
→ Supervisor
→ Tehsil Officer
→ Subdivision Officer
→ Department Head
→ District Admin
But administrators can configure different levels.
________________________________________
45. VERIFICATION ACTIONS
Reviewer can:
• Approve.
• Reject.
• Request correction.
• Add comment.
• Escalate.
• Reassign.
• View history.
________________________________________
46. CORRECTION WORKFLOW
Example:
Reviewer:
"Please upload a clear photograph."
Status:
Correction Required.
Employee receives notification.
Employee edits only permitted fields.
Resubmits.
Reviewer receives notification.
________________________________________
47. DATA VERSIONING
Every submission must preserve:
• Version.
• Answers.
• Files.
• GPS.
• User.
• Timestamp.
• Changes.
• Reason.
Never destroy historical versions.
________________________________________
48. FINAL APPROVAL
Only approved records should be included in final results when the campaign requires final approval.
Allow reports to distinguish:
• Preliminary.
• Submitted.
• Verified.
• Final Approved.
________________________________________
49. DATA QUALITY ENGINE
Create automatic quality checks.
Examples:
• Duplicate household.
• Duplicate task.
• Missing GPS.
• Impossible values.
• Inconsistent totals.
• Required evidence missing.
• Conflicting phase data.
• Unusual completion speed.
• Repeated identical GPS coordinates where suspicious.
• Excessive submissions in a short time.
Flag anomalies for review.
Do not automatically accuse an employee of misconduct.
Mark:
Data Quality Exception
________________________________________
50. EXCEPTION MANAGEMENT
Create a central Exception Center.
Examples:
• Missing household.
• Duplicate household.
• GPS issue.
• Incomplete form.
• Conflicting data.
• Overdue task.
• Employee unavailable.
• Team unavailable.
• Payment failure.
• Sync failure.
Admin can assign exceptions.
________________________________________
51. CENSUS 2027 EXAMPLE
Create Census 2027 as a sample campaign.
Do not invent official Census questions.
Use placeholder/configurable forms or officially supplied forms.
Example structure:
Census 2027
→ Phase 1: House Listing
→ Phase 2: Enumeration
→ Phase 3: Verification
→ Phase 4: Correction
Each phase has different forms and possibly different workforce.
________________________________________
52. CENSUS GEOGRAPHIC STRUCTURE
Support:
District
→ Subdivision
→ Tehsil
→ Municipality / Nagar Palika
→ Ward
→ Village
→ Mohalla
→ Household
The actual hierarchy must be configurable.
________________________________________
53. CENSUS JOINT TEAM
Example:
Team 001
Area:
Ward 10 / Mohalla A
Members:
• Education Department employee.
• Revenue Department employee.
• Municipal employee.
The team visits households within its assigned area.
________________________________________
54. HOUSEHOLD CENSUS FLOW
Team opens:
Household HH-000123
System displays:
• Location.
• Address.
• Previous phase information if authorized.
• Current phase form.
Team collects required information.
Submits.
Result goes through configured verification workflow.
________________________________________
55. CENSUS MULTI-PHASE WORKLOAD
Support distributing work over multiple phases to reduce employee workload.
Example:
Phase 1:
Employee Group A
Phase 2:
Employee Group B
Phase 3:
Employee Group C
Or the same employees can participate in multiple phases.
The system must support both.
________________________________________
56. PAYMENT/HONORARIUM ENGINE
Create a dedicated payment module.
IMPORTANT:
Never hard-code government payment amounts.
Payment amounts must come from authorized configurable rules based on the applicable government order/guideline.
________________________________________
57. PAYMENT RULE
Payment rule fields:
• Campaign.
• Phase.
• Role.
• Department if applicable.
• Duty type.
• Calculation method.
• Amount/rate.
• Effective date.
• Government order reference.
• Version.
• Approval status.
________________________________________
58. PAYMENT CALCULATION
Possible calculation models:
• Fixed amount.
• Per day.
• Per task.
• Per approved household.
• Role-based.
• Phase-based.
• Component-based.
Do not assume these are legally applicable.
The administrator configures the permitted calculation method according to official rules.
________________________________________
59. PAYMENT ELIGIBILITY
Example:
Assignment
↓
Duty completed
↓
Required work completed
↓
Submission accepted/approved
↓
Eligibility generated
↓
Payment approval
↓
Payment processing
↓
Paid
Exact rules must be configurable.
________________________________________
60. EMPLOYEE PAYMENT DASHBOARD
Employee sees:
Campaign:
Census 2027
Phase:
Phase 1
Duty:
Completed
Eligibility:
Eligible
Payment:
₹XXXX
Status:
Pending / Approved / Processing / Paid.
Sensitive financial information must be protected.
________________________________________
61. PAYMENT STATUS
Statuses:
• Not Eligible.
• Pending Eligibility.
• Eligible.
• Pending Approval.
• Approved.
• Processing.
• Paid.
• Failed.
• Returned.
• On Hold.
• Disputed.
________________________________________
62. PAYMENT REMINDERS
If payment is pending:
Send:
• In-app notification.
• SMS where configured.
• Email where configured.
Example:
Your approved campaign duty payment is still pending processing.
Do not make unverified claims about payment timing.
________________________________________
63. PAYMENT EXCEPTIONS
Support:
• Failed payments.
• Incorrect records.
• Missing approval.
• Duplicate payment prevention.
• Hold.
• Retry.
• Resolution.
Maintain complete audit trail.
________________________________________
64. PAYMENT DUPLICATE PREVENTION
Prevent duplicate payment for:
Employee + Campaign + Phase + Duty
unless explicitly allowed by an authorized adjustment process.
________________________________________
65. ATTENDANCE / DUTY PROOF
Where required by campaign rules, support duty attendance.
Possible methods:
• Start duty.
• End duty.
• GPS.
• Team leader confirmation.
• Supervisor approval.
• Task completion.
Do not assume attendance equals payment eligibility.
Make it configurable.
________________________________________
66. TRAINING
Track:
• Training assigned.
• Training completed.
• Training date.
• Training material.
• Assessment.
• Certification.
A campaign phase can optionally require training before assignment.
________________________________________
67. GUIDELINES / DOCUMENTS
Campaign administrators can upload:
• Government orders.
• Guidelines.
• SOPs.
• Training documents.
• Circulars.
• Forms.
• Payment orders.
Documents should be versioned.
________________________________________
68. NOTIFICATION ENGINE
Support:
• In-app.
• SMS.
• Email.
• Push notifications.
Notifications:
• New task.
• New campaign.
• Phase starting.
• Deadline.
• Overdue.
• Correction.
• Approval.
• Reassignment.
• Reserve activation.
• Payment update.
________________________________________
69. ESCALATION ENGINE
Create configurable escalation.
Example:
Task overdue by 2 days:
→ Supervisor notification.
Overdue by 4 days:
→ Tehsil Officer.
Overdue by 7 days:
→ District Admin.
The escalation schedule must be configurable.
________________________________________
70. REMINDER ENGINE
Support scheduled reminders.
Examples:
7 days before deadline.
3 days before.
1 day before.
Due date.
Overdue.
Payment pending for X days.
________________________________________
71. DISTRICT COMMAND DASHBOARD
Create a professional command center.
Show:
Campaigns
Total.
Active.
Completed.
Delayed.
Workforce
Total.
Assigned.
Available.
Reserve.
Unavailable.
Tasks
Total.
Completed.
Pending.
Overdue.
Correction.
Verification
Submitted.
Under review.
Approved.
Rejected.
Payment
Eligible.
Approved.
Paid.
Pending.
Failed.
________________________________________
72. GEOGRAPHIC DRILL-DOWN
Dashboard:
District
↓
Subdivision
↓
Tehsil
↓
Village/Ward
↓
Mohalla
↓
Household
At every level:
• Total.
• Assigned.
• Completed.
• Pending.
• Verified.
• Exceptions.
________________________________________
73. MAP
Provide interactive map.
Show:
• Assigned areas.
• Completed targets.
• Pending targets.
• Exceptions.
• GPS submissions where authorized.
Use clustering for large datasets.
Do not attempt to render millions of points simultaneously.
________________________________________
74. DEPARTMENT DASHBOARD
Department Head sees:
• Campaign participation.
• Employees.
• Teams.
• Workload.
• Completion.
• Verification.
• Exceptions.
• Payment.
Only authorized department data.
________________________________________
75. SUBDIVISION / TEHSIL DASHBOARD
Show local progress.
Example:
Tehsil A:
Targets: 100,000
Completed: 92,000
Pending: 8,000
Completion:
92%
________________________________________
76. TEAM DASHBOARD
Team Leader sees:
• Members.
• Assigned targets.
• Completed.
• Pending.
• Corrections.
• Overdue.
• Sync status.
________________________________________
77. EMPLOYEE DASHBOARD
Employee sees:
• My campaigns.
• My phases.
• My tasks.
• My progress.
• Corrections.
• Notifications.
• Payment.
• Guidelines.
________________________________________
78. REPORTING ENGINE
Create configurable reports.
Reports:
• Campaign.
• Phase.
• Department.
• Employee.
• Team.
• Geography.
• Target entity.
• Verification.
• Exception.
• Payment.
• Workforce.
• Productivity.
________________________________________
79. REPORT FILTERS
Filters:
• Campaign.
• Phase.
• Date.
• Department.
• Employee.
• Team.
• District.
• Subdivision.
• Tehsil.
• Village.
• Ward.
• Mohalla.
• Status.
________________________________________
80. EXPORT
Support:
• Excel.
• CSV.
• PDF.
Large reports must be generated asynchronously.
________________________________________
81. REPORT SCHEDULING
Allow authorized users to schedule reports.
Example:
Every day at 6 PM:
District Campaign Progress Report
Send to authorized users.
________________________________________
82. DATA ACCESS CONTROL
Use:
RBAC + Geographic Scope + Department Scope + Campaign Scope
Example:
Field Officer:
Only assigned tasks.
Tehsil Officer:
Authorized tehsil.
Department Head:
Authorized department.
District Admin:
District-wide.
Never rely only on frontend restrictions.
________________________________________
83. SUPER ADMIN
Super Admin manages:
• Districts.
• Departments.
• Users.
• Roles.
• Permissions.
• System settings.
• Master data.
• Integrations.
________________________________________
84. DISTRICT ADMIN
District Admin can:
• Create campaigns.
• Create phases.
• Create forms.
• Select departments.
• Manage workforce.
• Create teams.
• Assign areas.
• Assign tasks.
• Configure verification.
• Configure payment rules where authorized.
• Monitor progress.
• Approve/review data.
• Generate reports.
________________________________________
85. DEPARTMENT HEAD
Can:
• View department campaigns.
• Manage department workforce.
• Review submissions.
• Verify data.
• Monitor department performance.
• Generate authorized reports.
________________________________________
86. FIELD OFFICER
Can:
• Login.
• View assigned duties.
• Collect field data.
• Capture GPS.
• Capture media.
• Save drafts.
• Submit.
• Correct.
• Resubmit.
• View payment status.
• View guidelines.
________________________________________
87. PAYMENT OFFICER
Can:
• Review eligibility.
• Approve payment.
• Process payment.
• View failures.
• Retry authorized payments.
• Generate payment reports.
Do not give payment officers unnecessary household-data access.
________________________________________
88. SYSTEM SECURITY
Implement:
• Password hashing.
• Secure authentication.
• OTP.
• Session security.
• JWT or secure session architecture.
• Refresh token rotation where applicable.
• Rate limiting.
• API authorization.
• Input validation.
• File validation.
• Secure headers.
• CORS.
• CSRF protection where applicable.
• SQL injection protection.
• XSS protection.
• Audit logs.
________________________________________
89. DATA PRIVACY
Collect only officially required information.
Sensitive information should have:
• Strict access control.
• Encryption where appropriate.
• Audit logs.
• Retention rules.
• Export restrictions.
Do not display sensitive household/person information on public dashboards.
________________________________________
90. AUDIT LOG
Record every important operation.
Examples:
• Login.
• OTP.
• Campaign creation.
• Phase creation.
• Form changes.
• Employee assignment.
• Team creation.
• Household assignment.
• Submission.
• Correction.
• Approval.
• Reassignment.
• Reserve activation.
• Payment calculation.
• Payment approval.
• Payment processing.
• Export.
Audit log should be immutable for normal users.
________________________________________
91. SYSTEM LOGGING
Use structured application logs.
Separate:
• Application logs.
• Security logs.
• Audit logs.
• Error logs.
Do not expose internal errors to users.
________________________________________
92. DATABASE
Use:
PostgreSQL
ORM:
Prisma
Design a normalized schema.
Important entities:
• State.
• District.
• Department.
• OrganizationNode.
• GeographicNode.
• User.
• Employee.
• Role.
• Permission.
• Campaign.
• CampaignPhase.
• CampaignDepartment.
• CampaignGeography.
• TargetEntity.
• Household.
• Person.
• Team.
• TeamMember.
• ReserveEmployee.
• Task.
• TaskAssignment.
• Form.
• FormVersion.
• FormSection.
• FormQuestion.
• FormOption.
• FormCondition.
• Submission.
• SubmissionVersion.
• SubmissionAnswer.
• SubmissionFile.
• GPSRecord.
• Verification.
• CorrectionRequest.
• Exception.
• PaymentRule.
• PaymentRuleVersion.
• EmployeePayment.
• PaymentComponent.
• PaymentTransaction.
• Notification.
• GuidelineDocument.
• Training.
• AuditLog.
• Report.
• ReportJob.
Use appropriate:
• Foreign keys.
• Unique constraints.
• Indexes.
• Soft deletion where appropriate.
• Timestamps.
________________________________________
93. DATA RETENTION
Build configurable retention policies.
Different records may have different retention requirements.
Do not automatically delete official records without an authorized retention policy.
________________________________________
94. BACKUP
Design:
• Automated database backups.
• Point-in-time recovery where supported.
• Object storage backup.
• Backup monitoring.
• Restore testing.
________________________________________
95. DISASTER RECOVERY
Document:
• Recovery procedure.
• Backup restoration.
• Database recovery.
• File recovery.
• Queue recovery.
• Disaster scenarios.
________________________________________
96. SCALABILITY
Design for:
• 50,000+ employees.
• Millions of targets.
• Millions of submissions.
• Large file uploads.
• Thousands of concurrent mobile users.
Use:
• Pagination.
• Cursor pagination where appropriate.
• Database indexes.
• Redis.
• Queue workers.
• Object storage.
• Background jobs.
• Horizontal scaling.
• Database connection pooling.
________________________________________
97. BACKGROUND JOB SYSTEM
Use:
Redis + BullMQ or equivalent.
Jobs:
• Report generation.
• Excel export.
• PDF generation.
• Notifications.
• SMS.
• Email.
• Image processing.
• Video processing.
• Payment processing integration.
• Reminder jobs.
• Escalation.
• Data aggregation.
________________________________________
98. FILE STORAGE
Use:
• S3-compatible storage.
Store only metadata in PostgreSQL.
Metadata:
• File ID.
• Name.
• Type.
• Size.
• Storage path.
• Upload user.
• Campaign.
• Phase.
• Task.
• Question.
• Timestamp.
Use signed URLs for private files.
________________________________________
99. MOBILE PERFORMANCE
Optimize for:
• Low-end Android devices.
• Slow networks.
• Limited storage.
• Intermittent connectivity.
Avoid unnecessary animations.
Keep forms lightweight.
Compress images.
Use resumable uploads where practical.
________________________________________
100. ACCESSIBILITY
Support:
• High contrast.
• Large touch targets.
• Keyboard navigation.
• Screen readers.
• Proper labels.
• Accessible validation messages.
________________________________________
101. USER INTERFACE
Admin desktop:
Sidebar
→ Dashboard
→ Campaigns
→ Phases
→ Workforce
→ Teams
→ Geography
→ Targets
→ Forms
→ Tasks
→ Verification
→ Payments
→ Reports
→ Maps
→ Notifications
→ Audit Logs
→ Settings
Field mobile:
Home
My Campaigns
My Tasks
Corrections
Notifications
Payment
Guidelines
Profile
________________________________________
102. DESIGN STYLE
Use a professional government administration design.
Primary:
Navy/blue.
Secondary:
White/gray.
Use:
• Tables.
• Cards.
• Status badges.
• Charts.
• Maps.
• Progress bars.
Avoid excessive decorative UI.
Prioritize usability.
________________________________________
103. BULK OPERATIONS
Admin must be able to:
• Import employees.
• Import geography.
• Import target entities.
• Assign teams in bulk.
• Reassign tasks in bulk.
• Activate reserve employees in bulk.
• Generate reports in bulk.
Always show confirmation before large operations.
________________________________________
104. BULK OPERATION SAFETY
For every bulk action:
1. Preview.
2. Validate.
3. Show number of affected records.
4. Confirm.
5. Execute.
6. Show result.
7. Provide error report.
8. Record audit event.
________________________________________
105. SEARCH
Global search across authorized data:
• Campaign.
• Employee.
• Team.
• Household.
• Institution.
• Task.
• Submission.
• Geographic area.
________________________________________
106. DUPLICATE DETECTION
Detect:
• Duplicate employee.
• Duplicate target.
• Duplicate household.
• Duplicate task.
• Duplicate assignment.
• Duplicate payment.
Do not automatically delete records.
Flag them for review.
________________________________________
107. NOTIFICATION PREFERENCES
Allow users to configure permitted notification preferences.
However, mandatory government alerts cannot be disabled if configured as mandatory.
________________________________________
108. API ARCHITECTURE
Use a clean REST API or equivalent.
Organize APIs by module:
/auth
/users
/employees
/departments
/geography
/campaigns
/phases
/teams
/targets
/tasks
/forms
/submissions
/verifications
/exceptions
/payments
/notifications
/reports
/audit
/files
________________________________________
109. API DOCUMENTATION
Generate:
OpenAPI / Swagger
Document:
• Authentication.
• Request.
• Response.
• Errors.
• Permissions.
________________________________________
110. ERROR HANDLING
Use consistent API responses.
Example:
{
"success": false,
"error": {
"code": "VALIDATION_ERROR",
"message": "Required fields are missing."
}
}
Never expose:
• Database errors.
• Stack traces.
• Secrets.
• Internal infrastructure details.
________________________________________
111. FRONTEND STATE
Use an appropriate state-management/data-fetching strategy.
Use:
• Server-side fetching where appropriate.
• Query caching.
• Optimistic updates only when safe.
• Offline state.
________________________________________
112. OFFLINE DATA SECURITY
Do not store unnecessary sensitive personal data permanently on the device.
Encrypt/local-protect sensitive offline storage where practical.
Provide device/session expiration.
________________________________________
113. SESSION SECURITY
Support:
• Session timeout.
• Device/session management.
• Logout all devices where authorized.
• Token revocation.
• Suspicious login detection.
________________________________________
114. OTP SECURITY
OTP:
• Expiration.
• Rate limiting.
• Attempt limit.
• Resend cooldown.
• Secure storage.
• Audit logging.
Never store OTP in plain text longer than necessary.
________________________________________
115. LOGIN
Support:
Mobile Number
→ OTP for initial verification
→ Password creation
Then:
Mobile Number
→ Password
→ Dashboard
Forgot password:
Mobile
→ OTP
→ New Password
________________________________________
116. GOVERNMENT INTEGRATIONS
Build integration interfaces rather than hard-code external systems.
Possible future integrations:
• SMS gateway.
• Government employee master database.
• Official GIS.
• Payment/treasury system.
• Identity/authentication system.
• Email.
• Notification gateway.
Use adapters/interfaces so providers can be changed.
________________________________________
117. NO FAKE INTEGRATIONS
If a real government API is not available:
Use a clearly marked development/mock adapter.
Do not pretend that a fake integration is real.
________________________________________
118. PAYMENT INTEGRATION
Payment processing should initially support:
Manual/administrative status
and be architected for future integration with an authorized government payment/treasury system.
Do not invent a government payment API.
________________________________________
119. IMPORT/EXPORT SECURITY
Exports must respect authorization.
Do not allow field officers to export all district data.
Sensitive exports should require additional authorization where appropriate.
Log every export.
________________________________________
120. DATA QUALITY DASHBOARD
Show:
• Missing data.
• Invalid data.
• Duplicate data.
• GPS exceptions.
• Correction rates.
• Rejection rates.
• Unusual activity.
________________________________________
121. PRODUCTIVITY METRICS
Show:
• Tasks completed per day.
• Average task duration.
• Completion rate.
• Correction rate.
• Approval rate.
• Overdue rate.
Do not use productivity metrics as automatic disciplinary conclusions.
________________________________________
122. CAMPAIGN TEMPLATES
Allow administrators to save:
Campaign Template
Example:
School Inspection Template.
When creating a new campaign:
Use Template
Then modify:
• Form.
• Departments.
• Geography.
• Workforce.
• Dates.
• Payment.
________________________________________
123. FORM TEMPLATE LIBRARY
Allow reusable forms.
Examples:
• Inspection form.
• Survey form.
• Household form.
• Verification form.
Forms must remain versioned.
________________________________________
124. WORKFLOW TEMPLATE
Allow reusable workflows.
Example:
Field Officer
→ Supervisor
→ Department Head
→ District Admin.
Save as:
Standard Inspection Workflow.
________________________________________
125. PAYMENT TEMPLATE
Allow authorized users to reuse payment structures.
Example:
Field Duty Payment Template.
But each campaign/phase must reference the exact applicable rule/version.
________________________________________
126. CAMPAIGN PAUSE
Admin can pause a campaign.
When paused:
• No new tasks can start.
• Existing drafts remain safe.
• Admin can resume later.
Clearly display:
Campaign Paused.
________________________________________
127. CAMPAIGN EXTENSION
Authorized admin can extend deadline.
Require:
• New date.
• Reason.
• Approval if configured.
Record audit history.
________________________________________
128. TASK REASSIGNMENT
Allow:
Employee A
→ Employee B
Reason:
Employee unavailable.
Maintain full history.
________________________________________
129. TEAM CHANGE
Allow adding/removing team members during campaign with authorization.
Do not rewrite old historical assignments.
________________________________________
130. TARGET REASSIGNMENT
If a household/institution is reassigned:
Maintain:
Old Team
→ New Team
Reason
Date
Authorized By
________________________________________
131. FINALIZATION
When a phase is finalized:
• Prevent normal editing.
• Allow authorized correction workflow only.
• Lock official result.
• Preserve historical versions.
________________________________________
132. CAMPAIGN CLOSURE
When campaign is completed:
• Lock operational changes.
• Finalize reports.
• Finalize payment eligibility.
• Preserve audit logs.
• Archive according to retention policy.
________________________________________
133. REAL-LIFE CENSUS 2027 DEMO
Create development demo:
Campaign:
Census 2027
District:
Demo District
Departments:
• Revenue.
• Education.
• Municipal Administration.
• Panchayat.
Create sample:
• 2 subdivisions.
• 4 tehsils.
• 3 municipalities.
• 10 villages.
• 20 wards.
• 50 mohallas.
• 500 households.
• 100 employees.
• 20 teams.
• 5 reserve teams.
Create:
Phase 1:
House Listing
Phase 2:
Enumeration
Phase 3:
Verification
Use sample placeholder questions.
Clearly label:
DEMO DATA — NOT OFFICIAL CENSUS DATA
________________________________________
134. END-TO-END DEMO
Demonstrate:
District Admin
→ Creates Census campaign
→ Creates Phase 1
→ Selects departments
→ Imports employees
→ Creates joint teams
→ Assigns geography
→ Loads target households
→ Creates Phase 1 form
→ Configures verification
→ Configures payment rule
→ Launches phase
Field Team
→ Logs in
→ Views households
→ Opens household
→ Completes form
→ Captures GPS
→ Saves draft
→ Submits
Reviewer
→ Reviews
→ Requests correction
Field Team
→ Corrects
→ Resubmits
Reviewer
→ Approves
System
→ Generates final result
→ Generates payment eligibility
→ Shows payment status
District Admin
→ Views dashboard
→ Drills down to household
→ Views map
→ Generates Excel/PDF
→ Views audit history.
________________________________________
135. TESTING
Create:
Unit Tests
For:
• Assignment.
• Permissions.
• Form validation.
• GPS.
• Payment calculation.
• Workflow.
Integration Tests
For:
• Authentication.
• Campaign.
• Teams.
• Forms.
• Submission.
• Verification.
• Payment.
E2E Tests
Test complete campaign lifecycle.
________________________________________
136. LOAD TESTING
Test realistic loads.
At minimum test:
• 50,000 employees.
• Large task volumes.
• Large submission volumes.
• Concurrent mobile users.
• Large report generation.
• Large file uploads.
Identify bottlenecks.
________________________________________
137. DATABASE INDEXING
Create indexes for frequently queried:
• Employee ID.
• Mobile.
• Campaign ID.
• Phase ID.
• Team ID.
• Geographic ID.
• Target ID.
• Task status.
• Submission status.
• Payment status.
• Created date.
Review query plans for large tables.
________________________________________
138. ARCHITECTURE
Preferred stack:
Frontend
Next.js
React
TypeScript
Tailwind CSS
shadcn/ui
PWA
IndexedDB
Backend
NestJS
TypeScript
Database
PostgreSQL
Prisma
Cache / Queue
Redis
BullMQ
Storage
S3-compatible storage
Maps
Provider abstraction supporting:
OpenStreetMap / Mapbox / Google Maps
________________________________________
139. PROJECT STRUCTURE
Use a clean modular architecture.
Example:
/apps
/web
/api
/packages
/ui
/types
/config
/validation
/infrastructure
/docs
/tests
Keep business logic separate from UI.
________________________________________
140. ENVIRONMENT VARIABLES
Create:
.env.example
Include:
DATABASE_URL
REDIS_URL
JWT_SECRET
S3_ENDPOINT
S3_ACCESS_KEY
S3_SECRET_KEY
S3_BUCKET
SMS_PROVIDER
SMS_API_KEY
SMS_SENDER_ID
EMAIL_PROVIDER
EMAIL_API_KEY
MAP_PROVIDER
MAP_API_KEY
Never commit real secrets.
________________________________________
141. DOCKER
Provide:
Dockerfile
docker-compose.yml
Services:
• Web.
• API.
• PostgreSQL.
• Redis.
• Object storage for local development.
________________________________________
142. DEVELOPMENT README
Provide complete instructions:
1. Install dependencies.
2. Configure .env.
3. Start PostgreSQL.
4. Start Redis.
5. Run migrations.
6. Seed database.
7. Start backend.
8. Start frontend.
9. Login using demo credentials.
10. Run tests.
________________________________________
143. SEED DATA
Create realistic but clearly fake development data.
Include:
• Admin.
• Department Heads.
• Supervisors.
• Field Officers.
• Reserve employees.
• Departments.
• Geographic hierarchy.
• Teams.
• Households.
• Campaigns.
• Phases.
• Forms.
• Tasks.
Never use real personal information.
________________________________________
144. DEMO CREDENTIALS
Provide development-only demo accounts.
Example:
District Admin
Department Head
Supervisor
Field Officer
Payment Officer
Clearly mark:
DEVELOPMENT ONLY
Do not use these credentials in production.
________________________________________
145. IMPORTANT GOVERNMENT DATA RULE
Do not invent:
• Official Census questions.
• Government payment amounts.
• Government orders.
• Government employee records.
• Official geographic datasets.
• Government APIs.
• Official Census procedures.
Where official information is unavailable, create:
CONFIGURABLE PLACEHOLDERS
and clearly label them.
________________________________________
146. IMPORTANT DESIGN RULE
Do not create separate applications for:
• Census.
• School inspection.
• Road survey.
• Flood survey.
Instead create one:
District Campaign Platform
Then:
Campaign Type:
Census
Campaign:
Census 2027
Phase:
House Listing
This makes the platform reusable.
________________________________________
147. MOST IMPORTANT ARCHITECTURAL MODULES
The application must be built around these engines:
1. Campaign Engine
Campaigns and phases.
2. Organization Engine
Departments and hierarchy.
3. Geography Engine
District/tehsil/village/ward/mohalla.
4. Workforce Engine
Employees and availability.
5. Team Engine
Joint teams and reserve teams.
6. Target Entity Engine
Households, schools, roads, etc.
7. Assignment Engine
Assign targets to teams/employees.
8. Form Engine
Dynamic forms and versions.
9. Field Data Engine
Mobile data collection.
10. Offline Sync Engine
Mobile offline operation.
11. Verification Engine
Review and approval.
12. Exception Engine
Data quality and operational exceptions.
13. Payment Engine
Configurable government-guideline-based payment.
14. Notification Engine
SMS/email/push/in-app.
15. Reporting Engine
Dashboards and reports.
16. Audit Engine
Complete history.
________________________________________
148. DEVELOPMENT ORDER
Do not attempt to create the entire application as disconnected pages.
Build in this order:
Phase A — Foundation
• Architecture.
• Database.
• Authentication.
• RBAC.
• Organization.
• Geography.
Phase B — Campaign
• Campaign.
• Phase.
• Department.
• Workforce.
• Team.
Phase C — Target & Assignment
• Target entities.
• Household.
• Geographic assignment.
• Task engine.
• Workload.
Phase D — Forms
• Form builder.
• Dynamic questions.
• Conditions.
• Repeating groups.
• Validation.
• Versioning.
Phase E — Mobile
• Field officer UI.
• Offline.
• GPS.
• Image.
• Video.
• Sync.
Phase F — Workflow
• Verification.
• Correction.
• Resubmission.
• Approval.
• Exceptions.
Phase G — Payment
• Payment rules.
• Eligibility.
• Approval.
• Status.
• Reminders.
• Exceptions.
Phase H — Intelligence
• Dashboards.
• Maps.
• Analytics.
• Reports.
• Exports.
Phase I — Production
• Security.
• Testing.
• Load testing.
• Monitoring.
• Backups.
• Disaster recovery.
• Docker.
• Documentation.
________________________________________
149. FINAL ACCEPTANCE CRITERIA
The project is considered complete only when a real end-to-end flow works:
District Admin
→ creates campaign
→ creates multiple phases
→ selects departments
→ imports workforce
→ creates joint teams
→ creates reserve teams
→ assigns geography
→ loads target entities
→ creates phase-specific dynamic form
→ creates verification workflow
→ configures authorized payment rule
→ launches campaign
↓
Field Team
→ logs in on mobile
→ receives assigned area
→ sees assigned targets
→ visits target
→ fills form
→ captures GPS where required
→ captures media where required
→ works offline if necessary
→ synchronizes
→ submits
↓
Reviewer
→ receives submission
→ verifies
→ requests correction
↓
Field Team
→ corrects
→ resubmits
↓
Reviewer
→ approves
↓
System
→ finalizes result
→ calculates authorized payment eligibility
→ tracks payment
→ sends notifications
↓
District Admin
→ sees real-time progress
→ drills down geographically
→ views maps
→ views department/team/employee performance
→ views exceptions
→ views payment status
→ generates reports
→ exports authorized data
→ reviews audit history.
________________________________________
150. FINAL INSTRUCTION TO THE AI DEVELOPER
Do not build a toy project.
Do not build only frontend screens.
Do not use hard-coded arrays for core business data.
Do not make fake buttons.
Do not create disconnected demo pages.
Build a real full-stack system with:
Frontend
↕
Backend API
↕
Business Logic
↕
PostgreSQL
↕
Object Storage
↕
Redis/Background Jobs
↕
Notification Services
Every important button must perform a real operation.
Every important record must be persisted.
Every permission must be enforced on the backend.
Every official submission must be versioned.
Every important change must be audited.
Every large operation must be designed for scale.
Every mobile workflow must consider poor connectivity.
Every payment amount must be configurable and linked to an authorized rule/reference.
Every campaign must be configurable.
Every phase must be independently configurable.
Every form must be dynamically configurable.
Every organization/geographic hierarchy must be configurable.
The final product should be a reusable District Administration Digital Campaign & Field Operations Platform, with Census 2027 as one realistic implementation, rather than a Census-only application.
One thing I would strongly recommend
For a project of this scale, don't ask the AI to generate the entire application in one shot. Give it the master prompt above, then make it work module-by-module, starting with the database and architecture.
The most important foundation is:
Organization → Geography → Workforce → Campaign → Phase → Team → Target → Task → Form → Submission → Verification → Result → Payment
If that data model is correct, the rest of the application can evolve without needing to rebuild it laterI want to change the attire of this image to something with good clothes and should change the background to some famous places
I want you to act the the best brave thinker in the world while looking for solution to each world current problem on earth.make it easy to assimilate with the best solution in a way to make money online procedure to take how to reach out to people which app will the people that need the solution be how to approach them even if there is need to create appp teach me am ready to learn
give me a marketing video for spare parts slaes company
create me a proper documentation of this whole website that i have created in such a way that if s new person comes at my place so he/she can be able to understand what is happeing here and be able to use it efficiently
I want you to act like the best AI video editor in the world while am working on campus life add a water map write up that says @campus life let the dialogue be very funny
i want to create the best strategy which will take only winning trades and as many as possible in a day in propfirm challenges using ctrader which i will connect using mcp server of ctrader backtest it with the data available in the ctrader and create a strategy in c#
---
name: crypto-whitepaper-writer
description: Write a credible, well-structured whitepaper for any crypto/blockchain project (L1 chain, DeFi protocol, oracle network, stablecoin, token system) — grounded in the structural and rhetorical patterns of Bitcoin, Ethereum, Uniswap, Chainlink, and MakerDAO, not generic ICO-hype templates. Use whenever asked to draft, outline, or review a crypto/Web3 whitepaper.
---
# Crypto Whitepaper Writer
## Why this skill exists
Most AI-generated crypto whitepapers read like marketing decks: vague "revolutionary" language,
unbacked security claims, a roadmap standing in for proof, tokenomics as decoration. The five
most-cited whitepapers in the industry — Bitcoin, Ethereum, Uniswap, Chainlink, MakerDAO — share a
different discipline instead: problem-first framing, incremental mechanism-building, named threat
models, and per-actor incentive analysis. This skill encodes that discipline so the output reads
like engineering, not a pitch.
See `reference.md` for the full research base: archetype skeletons, craft principles, anti-patterns,
and the pre-publish checklist. Read it before drafting.
## Step 1 — Gather inputs before writing a word
Do not start drafting until you have, from the user:
- **The problem** this project solves, in one sentence, with a concrete scenario of what fails
without it.
- **The project archetype** — the closest match in `reference.md` → Archetypes: base-layer
consensus/settlement, DeFi mechanism (AMM/lending/derivatives), oracle/coordination network,
stablecoin/collateral system, or application-layer protocol/token system.
- **The core mechanism** — what actually happens on-chain/off-chain, in enough detail to describe a
state transition or formula, not just a feature list.
- **Token existence and role** — if a token exists, what specific mechanical function does it serve
(fee payment, collateral, staking/slashing, governance weight)? If the project doesn't need a
token, say so explicitly rather than inventing tokenomics to look complete.
- **Known limitations / open risks** the team is willing to state honestly.
If any of these is missing or vague, ask before drafting — never fabricate a problem statement, team
background, adoption numbers, or token model to fill a gap.
## Step 2 — Pick the section skeleton, calibrate length
Match the archetype to its skeleton in `reference.md`. Don't force every whitepaper through an
identical template — Bitcoin has no tokenomics section; MakerDAO has no proof-of-work security proof.
Use the shared spine (problem → mechanism → actor/incentive analysis → threat/risk model →
architecture → [governance/token, only if applicable] → limitations → conclusion), and cut whatever
doesn't apply to this project.
Length follows mechanism complexity, not a fixed page count — Bitcoin proves its point in 9 pages;
MakerDAO needs far more because it coordinates more actors and failure modes. Let each section run as
long as the idea needs and no longer (anti-pattern: forced symmetry). If the project has a token or
makes any financial/return-implying claim, include a short disclaimer (Uniswap's precedent) stating
the document is not investment advice.
## Step 3 — Draft section by section, applying the craft principles
Work through `reference.md`'s craft principles as each section gets written. The ones that matter
most in practice:
- Open with a failure scenario, not a mission statement.
- Build the mechanism from a minimal model upward — don't state the final design cold.
- Every security or performance claim gets a number, threshold, or named proof — never an adjective
alone ("highly secure" is not a claim).
- Every incentive-driven actor (miner, LP, oracle node, keeper, validator) gets its own paragraph
naming exactly what they earn and what punishes defection.
- Name specific attack vectors and pair each with its specific countermeasure.
- Include at least one worked, traceable example — a transaction walkthrough, a numeric calculation,
a step-by-step lifecycle.
- If this design revises or extends a known predecessor, write it as an explicit delta (what changed
and why), not a from-scratch re-explanation.
## Step 4 — Run the anti-pattern filter before showing a draft
Scan against `reference.md`'s anti-pattern list. Any hit is a reason to rewrite the line, not soften
it: price predictions or market-cap claims, unverifiable team/advisor credentials used as an
argument, roadmap phases presented as proof of viability, a tokenomics table with no stated
mechanical purpose, novelty claimed without naming what came before, a security claim with no threat
model behind it, a missing limitations section, buzzwords substituting for a mechanism description.
## Step 5 — Self-check and disclose gaps
Before finalizing:
- Confirm every numeric or technical claim traces to something the user provided or a cited external
source — flag anything you inferred rather than were told.
- Confirm a limitations/risks section exists and isn't hollow.
- Run `reference.md`'s pre-publish checklist and state the result (pass/fail per item, or a
self-rating) to the user before calling the draft done — don't ship a whitepaper without surfacing
what's unresolved.
## Output format
Markdown, sections matching the chosen archetype skeleton, an abstract at the top, references/sources
at the bottom if external material was used. No emoji, no bolded label scaffolding (`Problem:`,
`Solution:`), no forced symmetry between sections — a simple idea gets a short section, a hard one
gets a long one, exactly as the five reference papers do.
Cite every external claim (a cryptographic primitive, a competitor's known limitation, a market fact)
inline with a bracketed marker (e.g. `[1]`) resolving to a numbered reference list at the end — never
assert an outside fact with no traceable source. See `reference.md` → Calibration examples for what a
compliant vs. non-compliant sentence looks like before you draft the real thing.# Technical Whitepaper Writer
## Why this skill exists
Most AI-generated whitepapers read like marketing documents: vague claims, excessive adjectives, feature lists presented as innovation, unsupported performance numbers, generic architecture diagrams, and roadmaps used as substitutes for technical evidence.
A strong whitepaper instead explains **why a problem exists, what the proposed system does, how it works, why the design is structured that way, what assumptions it makes, how it behaves under normal and failure conditions, and where the design remains limited.**
The goal is a document that reads like **engineering and technical research**, not a sales brochure.
The whitepaper should let a technically capable reader answer:
1. What problem is being solved?
2. Why do existing approaches fail or become insufficient?
3. What is being proposed?
4. How does the proposed system actually work?
5. What are the major components, and how do they interact?
6. What assumptions does the design make?
7. What happens during normal operation?
8. What happens when something goes wrong?
9. What evidence supports the technical claims?
10. What are the limitations and unresolved risks?
11. How is this different from existing approaches?
12. What would someone need to implement, evaluate, or deploy it?
Writing should prioritize **clarity, technical precision, traceability, and intellectual honesty** over impressive-sounding language.
---
## Step 1 — Gather inputs before writing
Do not begin drafting the full whitepaper until the core information is available. If critical information is missing, ask for it (see Step 20) rather than inventing it.
**1. The problem.** State it in one clear sentence, then describe a concrete scenario showing what fails without the proposed solution.
- Avoid: *"The industry needs a revolutionary new approach."*
- Prefer: *"Current systems require each application to independently integrate multiple model providers, resulting in duplicated integration logic, inconsistent observability, and difficult provider switching."*
**2. The project type.** Identify the primary category (and any important secondary categories) without forcing the project into an inappropriate one. Examples: AI/ML system, LLM application, AI infrastructure, data platform, developer tool, cloud/distributed system, cybersecurity system, networking system, database/storage system, hardware/embedded system, robotics system, scientific/research system, enterprise architecture, SaaS platform, API/middleware, agentic system, FinTech, healthcare tech, industrial or energy tech, protocol/standards system, or other.
**3. The core mechanism.** Describe what actually happens inside the system — inputs, processing, state, transformations, decisions, outputs, feedback loops, external dependencies, failure paths, system boundaries. Not a feature list.
- Avoid: *"The platform provides intelligent routing, security, observability, and scalability."*
- Prefer: *"An incoming request is classified according to model, latency, cost, and policy requirements. The routing layer selects an eligible provider, executes the request, records telemetry, and applies retry or fallback logic when the selected provider fails."*
**4. System boundaries.** What's inside the system vs. external? Which components are controlled vs. dependencies? Where does data enter and leave? Where does trust begin and end?
**5. Actors and stakeholders.** Only include actors relevant to the system (e.g., end users, developers, administrators, operators, services, models, agents, data/infrastructure providers, validators, attackers, external systems). For each important actor: what they do, need, control, can observe, and what incentives or constraints shape their behavior.
**6. Resources, economics, or tokens — only when applicable.** If the system has a token, credits, usage units, subscriptions, fees, incentives, rewards, penalties, compute allocation, or quotas, explain their *mechanical* purpose. Never introduce tokenomics or financial mechanisms just because a whitepaper is "expected" to have them. Omit this section if no economic mechanism exists.
**7. Known limitations and risks.** What might fail, degrade, or remain unresolved — scalability limits, latency constraints, dependency risks, model limitations, data quality issues, security assumptions, operational complexity, cost constraints, hardware limitations, privacy concerns, regulatory uncertainty, availability dependencies, integration complexity. State these explicitly; do not hide them.
---
## Step 2 — Choose the whitepaper structure
Do not force every project into an identical template. The default technical spine:
1. Abstract
2. Introduction
3. Problem and Motivation
4. Existing Approaches
5. Design Goals and Non-Goals
6. Proposed Architecture
7. Core Mechanism
8. System Workflow
9. Technical Design
10. Security / Safety / Reliability Model
11. Performance and Scalability
12. Implementation Considerations
13. Worked Example
14. Evaluation / Evidence
15. Limitations and Open Problems
16. Future Work
17. Conclusion
18. References
Not every section is mandatory — use only what materially improves understanding:
- A simple software architecture may skip heavy mathematical analysis.
- An AI research system may need experiments and evaluation methodology.
- A cybersecurity system may need a detailed threat model.
- A hardware system may need physical constraints and benchmarking.
- A distributed system may need consistency, fault tolerance, and failure analysis.
- A commercial SaaS platform may need deployment/operational architecture rather than formal proofs.
---
## Step 3 — Establish the technical delta
If the project builds on or extends existing technology, explicitly trace:
> What existed before → What limitation remained → What this design changes → Why that change matters.
- Avoid: *"This is the world's first revolutionary architecture."*
- Prefer: *"Existing approach A provides X but requires Y. Approach B removes Y but introduces Z. The proposed architecture combines X with a different execution model that removes Y while accepting an explicit trade-off in Z."*
---
## Step 4 — Build the mechanism from a minimal model
Introduce complexity progressively rather than presenting the full architecture at once:
1. **Intuition** — the idea in simple language.
2. **Minimal model** — the smallest system that could solve the problem.
3. **Architecture** — the major components.
4. **Data / request flow** — how information moves through the system.
5. **Technical mechanisms** — algorithms, protocols, models, APIs, state transitions, policies.
6. **Failure behavior** — what happens when components fail or assumptions break.
7. **Optimization** — performance, scalability, caching, batching, routing, parallelism.
---
## Step 5 — Apply evidence discipline
Claims like *faster, cheaper, more secure, scalable, reliable, accurate, lower latency, higher throughput, reduced hallucination, improved efficiency* must never be asserted without support.
Where possible, provide: benchmark results, measurements, formulas, thresholds, experimental results, architectural reasoning, citations, assumptions, or comparison methodology.
- Avoid: *"The architecture provides extremely low latency."*
- Prefer: *"In the evaluated configuration, the routing layer adds a median of X ms of processing overhead under Y workload."*
If a number is unavailable, say so. **Never fabricate measurements.**
---
## Step 6 — Analyze each important actor
For each actor: responsibility, inputs, outputs, permissions, dependencies, incentives, constraints, failure modes, and consequences of incorrect behavior. Example set (adapt to the actual project):
- **User** — submits a request and receives a response.
- **Application** — authenticates the request and invokes the platform API.
- **Model Provider** — processes the inference request.
- **Gateway** — applies routing, policy, retry, and observability logic.
- **Operator** — configures policies and monitors system health.
---
## Step 7 — Define the threat, failure, or risk model
Depending on the project, analyze relevant risks: malicious users, compromised components, unauthorized access, data leakage, model manipulation, prompt injection, supply-chain attacks, denial of service, corrupted data, incorrect outputs, infrastructure/dependency failure, network partitions, hardware failure, operator error, configuration errors, adversarial inputs, economic attacks, privacy violations.
For every significant threat:
> Threat → Attack/Failure Mechanism → Impact → Mitigation → Remaining Risk
- Avoid: *"The system is highly secure."*
- Prefer: explaining secure **against what**, **under which assumptions**, and **with what controls**.
---
## Step 8 — Include a worked example
Every substantive whitepaper needs at least one concrete, end-to-end example — a transaction lifecycle, API request, inference request, data pipeline, user workflow, state transition, attack scenario, failure scenario, or numerical calculation. Include real numbers where useful.
Example shape:
> 1. Client submits request.
> 2. Gateway validates policy.
> 3. Router selects provider.
> 4. Provider executes inference.
> 5. Response passes through validation.
> 6. Telemetry is recorded.
> 7. Client receives response.
---
## Step 9 — Explain architecture clearly
Architecture descriptions should answer: What are the major components? What does each do? How are they connected? What protocols/interfaces link them? Where is state stored? Where does computation happen? Where are decisions made? Where are the security boundaries? Where can failures occur?
Use layered structure only where it reflects reality, e.g.:
```text
User / Client Layer
↓
API / Interface Layer
↓
Application / Orchestration Layer
↓
Core Processing Layer
↓
Data / Model / Storage Layer
↓
Infrastructure Layer
```
Do not add layers for visual symmetry alone.
---
## Step 10 — Handle mathematics appropriately
Use equations when they clarify the mechanism: optimization objectives, probability models, scoring functions, cost/latency/throughput calculations, capacity planning, cryptographic formulas, ML objectives, resource allocation, economic models, reliability calculations. Always explain each equation in plain language. Never add math purely for appearance.
---
## Step 11 — Handle AI/ML systems appropriately
Distinguish clearly between model architecture, training, fine-tuning, inference, retrieval, orchestration, evaluation, safety, monitoring, data pipelines, and human-in-the-loop processes.
Frame the pipeline explicitly:
> Input → Processing → Model / Retrieval / Tool Use → Validation → Output
Where relevant, cover: model selection, training methodology, dataset assumptions, context management, retrieval strategy, evaluation methodology, hallucination mitigation, guardrails, latency, inference cost, observability, and model failure modes.
Avoid vague terms like "intelligent," "cognitive," or "human-like" unless technically defined.
---
## Step 12 — Compare against existing approaches
Where relevant, compare on concrete dimensions:
| Dimension | Existing Approach | Proposed Approach |
|---|---|---|
| Architecture | ... | ... |
| Latency | ... | ... |
| Scalability | ... | ... |
| Cost | ... | ... |
| Security | ... | ... |
| Flexibility | ... | ... |
| Operational Complexity | ... | ... |
Every row needs a defensible basis — don't build the table just to look complete.
---
## Step 13 — Discuss trade-offs
Every meaningful architecture has trade-offs. Discuss the relevant ones explicitly: performance vs. cost, flexibility vs. complexity, security vs. usability, consistency vs. availability, latency vs. accuracy, centralization vs. decentralization, automation vs. human control, compute vs. memory, precision vs. recall, privacy vs. observability.
Never claim the design eliminates trade-offs — explain **which were chosen, and why**.
---
## Step 14 — Separate current capability from future work
Do not present roadmap items as evidence the system currently works. Distinguish:
- **Current design** — what exists or is technically specified today.
- **Experimental / validated** — what has been implemented and tested.
- **Proposed extensions** — what could be built later.
- **Open research problems** — what remains unresolved.
A roadmap is not proof of technical viability.
---
## Step 15 — Anti-pattern filter
Before presenting a draft, scan for and rewrite:
**Marketing language** — revolutionary, groundbreaking, game-changing, next-generation, world-class, unprecedented, highly intelligent, infinitely scalable, military-grade, enterprise-grade — unless technically defined and supported.
**Unsupported claims** — "10x faster," "99.99% reliable," "100% secure," "zero hallucinations," "fully autonomous," "unlimited scalability" — unless evidence exists.
**Feature dumping** — a list of features is not an architecture.
**Buzzword substitution** — "AI + blockchain + cloud + quantum + autonomous agents" is not a mechanism.
**Roadmap-as-proof** — future plans don't demonstrate present viability.
**Tokenomics without purpose** — don't invent economic mechanisms.
**Novelty without comparison** — don't claim innovation without explaining what came before.
**Security without threat modeling** — don't claim security without naming threats and mitigations.
**Architecture without data flow** — components alone don't explain a system.
**Missing limitations** — every serious design has them; state them.
---
## Step 16 — External research and citations
When using external information: cite every external technical claim, prefer primary and authoritative sources, cite research papers for scientific claims, official documentation for technical specs, standards bodies for standards, and vendor docs for vendor-specific behavior.
Use inline numbered citations:
> Transformer architectures use self-attention to model relationships between tokens [1].
```markdown
## References
[1] Vaswani et al., "Attention Is All You Need," 2017.
```
**Never fabricate references. Never cite a source that doesn't actually support the statement.**
---
## Step 17 — Writing style
Write as an experienced engineer or researcher explaining a complex system to another technically capable person.
**Prefer:** precise language, short-to-medium paragraphs, clear explanations, explicit assumptions, concrete examples, technical depth where useful, structured-text diagrams where appropriate, meaningful section titles.
**Avoid:** excessive adjectives, startup-style hype, repetitive conclusions, generic mission statements, unnecessary jargon, artificial complexity, fake certainty.
The tone: *"Here is the problem. Here is why existing approaches struggle. Here is the mechanism we propose. Here is how it works. Here is the evidence. Here is where it can fail."*
Not: *"We are revolutionizing the future of technology."*
---
## Step 18 — Output structure
Produce the whitepaper in Markdown, adapting section numbers to the actual project (omit irrelevant sections):
```markdown
# Title
## Abstract
## 1. Introduction
## 2. Problem and Motivation
## 3. Existing Approaches
## 4. Design Goals and Non-Goals
## 5. Proposed Architecture
## 6. Core Mechanism
## 7. System Workflow
## 8. Technical Design
## 9. Security, Safety, and Reliability
## 10. Performance and Scalability
## 11. Worked Example
## 12. Evaluation
## 13. Limitations and Open Problems
## 14. Future Work
## 15. Conclusion
## References
```
**Length should follow technical complexity, not an arbitrary page count.** A simple system gets a concise paper; a complex one gets deeper treatment.
---
## Step 19 — Pre-publish self-check
Before declaring the whitepaper complete, verify each item:
| Check | Status |
|---|---|
| Problem is concrete | PASS / FAIL |
| Failure scenario is explained | PASS / FAIL |
| Project type is correctly identified | PASS / FAIL |
| Core mechanism is clearly explained | PASS / FAIL |
| System boundaries are defined | PASS / FAIL |
| Architecture is understandable | PASS / FAIL |
| Data / request flow is explained | PASS / FAIL |
| Existing approaches are discussed | PASS / FAIL |
| Technical delta is clear | PASS / FAIL |
| Important actors are analyzed | PASS / FAIL |
| Threat / failure model exists | PASS / FAIL |
| Major claims have evidence | PASS / FAIL |
| At least one worked example exists | PASS / FAIL |
| Trade-offs are acknowledged | PASS / FAIL |
| Limitations are explicitly stated | PASS / FAIL |
| Future work is separated from current capability | PASS / FAIL |
| External claims are cited | PASS / FAIL |
| No fabricated numbers or references | PASS / FAIL |
| No marketing hype substitutes for technical explanation | PASS / FAIL |
Report a short **Whitepaper Quality Check** summarizing these results. If important information is missing, say so explicitly rather than inventing it.
---
## Step 20 — Missing information policy
If critical information is missing, ask for it before drafting that portion. **Never fabricate:** technical specifications, benchmark results, customer numbers, adoption statistics, revenue, market size, team credentials, partnerships, security guarantees, performance measurements, token economics, implementation details, or research results.
When something is unknown, mark it clearly:
> **Not specified** / **Requires validation** / **Assumption:** ...
Never silently convert an assumption into a stated fact.
---
## Core principle
The whitepaper should answer one question above all others:
> **Can a technically capable reader understand what this system does, how it works, why it was designed this way, what evidence supports it, and where it can fail?**
If yes, the whitepaper is doing its job.Scenic alpine village on the edge of a serene turquoise lake, (charming European architecture:1.2) with terracotta-tiled roofs, classic Swiss-style buildings, a prominent clock tower spire reaching towards the sky, lush green trees lining the water's edge, majestic snow-capped mountains (Alps:1.3) towering in the background under a clear blue sky with fluffy white clouds, a small wooden motorboat (detailed textures:1.1) navigating the gentle ripples in the foreground, bright natural daylight, crisp atmosphere, (vivid colors:1.2), photorealistic, high-resolution photography, travel magazine aesthetic, wide-angle lens, sharp focus, serene summer day, detailed landscape, depth of field, cinematic lighting.
(Portrait of a beautiful young woman:1.3), (Middle Eastern ethnicity:1.2), (age 20:1.1), (intricate facial features:1.3), (soft natural expression:1.2), wearing a (vibrant red headscarf:1.2) wrapped around wavy dark hair, dressed in a (detailed blue floral blouse:1.2) over a (yellow textured top:1.1), adorned with (ornate turquoise beaded necklace:1.2), (large vintage drop earrings:1.1), and gold bangles. The subject is positioned slightly to the left, (facing the viewer:1.2), resting her arms on a surface. Background features a (distressed turquoise wall:1.2), a (large rustic ceramic vase:1.1) containing yellow wildflowers, and a small painted bowl. (Fine art oil painting style:1.3), rich color palette of teal, gold, and crimson, (soft cinematic lighting:1.2), painterly textures, elegant composition, high detail, masterpiece, 8k resolution, volumetric atmosphere, sophisticated classic portraiture style.
You are a senior software architect and pharmacy management systems specialist. Design and build a private pharmacy CRM for my pharmacy in Mosul, Iraq. The system is for managing patients, chronic medications, follow-ups, sales insights, inventory, and customer relationships. Main goal Create a simple, fast, private CRM that helps me remember patients, understand their medication history, follow up with chronic patients, identify sales opportunities, and improve pharmacy service without encouraging unsafe or unnecessary medication use. Users The system will initially have one administrator user. The pharmacist must control access to patient information. Patient data must not be publicly accessible. Core patient profile Each patient should have: - Unique patient ID - QR code - Full name - Age or date of birth - Sex - Phone number - Address or area - Notes - Date added - Last visit - Next follow-up date - Patient status Medication profile For each patient store: - Medication name - Active ingredient - Strength - Dosage form - Dose - Frequency - Duration - Start date - End date - Prescriber - Reason for use - Current or discontinued status - Notes Medication history must remain available so I can see previous medications. Chronic medication management Allow me to mark patients as chronic-care patients. For chronic patients show: - Active medications - Previous medications - Expected refill date - Last purchase date - Days since last purchase - Follow-up date - Missed refill - Pharmacist notes The system should help identify patients who may need follow-up. Do not automatically recommend changing treatment or stopping medication. Dashboard Create a dashboard showing: - Total patients - Active chronic patients - Patients due for follow-up - Missed follow-ups - Patients due for medication refill - New patients - Returning patients - Today's follow-ups - Recent purchases - Sales - Profit - Low-stock products - Products approaching expiry CRM features Allow me to: - Search patients by name - Search by phone number - Search by patient ID - Scan a QR code - Open the patient profile quickly - Add a visit - Add medication - Edit medication - Record a purchase - Record pharmacist notes - Set a follow-up date - Mark a follow-up as completed - View patient history QR system Every patient should have a unique QR code. Scanning the QR code should open the patient's profile inside the authenticated CRM. The QR code must not expose sensitive patient information directly. Inventory integration If pharmacy inventory data is available, connect the CRM to it. Show: - Product - Category - Stock - Purchase cost - Selling price - Profit - Profit margin - Daily consumption - Estimated days until stockout - Expiry date Marketing and CRM analytics Create useful customer segments such as: - Chronic patients - Frequent customers - Inactive customers - Patients due for refill - Patients due for follow-up - High-value customers - OTC customers - Supplement customers Use these segments to suggest ethical pharmacy actions. Examples: - Reminder to refill a chronic medication - Follow-up reminder - Blood pressure monitoring service - Medication adherence follow-up - Relevant OTC product suggestion when clinically appropriate - Personal-care recommendation based on customer needs Never recommend unnecessary medication or supplements simply to increase sales. Sales analytics Track: - Daily sales - Weekly sales - Monthly sales - Gross profit - Profit margin - Number of transactions - Average transaction value - Sales by category - Sales by product - OTC sales - Supplement sales - Chronic medication sales Show trends and identify changes in customer behavior. Alerts Create alerts for: - Follow-up due - Missed follow-up - Expected refill - Missed refill - Low stock - Near expiry - Expired product - Unusual sales changes Privacy and security Patient information is sensitive. Use: - Authentication - Secure local storage or encrypted database - Role-based access if multiple users are added later - Automatic session timeout - Database backup - Restore function - Audit log for important changes The system should work locally whenever possible. Avoid sending patient information to external AI services unless I explicitly enable it. Interface Design the interface for a pharmacist working quickly during busy hours. Prioritize: - Fast search - Few clicks - Large buttons - Clear patient timeline - Simple forms - Mobile-friendly interface - Arabic and English support - Iraqi pharmacy terminology where appropriate Main screens Create: 1. Dashboard 2. Patients 3. Patient profile 4. Medication history 5. Visits 6. Follow-ups 7. Inventory 8. Sales analytics 9. Alerts 10. Reports 11. Settings 12. Backup and restore Patient timeline Every patient should have a chronological timeline containing: - Registration - Visits - Medication additions - Medication changes - Purchases - Follow-ups - Notes Analytics The CRM should generate actionable insights rather than only displaying numbers. For example: "23 chronic patients are expected to refill within 7 days." "11 patients have not returned within their expected refill period." "OTC sales increased 14% this month." "Category X has high sales but low profit margin." "17 products may expire before expected stock depletion." Explain why each insight matters and what action I should consider. AI assistant Include an optional AI assistant that can answer questions about CRM data. Examples: - Which chronic patients are due for refill this week? - Which patients have missed their expected refill? - What are my top 20 profitable products? - Which categories have high sales but low margins? - Which products are at risk of expiry? - Which days have the highest sales? - What changed compared with last month? - Which patients need follow-up today? The AI must distinguish between: - Facts directly available in the database - Calculations - Predictions - Suggestions Never invent patient information or sales data. Architecture Recommend a production-ready architecture that is simple enough for a small pharmacy. Prefer a local-first architecture. Explain: - Frontend - Backend - Database - Authentication - QR generation - Backup system - API structure - AI integration - Deployment - Security Design the database schema before implementation. Include relationships between: - Patients - Medications - Visits - Purchases - Products - Follow-ups - Users - Alerts - Audit logs Important constraints The system must remain simple. Do not add features just because they sound impressive. Every feature should answer one of these questions: - Does it save pharmacist time? - Does it improve patient follow-up? - Does it reduce stock problems? - Does it improve business visibility? - Does it improve patient service? - Does it protect patient data? Before writing implementation code: 1. Define the complete requirements. 2. Identify missing requirements. 3. Design the database. 4. Design the user workflow. 5. Design the API. 6. Define the security model. 7. Define the MVP. 8. Then propose the implementation plan. Build the MVP first. Do not overengineer the system.
Give me note book to learn Spanish with Myanmar translation
Maktaba shamela and turath app etc cross checked 5 times verification Master research prompt 100/100 rating regarding (on Genspark Deep AI research agent research) 73 sects and 72 will be in fire who are the 72 sects in fire what scholars say that these are the 72 sects etc
Analyze the current website's design system by reviewing its key pages: homepage, a product or pricing page, an interior content page, a form or contact page, and any page with unique UI patterns (testimonials, pricing tables, etc.). Where possible, inspect actual computed CSS values (via element inspection) rather than estimating visually, so colors, sizes, and spacing are accurate rather than approximate. Document the following: - Color palette: primary, secondary, accent, and neutral colors with hex/rgb values and where each is used - Typography: font families, weights, sizes, and line-heights for H1-H6, body text, and captions/labels - Spacing and layout: spacing scale, container widths, grid structure, and responsive breakpoints - Buttons and CTAs: primary/secondary/tertiary button styles, including hover and active states if visible - Forms and inputs: field styling, borders, focus states - Navigation: header/nav structure and styling, footer structure - Cards and containers: border-radius, shadows, borders - Iconography and imagery style Flag any inconsistencies across pages (e.g., different button styles in different places) instead of picking one and ignoring the rest. Output the result as a single markdown (.md) file with H2 headers for each category, tables for color palettes and typography scales, and code blocks for CSS values. Structure it so a developer or designer could use it directly. Save it as [site-name]-design-system.md so I can export it from this thread.
---
name: herdr-multiagent
description: "Playbook for running several coding agents in parallel under Herdr: stage decomposition, one git worktree + isolated env + pane per agent, file-based briefs, state monitoring, review and merge. Agent-agnostic: the child kind comes from `herdr pane current` (.result.pane.agent) and matches the orchestrator (omp, opencode, claude, codex, kimi, ...). Use for multi-agent parallel work in separate worktrees. Requires HERDR_ENV=1."
---
# Multi-agent work through Herdr
Playbook: split the project's remaining work into independent stages, put each stage
on its own agent in its own git worktree and Herdr pane, hand it a file-based brief,
monitor it, and accept the result.
This skill is agent-agnostic: the kind of the children equals the kind of the
orchestrator. Launched from opencode, the children are opencode; from omp, they are
omp; from claude, they are claude. Never hardcode the orchestrator's kind from
memory and never pick a "popular" kind.
## 0. Preconditions
```bash
test "${HERDR_ENV:-}" = 1 # if this fails, stop - we are not inside Herdr
```
If the check fails, tell the user the session is not running under Herdr and stop.
Do not drive someone else's Herdr from outside it.
The basic pane/agent commands live in Herdr's own skill (`herdr --skill`).
The installed binary is the authority on syntax; when unsure read
`herdr agent`, `herdr pane`, `herdr integration` instead of guessing.
## 1. Determine your own kind - before anything else
```bash
herdr pane current --current
```
The `.result.pane.agent` field IS the orchestrator's kind, and the same value goes
to `--kind` for the children:
```bash
KIND=$(herdr pane current --current | jq -r '.result.pane.agent')
# without jq:
KIND=$(herdr pane current --current | sed -E 's/.*"agent":"([^"]+)".*/\1/' | head -1)
echo "$KIND"
```
Empty or `unknown` - ask the user which kind to start the children with.
Below, `$KIND` always means this resolved value, never a literal.
Check the Herdr integration for this kind (it provides `agent list/wait/prompt`):
```bash
herdr integration status | grep -i "$KIND"
```
- `current` - good.
- `not installed` - run `herdr integration install "$KIND"`. Only **new** sessions
pick the integration up, so install it BEFORE starting children; the orchestrator
itself stays invisible to `agent list`, which is fine - it needs no monitoring.
- The kind is absent from `herdr integration install` (e.g. `amp`, `cline`, `kiro`,
`maki`) - there will be no structural monitoring, use the §7 fallback
(`pane read` + git). Not a blocker.
State it explicitly to the user: "children kind = $KIND".
## 2. Decomposition - the main step, do not rush
- Read the project plan/spec and the current state (`git log`, tests,
`git worktree list`).
- Split the remaining work into stages with **non-overlapping file areas**.
Two agents on one package - only deliberately and with an explicit order
(afterwards, not in parallel).
- Additive edits to shared files (config, lock) are acceptable - record in the briefs
"additive only, no signature changes"; the orchestrator resolves merge conflicts.
- Write down the matrix "stage -> files it MAY / MUST NOT touch".
Before launch: everything finished in main is committed, the tree is clean.
## 3. Worktree + isolated environment per agent
```bash
git worktree add ../<proj>-s<N> -b stage-<N>-<name>
```
Python trap: a shared venv imports SOMEONE ELSE's code (editable install of the main
repo). Give each worktree its own venv:
```bash
cd ../<proj>-s<N> && python -m venv .venv \
&& ./.venv/Scripts/python.exe -m pip install -q -e "./api[dev]"
```
Install several venvs sequentially in one background command (the pip cache is shared).
JS stack: its own `node_modules` per worktree (`npm ci`).
If the orchestrator has a command-wrapper hook (rtk and similar): a relative
interpreter path (`../.venv/Scripts/python.exe`) in briefs does not resolve through
such a hook ("command not found"). In briefs and prompts use only ABSOLUTE paths to
the python/npm of that worktree.
## 4. Briefs - as files, not on the command line
`<repo>/.briefs/stage-<N>.md` (untracked). Brief structure:
- **context**: what to read first (spec, contract, key files), what is already done;
- **task**: concrete requirements referencing spec items;
- **boundaries**: files allowed/forbidden, "do not leave the worktree", "do NOT push";
- **acceptance**: exact test/linter commands (with the absolute interpreter path of
the worktree), "pre-existing tests stay green", commit to its own branch, final report.
A brief must not assume a particular agent kind: do not write "run omp/skill/..."
into it - write the goal, the boundaries and the acceptance commands. The child
decides which of its own tools to use.
The prompt to the agent is short: "Read the file <brief> and complete it fully".
## 5. Panes: create them, name them IMMEDIATELY
Recommended layout - main-left: the orchestrator pane on the left at full height, all
children in a column on the right, one under another. If the user has layout plugins
built around main-left, any other scheme breaks their view.
If the user explicitly asks for a different layout, follow the user.
First child - `split --current --direction right`, the rest -
`split --pane <previous child> --direction down` INSIDE the right column.
Do NOT split the orchestrator pane, and do not split agent panes to the right - only
the down-chain inside the right column.
```bash
herdr pane split --current --direction right --cwd "<worktree1>" --no-focus
herdr pane split --pane <agent1-pane> --direction down --cwd "<worktree2>" --no-focus
```
The new pane ID comes from JSON `.result.pane.pane_id`. Do not touch the user's focus
(`--no-focus`). The child gets its name in step 6 via `agent start`; additionally
`herdr pane rename <pane_id> "s<N>-<name>"` for clarity.
## 6. Starting a child of your own kind
The standard path is `agent start`, which also validates that the expected agent
actually came up in the pane:
```bash
herdr agent start s1-<name> --kind "$KIND" --pane <pane_id> -- <autonomy-flags>
```
The name must match `[a-z][a-z0-9_-]{0,31}` and be unique among live agents.
### Autonomy flags
A child works unattended, otherwise it stops at an approval. The flag belongs to the
CLI, not to Herdr. Confirmed ones:
| kind | launch |
|---|---|
| `omp` | `-- --yolo` |
| `claude` | `-- --dangerously-skip-permissions` (or `--permission-mode bypassPermissions`) |
| `opencode` | `-- --auto` |
For any other kind (codex, gemini, kimi, cursor, copilot, droid, kilo, grok, hermes,
qodercli, mastracode, pi, ...) do NOT invent a flag. Resolve the canonical executable
and read its help:
```bash
herdr agent start --help # the --kind help text names the canonical executable
<executable> --help | grep -iE "permission|approve|yolo|auto|dangerous|allow"
```
No flag found - check whether the CLI has an autonomy mode in its config
(e.g. `~/.omp/agent/config.yml: tools.approvalMode: yolo`,
`~/.claude/settings.json: permissions`, `opencode.json: permission`), and warn the
user that the child may stop at approvals - those surface as the `blocked` state (§7).
### If `agent start` timed out
Known bug on Windows in PowerShell panes: `agent start` sends a mangled
`Start-Process` -> timeout. Workaround - launch the CLI in the pane directly:
```bash
herdr pane run <pane_id> "<executable> <autonomy-flags>"
sleep 3 && herdr pane read <pane_id> --lines 15 # expect the CLI prompt
herdr agent rename <pane_id> s1-<name> # if herdr recognized the agent
```
If `herdr agent explain <pane_id>` still reports no recognized agent afterwards,
structural monitoring is unavailable for that pane - use the §7 fallback.
### Handing over the brief
NOT via `pane run`: Enter gets swallowed while the TUI renders the paste. Two steps
with a pause:
```bash
herdr pane send-text <pane_id> "Read the file <absolute path to the brief> - that is your brief. Complete it fully (code, tests, linter, commit to your own branch), then give a final report."
sleep 5 && herdr pane send-keys <pane_id> Enter
```
Standard alternative once the integration is installed and `agent start` succeeded:
```bash
herdr agent prompt s1-<name> "Read the file <brief> and complete it fully" --wait --timeout 300000
```
Verify with `pane read` that the brief actually WENT IN: input empty, agent working.
## 7. Monitoring - through the integration, NOT cron
```bash
herdr agent list # states of all children
herdr agent wait s1-<name> --until idle --timeout 1800000
herdr agent prompt s1-<name> "<text>" # push an instruction to a working child
herdr agent read s1-<name> --lines 40
```
State semantics: `idle` - ready for input and its tab has been seen in the UI;
`done` - the same idle state after unseen background work (reading through the CLI
does not mark the tab seen); `blocked` - Herdr recognized an approval/question UI,
the child is WAITING for a human; `unknown` - an agent is present but cannot be
classified, which is NOT evidence of completion.
Orchestrator loop: `agent wait` in turn or on an event -> acceptance (§8).
`blocked` -> `agent read`, understand the question, answer via `agent prompt` or ask
the user. Suspicious silence -> `pane read <pane_id>`.
Keep the `wait` timeout moderate (~30 min) and re-arm it on each return: very large
values end up as "timed out".
Fallback when the integration for `$KIND` is unavailable or `agent explain` did not
recognize the child: periodic `herdr pane read <pane_id> --lines 60` plus
`git log/status` in the worktree. Cron only as a last resort, and always remove it
when done.
Child session dropped: the work in the worktree survives. Restart with the same CLI
and its continue flag (check `--help`): `omp --resume`, `claude --continue`,
`opencode --continue`. Then prompt: "Your session was interrupted. Check git status
and finish the brief <file>".
## 8. Acceptance and merge
- Each branch: tests + linter in its own worktree, review `git diff main...<branch> --stat`.
- Do not take the child's final report on faith - run the acceptance commands yourself.
- Merge into main only with the user's confirmation; resolve additive overlaps manually.
- After the merge: `git worktree remove`; branches as agreed with the user.
- Release the children's panes without touching the user's pane.
FILE:README.md
# herdr-multiagent
An agent skill (playbook) for driving a project with **several coding agents in
parallel** through [Herdr](https://herdr.dev), a terminal multiplexer for coding
agents — one git worktree and one pane per stage, file-based briefs, state
monitoring and acceptance.
The skill is **agent-agnostic**: the kind of the children is resolved from Herdr and
matches the kind of the orchestrator. Launched from `opencode`, the children are
`opencode`; from `omp`, they are `omp`; from `claude`, they are `claude`. Any kind
listed by `herdr agent start --help` works (pi, claude, codex, gemini, cursor, devin,
agy, cline, omp, mastracode, opencode, copilot, kimi, kiro, droid, amp, grok, hermes,
kilo, qodercli, maki).
## What it covers
- §1 resolve your own kind, verify the Herdr integration for it;
- §2 decompose into stages with non-overlapping file areas;
- §3 worktree + isolated environment (own venv / node_modules — otherwise agents
import someone else's code through the main repo's editable install);
- §4 briefs as files, not on the command line;
- §5 main-left pane layout, `--no-focus` (the user's focus is never taken);
- §6 starting a child, autonomy flags per kind, the Windows `agent start` timeout
workaround, correct brief hand-over (Enter gets swallowed by `pane run`);
- §7 monitoring via `herdr agent list/wait/prompt/read`, the semantics of
`idle/done/blocked/unknown`, fallback to `pane read` + git, recovering a dropped
child session;
- §8 acceptance and merge only with the user's confirmation.
## Requirements
- Herdr, with the session running inside one of its panes (`HERDR_ENV=1`). Outside
Herdr the skill stops.
- Git (worktrees).
- One supported agent CLI on `PATH`.
- For structural monitoring: `herdr integration install <kind>`. Kinds without an
integration fall back to `pane read` + git — not a blocker.
- Verified on Windows (Git Bash + PowerShell panes); the commands are POSIX, with an
explicit note where Windows venv paths differ.
## Installation
A skill is a directory containing `SKILL.md`. Put it into your agent's skills root:
| Agent | path (verified on the author's machine) |
|---|---|
| omp, pi | `~/.agents/skills/herdr-multiagent/SKILL.md` |
| Claude Code | `~/.claude/skills/herdr-multiagent/SKILL.md` |
| opencode | `~/.config/opencode/skills/herdr-multiagent/SKILL.md` |
| project-local | `<repo>/.agents/skills/herdr-multiagent/SKILL.md` |
The layout is non-recursive: `<skills-root>/<skill-name>/SKILL.md`. A nested path
like `skills/team/herdr-multiagent/SKILL.md` is not discovered.
Check your own CLI's docs for the exact skills root — the directories differ per
agent, while `SKILL.md` with `name` + `description` frontmatter is read the same way.
## Usage
Explicitly: ask the agent to "work according to the herdr-multiagent skill", or
invoke `/skill:herdr-multiagent` (in omp, when skill commands are enabled).
Automatically: the skill is picked up when the task reads like "build this with
several agents in parallel" and the agent runs inside Herdr.
The first thing the agent does is check `HERDR_ENV=1` and resolve its own kind; then
it proposes a decomposition and asks for confirmation before starting any child.
## Layout
```
herdr-multiagent/
├─ SKILL.md # the skill body: frontmatter (name, description) + §0–§8
└─ README.md # this file, for humans; the agent does not need it
```
Extra assets (scripts, brief templates, `references/*.md`) go into the same directory
and are read by the agent via `skill://herdr-multiagent/<path>`. There are none here:
the playbook fits in a single file, and the brief template is described in prose in §4.
## Safety
Children run in an autonomy mode (`omp --yolo`, `claude
--dangerously-skip-permissions`, `opencode --auto`) — without approval prompts. That
means full filesystem and shell access inside their worktree. The skill constrains
them through the brief ("do not leave the worktree", "do NOT push"), but that is an
instruction, not isolation. Merging into main happens only on the user's explicit
confirmation.
## License
Free to use.Design and creation of a marketing plan on Social Media platforms to market Hayek Travel services and bicycles in Britain. The target segment is Gulf students and Arab tourists.
Teach me module 2 properly and sequentially in proper flow, compare both document and include every numerical and theory sequentially from both documents
Have this for your reference - Here are the extracted details and links for Digipromo Agency (Digipromo Services Private Limited) based in Chennai, gathered directly from their website and digital footprint: Website & Contact Links Primary Website: https://www.digipromoagency.com/ Alternative Domain: https://digipromo.in/ Email (General): info@digipromoagency.com Email (Sales): sales@digipromoagency.com Contact Information Phone / WhatsApp: +91 900-305-2900 / +91 900-305-7850 Headquarters Address: 10/11, 1st Floor, Indusind Bank ATM Upstairs, Aranganathan Subway Road, Kavery Nagar, Saidapet, Chennai, India 600 015. (Landmark: Near Vasanth & Co) Social Media Presence While their website displays icons for Facebook, Instagram, YouTube, Twitter (X), and LinkedIn in the footer, they appear to be placeholder links that do not currently direct to active public profiles. If you are trying to reach out to them for services or a business inquiry, utilizing their direct WhatsApp/Phone numbers or the sales@digipromoagency.com email address will be your most direct line of communication. Now i want u to mak something like, end to end analysis , reserch about this company - Potential if have any , all the negative sides , potential drawbags and things like that - And , Now mainly something like revical of this business and also about the market , chennai based things and all tjose things like tgat ,proper revival , the potential of the agency and something like proper revival to thr max , and about the market like saturated market or something like that . And abpve emntioned basic idea for your referebce and things like that
I need to make a social website designed for robinhood chain users to use,
You are a senior competitive programmer proficient in writing time and space optimized programs in C++, Java and Python. 1. Understand the problem without keeping any bias for a particular topic. 2. Try to find out the pattern of the problem. 3. Formulate an approach around the pattern to solve the question. 4. Generate 20 edge cases and evaluate your code on these test cases. 5. Look for optimization possibilities in terms of time and space. 6. Return the final code.
I wanna build an AI that can learning a deck in Master duel called Kewl Tune that can win against pro players with differents decks , This AI can build combos and evo his level with a tool called ygo agent on git hub and with Kaggle Notebook , Im vibe coder have a little bit of info about python and linux commands , This project is only for fun
Act as my Instagram reel script writer and write a script which have strong hook, engaging body,and strong end. And zero skip rate