Prompt Pharmacy

role: Open Source Release Checklist Builder
goal: >
  Turn an upcoming open-source release into a concrete YAML checklist covering
  version bump, changelog, artifacts, tests, security notes, and announce
  channels, so maintainers ship without forgetting a step.

ask_first:
  - project: "Repo name, primary language, and package registry (npm, PyPI, crates.io, Maven, Go module, or none)"
  - current_version: "SemVer currently on main/default branch"
  - target_version: "Proposed SemVer and bump type (patch | minor | major | pre-release)"
  - changes_since_last: "Bullet list of user-facing changes, breaking changes, deprecations"
  - artifacts: "What you publish (library, CLI binary, Docker image, docs site, GitHub Release assets)"
  - announce_channels: "GitHub Release, Discord, Slack, mailing list, X/Twitter, blog, none"
  - signing: "Do you sign tags or artifacts? (yes | no | unknown)"

rules:
  - Prefer Conventional Commits / Keep a Changelog style wording in the changelog draft.
  - Never invent CVE IDs or security claims; leave placeholders if unknown.
  - Mark breaking changes loudly and include a migration note stub.
  - Include a rollback step (yank, previous tag, previous image digest) when publishing packages or images.
  - If tests or CI are red, set ship_ready to false and list blockers first.
  - Do not include secrets, tokens, or private URLs in the output.

output:
  release:
    project: string
    from_version: string
    to_version: string
    bump: patch | minor | major | pre-release
    ship_ready: true | false
    blockers: [string]
  checklist:
    version_bump:
      - step: string
        owner: string
        done: false
    changelog:
      summary: string
      highlights: [string]
      breaking: [string]
      migration_notes: [string]
    quality_gates:
      - gate: "tests | lint | typecheck | security scan | docs build"
        status: pass | fail | skip | unknown
        notes: string
    artifacts:
      - name: string
        how: string
        verify: string
    git_and_tags:
      - step: "commit | tag annotated | push tags | GitHub Release"
        command_hint: string
    announce:
      - channel: string
        draft_blurb: string
    post_release:
      - step: string
  rollback:
    triggers: [string]
    steps: [string]

constraints:
  - Return valid YAML only matching the output schema.
  - Keep draft_blurb under 280 characters for social channels.
  - Prefer explicit command_hint examples using placeholders like <TAG>, not real secrets.

Twin peacocks facing each other beside lotus ponds, rendered as a traditional Indonesian batik textile pattern in indigo and cream. The peacocks fill the center with mirrored S-curved necks, fan tails filled with dotted isen motifs, and small floral medallions along the wings. Around them, oval lotus ponds hold blooming lotus flowers, pads, and tiny fish drawn as repeating geometric fills. A classic batik border frames the cloth: interlocking kawung and ceplok shapes with fine crackle lines from wax-resist dyeing. Palette limited to deep indigo blue, soft cream ground, and a few pale grey-blue washes. Hand-drawn irregularity in the lines, visible wax crackle texture, cloth weave grain, flat ornamental layout suitable for fabric. No people, no modern objects, no text, no signature.

A lighthouse keeper's kitchen painted in rich oil, warm and lived-in while a storm rages outside. Thick impasto strokes show whitewashed stone walls, a heavy wooden table with a brass kettle steaming, an oil lamp casting amber light, and a loaf of bread on a worn cutting board. Rain streaks the small paned window; through the glass, dark grey sea and white spray. Wet yellow oilskins and rubber boots hang dripping by the door. Copper pans and a enamel mug sit on open shelves. Cozy interior warmth contrasts with cold storm light at the window. Visible brushwork, muted ochres and deep blues, slight varnish sheen. No people, no text, no modern appliances.

An isometric voxel game scene of a mountain hot spring inn at dusk. Colorful blocky voxels build a wooden onsen inn with sliding doors, a tiled roof, and paper lanterns glowing warm orange along a stone path. Steam rises in soft white cubes from outdoor stone baths. Pine trees and rocky cliffs frame the inn; a small stream of blue voxels runs past mossy rocks. Lantern light pools on the path while the sky shifts violet to deep blue. Cheerful game-art palette, clean voxel edges, slight ambient occlusion in crevices, no UI, no text, no characters. Looking slightly down from a classic isometric angle.

Act as an architectural concept designer who specializes in small craft studios beside water. Help me build a clear, image-ready design brief for a lakeside pottery studio with a wood-fired kiln.

First ask me, in one short list, for: the lake setting (forest shore, rocky cove, open meadow), clay and firing style (stoneware, earthenware, wood-fired, electric assist), studio vibe (rustic cedar, modern glass-and-timber, Japanese-inspired), footprint budget (modest shed, mid-size workshop, generous), and any must-haves (dock, glaze sink, drying racks, guest seating, overnight loft).

Then produce the brief with these sections:

1. **Concept in One Sentence**: the feeling and purpose of the studio.
2. **Site and Orientation**: where it sits on the shore, which way the big windows face for light and lake view, kiln shed placement for smoke and wind, path from parking or path to the dock.
3. **Form and Massing**: footprint, roof shape, open bay doors for the wheel room, and a separate or attached kiln shed, with rough dimensions.
4. **Materials and Palette**: exterior cladding (for example weathered cedar), roofing, window frames, dock boards, and interior wood and plaster tones.
5. **Interior Layout**: potter's wheels by the lake-facing windows, wedging table, glaze bench, drying shelves, sink, wood stove if needed, and storage for clay and tools.
6. **Kiln and Safety Notes**: wood-fired kiln orientation, ash and smoke path, safe clearances, and how the shed opens for loading.
7. **Fixed Visual Details**: a numbered list of 8 to 10 details that must stay identical in every image of this studio (cladding, roof color, dock shape, kiln chimney, window count, tree types, water color, golden-hour light direction).
8. **Image Prompt Seeds**: two short prompt starters, one for a golden-hour exterior with dock and kiln shed, and one for a blue-hour interior workbench looking out to the water. Each must restate the fixed visual details and put the key subject in the first sentence.

Keep it concise, practical, and buildable. Avoid naming real architects or brands. Flag anything unsafe for wood firing near trees or a dock.

A cedar lakeside pottery studio with a wood-fired kiln shed at golden hour, exterior view from the dock side. Weathered vertical cedar boards, a low pitched metal roof, and wide open bay doors reveal two potter's wheels and drying shelves inside. A small wooden dock extends into calm lake water reflecting warm amber light. Beside the studio, a separate kiln shed with a brick chimney sends a thin thread of pale smoke. Stone path, stacked firewood, a few unfinished pots on outdoor racks, pine and birch trees along the shore. Soft long shadows, golden rim light on the cedar, peaceful craft-retreat mood. Photoreal, landscape 16:9. No people, no text, no cars.

Interior workbench room of a cedar lakeside pottery studio at blue hour, looking out to the water. A heavy wooden wedging table fills the foreground with clay tools, a wire cutter, and a few leather-hard bowls. Shelves of drying pots line the left wall. Through a wide rectangular window and open barn-style double bay doors with X-braces, the lake sits deep blue under a violet dusk sky; the wide wooden plank dock with its T-shaped extension, a small red rowboat, and slender birch trees are visible outside. Warm task lamp light on the bench contrasts cool blue window light. Vertical cedar interior boards, plaster accents, pottery wheel at the right edge. Same studio as the golden-hour exterior: weathered vertical cedar boards, dark pitched metal roof with a brick chimney, wide plank dock, two small side windows, pines and birches. Photoreal, landscape 16:9. No people, no text.

---
name: openapi-spec-drift-checker
description: Compares an OpenAPI YAML/JSON spec to a list of implemented routes and flags drift: missing operations, extra routes, method mismatches, and path-parameter shape differences. Use when reviewing API PRs, before a release, or asking "does our code still match the OpenAPI?". Includes a stdlib Python checker with --self-test.
---

# OpenAPI Spec Drift Checker

You help API teams find drift between an OpenAPI 3.x document and the routes their code actually serves. You compare paths and methods, flag missing or extra operations, and call out parameter or response mismatches that break clients. You do not invent endpoints; you only report what the inputs show.

## Files in this skill

- `scripts/check_drift.py` - compares a small OpenAPI snippet to a route list and prints a drift report (Python 3 standard library only)
- `references/openapi-checklist.md` - what to verify in the spec before comparing
- `references/common-drift.md` - frequent causes of drift and how to fix them
- `templates/drift-report.md` - report structure for PRs and release notes
- `examples/openapi-snippet.yaml` - sample OpenAPI fragment for demos

## Workflow

### 1. Collect inputs
Ask for, or infer:
- OpenAPI file (YAML or JSON) or a pasted paths section
- Implemented routes: method + path list from the framework router, generated from code, or a gateway config
- Whether path-parameter names must match exactly (`{id}` vs `{userId}`)

### 2. Normalize
Lowercase methods. Strip trailing slashes. Treat `/users/{id}` and `/users/{userId}` as the same path shape unless the team requires exact parameter names.

### 3. Run the checker
```
python3 scripts/check_drift.py --spec examples/openapi-snippet.yaml --routes examples/routes.txt
python3 scripts/check_drift.py --self-test
```
If routes are not in a file yet, help the user extract them from code first.

### 4. Classify findings
- **missing_in_code**: in OpenAPI, not implemented
- **missing_in_spec**: implemented, not in OpenAPI
- **method_mismatch**: path exists but verbs differ
- **param_shape_diff**: same path pattern, different parameter names (optional strict mode)

### 5. Answer
Return a short summary counts table, then each finding with severity (high/medium/low) and a suggested fix. Use `templates/drift-report.md` for PR comments.

## Rules

- Prefer documenting reality when the code is intentional and the spec is stale; prefer implementing when the spec is the contract already published to clients.
- Never claim security review was done; only report structural drift.
- If the OpenAPI cannot be parsed, stop and show the parse error.
FILE:references/openapi-checklist.md
# OpenAPI pre-compare checklist

Before running drift checks, confirm the spec is coherent:

1. **openapi** field is `3.0.x` or `3.1.x`.
2. **paths** exist and use leading `/`.
3. Every operation has an HTTP method (`get`, `post`, `put`, `patch`, `delete`, `head`, `options`).
4. Path parameters declared in the path (`{id}`) also appear under `parameters` with `in: path` and `required: true`.
5. `$ref` targets resolve (or you are comparing a resolved/bundled document).
6. Servers URL base path is understood: if `servers[].url` includes `/api/v1`, routes may be listed with or without that prefix—pick one convention and stick to it for the compare.
7. Deprecated operations are still listed until removed from code; mark them in the report rather than ignoring them.

## Minimal paths shape

```yaml
paths:
  /pets:
    get:
      summary: List pets
    post:
      summary: Create pet
  /pets/{petId}:
    get:
      parameters:
        - name: petId
          in: path
          required: true
          schema: { type: string }
```
FILE:references/common-drift.md
# Common OpenAPI drift

| Symptom | Likely cause | Fix |
| --- | --- | --- |
| Spec has `GET /users`, code has none | Spec written ahead of implementation | Implement or remove from published spec |
| Code has `POST /users/{id}/reset`, missing in spec | Hotfix shipped without doc update | Add operation; bump minor if public |
| Spec `PUT`, code `PATCH` | Partial-update semantics changed | Align method; note breaking if clients call PUT |
| `{userId}` vs `{id}` | Framework auto-names vs hand-written spec | Normalize in compare; document naming rule |
| Trailing slash only in code | Router is slash-tolerant | Strip for compare; pick canonical form in spec |
| Base path `/api` only in gateway | Spec servers omit gateway prefix | Strip or add the same prefix on both sides |
| Webhooks / callbacks only in spec | Not HTTP routes | Exclude from route compare |
| File upload route undocumented | Multipart added late | Document `multipart/form-data` requestBody |

## Severity guide

- **High**: public clients already call the operation; method or path missing on either side.
- **Medium**: internal-only route missing from spec; param rename with aliases.
- **Low**: trailing slash, optional HEAD/OPTIONS noise, deprecated-but-present.
FILE:templates/drift-report.md
# OpenAPI drift report

- **Spec**: <path or URL>
- **Routes source**: <router dump / gateway / manual>
- **Compared at**: <ISO timestamp>
- **Summary**: missing_in_code=<n>, missing_in_spec=<n>, method_mismatch=<n>, param_shape_diff=<n>

## Findings

| Severity | Kind | Spec | Code | Notes |
| --- | --- | --- | --- | --- |
| high | missing_in_code | `GET /pets/{petId}` | — | Implement or unpublish |
| medium | missing_in_spec | — | `POST /pets/{petId}/adopt` | Document before release |

## Decision

- [ ] Update OpenAPI
- [ ] Update code
- [ ] Accept as known exception (list why)

## Follow-ups

- [ ]
FILE:examples/openapi-snippet.yaml
openapi: 3.0.3
info:
  title: Pets demo
  version: 0.1.0
paths:
  /pets:
    get:
      summary: List pets
      operationId: listPets
    post:
      summary: Create pet
      operationId: createPet
  /pets/{petId}:
    get:
      summary: Get pet by id
      operationId: getPet
      parameters:
        - name: petId
          in: path
          required: true
          schema:
            type: string
    delete:
      summary: Delete pet
      operationId: deletePet
      parameters:
        - name: petId
          in: path
          required: true
          schema:
            type: string
  /health:
    get:
      summary: Health check
      operationId: health
FILE:scripts/check_drift.py
#!/usr/bin/env python3
"""Compare OpenAPI paths to an implemented route list.

Usage:
    python3 check_drift.py --spec openapi.yaml --routes routes.txt
    python3 check_drift.py --spec openapi.yaml --route 'GET /pets' --route 'POST /pets'
    python3 check_drift.py --self-test

Route file format: one "METHOD /path" per line. Comments (#) and blank lines ignored.
Python 3 standard library only.
"""
from __future__ import annotations

import argparse
import json
import re
import sys
from typing import Dict, List, Set, Tuple

try:
    import yaml  # type: ignore
except ImportError:
    yaml = None

PathKey = str  # normalized path template
Method = str


def normalize_path(path: str) -> str:
    path = path.strip()
    if not path.startswith("/"):
        path = "/" + path
    if len(path) > 1 and path.endswith("/"):
        path = path.rstrip("/")
    # unify parameter names to {}
    return re.sub(r"\{[^}]+\}", "{}", path)


def normalize_method(m: str) -> str:
    return m.strip().upper()


def parse_routes(lines: List[str]) -> Dict[PathKey, Set[Method]]:
    out: Dict[PathKey, Set[Method]] = {}
    for raw in lines:
        line = raw.split("#", 1)[0].strip()
        if not line:
            continue
        parts = line.split()
        if len(parts) < 2:
            raise ValueError(f"Bad route line: {raw!r}")
        method, path = normalize_method(parts[0]), normalize_path(parts[1])
        out.setdefault(path, set()).add(method)
    return out


def _parse_openapi_json(text: str) -> dict:
    return json.loads(text)


def _parse_openapi_yaml_simple(text: str) -> dict:
    """Minimal YAML subset parser for simple OpenAPI path maps when PyYAML is absent.

    Supports the examples shipped with this skill (no anchors, no multiline).
    Prefer PyYAML when installed.
    """
    # Very small fallback: extract path keys and methods via regex for our demos.
    paths: Dict[str, dict] = {}
    current_path = None
    for raw in text.splitlines():
        if raw.startswith("  /") and raw.rstrip().endswith(":"):
            current_path = raw.strip()[:-1]
            paths[current_path] = {}
            continue
        if current_path and re.match(r"^    (get|post|put|patch|delete|head|options):", raw):
            method = raw.strip().split(":", 1)[0]
            paths[current_path][method] = {}
    if not paths:
        raise ValueError("Could not parse OpenAPI YAML without PyYAML; install pyyaml or pass JSON.")
    return {"paths": paths}


def load_spec(path: str) -> Dict[PathKey, Set[Method]]:
    text = open(path, encoding="utf-8").read()
    if path.endswith(".json"):
        data = _parse_openapi_json(text)
    else:
        if yaml is not None:
            data = yaml.safe_load(text)
        else:
            # try JSON first, then simple YAML
            try:
                data = _parse_openapi_json(text)
            except json.JSONDecodeError:
                data = _parse_openapi_yaml_simple(text)
    paths = data.get("paths") or {}
    out: Dict[PathKey, Set[Method]] = {}
    http = {"get", "post", "put", "patch", "delete", "head", "options", "trace"}
    for p, item in paths.items():
        if not isinstance(item, dict):
            continue
        methods = {normalize_method(m) for m in item if m.lower() in http}
        if methods:
            out[normalize_path(p)] = methods
    return out


def compare(spec: Dict[PathKey, Set[Method]], code: Dict[PathKey, Set[Method]]) -> List[dict]:
    findings = []
    all_paths = set(spec) | set(code)
    for p in sorted(all_paths):
        s = spec.get(p, set())
        c = code.get(p, set())
        if s and not c:
            for m in sorted(s):
                findings.append({"kind": "missing_in_code", "severity": "high", "method": m, "path": p})
        elif c and not s:
            for m in sorted(c):
                findings.append({"kind": "missing_in_spec", "severity": "medium", "method": m, "path": p})
        else:
            only_spec = s - c
            only_code = c - s
            for m in sorted(only_spec):
                findings.append({"kind": "method_mismatch", "severity": "high", "method": m, "path": p, "side": "spec_only"})
            for m in sorted(only_code):
                findings.append({"kind": "method_mismatch", "severity": "high", "method": m, "path": p, "side": "code_only"})
    return findings


def format_report(findings: List[dict]) -> str:
    counts: Dict[str, int] = {}
    for f in findings:
        counts[f["kind"]] = counts.get(f["kind"], 0) + 1
    lines = ["Drift summary: " + ", ".join(f"{k}={v}" for k, v in sorted(counts.items()))]
    if not findings:
        lines.append("No drift found.")
        return "\n".join(lines)
    for f in findings:
        side = f.get("side", "")
        extra = f" ({side})" if side else ""
        lines.append(f"- [{f['severity']}] {f['kind']}: {f['method']} {f['path']}{extra}")
    return "\n".join(lines)


def self_test() -> int:
    spec = {
        normalize_path("/pets"): {"GET", "POST"},
        normalize_path("/pets/{petId}"): {"GET", "DELETE"},
        normalize_path("/health"): {"GET"},
    }
    code = {
        normalize_path("/pets"): {"GET", "POST"},
        normalize_path("/pets/{id}"): {"GET"},  # same shape, missing DELETE
        normalize_path("/pets/{id}/adopt"): {"POST"},  # extra
    }
    findings = compare(spec, code)
    kinds = sorted(f["kind"] + ":" + f["method"] + ":" + f["path"] for f in findings)

    cases = [
        ("missing delete on pet", any(f["kind"] == "method_mismatch" and f["method"] == "DELETE" for f in findings), True),
        ("extra adopt route", any(f["kind"] == "missing_in_spec" and f["path"].endswith("/adopt") for f in findings), True),
        ("health missing in code", any(f["kind"] == "missing_in_code" and f["path"] == "/health" for f in findings), True),
        ("list pets matched", not any(f["path"] == "/pets" and f["kind"] == "missing_in_code" for f in findings), True),
        ("normalize params", normalize_path("/pets/{petId}") == normalize_path("/pets/{id}"), True),
        ("normalize slash", normalize_path("/pets/") == "/pets", True),
        ("parse routes", parse_routes(["GET /pets", "POST /pets # create"]) == {"/pets": {"GET", "POST"}}, True),
        ("empty compare", compare({"/x": {"GET"}}, {"/x": {"GET"}}) == [], True),
    ]

    # parse simple yaml fallback
    sample = """openapi: 3.0.3\npaths:\n  /pets:\n    get:\n      summary: x\n    post:\n      summary: y\n"""
    parsed = _parse_openapi_yaml_simple(sample)
    cases.append(("yaml fallback paths", "get" in parsed["paths"]["/pets"] and "post" in parsed["paths"]["/pets"], True))

    failed = 0
    for name, got, expected in cases:
        ok = got == expected
        failed += not ok
        print(f"{'PASS' if ok else 'FAIL'}  {name}: got={got!r} expected={expected!r}")
    # also assert finding count sanity
    ok = len(findings) >= 3
    failed += not ok
    print(f"{'PASS' if ok else 'FAIL'}  findings_count>={3}: got={len(findings)}")
    print(f"\n{len(cases)+1 - failed}/{len(cases)+1} passed")
    return 1 if failed else 0


def main(argv=None) -> int:
    p = argparse.ArgumentParser(description="Compare OpenAPI paths to implemented routes.")
    p.add_argument("--spec", help="OpenAPI YAML or JSON file")
    p.add_argument("--routes", help="File with METHOD /path lines")
    p.add_argument("--route", action="append", default=[], help="Single METHOD /path (repeatable)")
    p.add_argument("--json", action="store_true", help="Print findings as JSON")
    p.add_argument("--self-test", action="store_true")
    a = p.parse_args(argv)
    if a.self_test:
        return self_test()
    if not a.spec:
        p.error("--spec is required (or use --self-test)")
    route_lines: List[str] = list(a.route)
    if a.routes:
        route_lines.extend(open(a.routes, encoding="utf-8").read().splitlines())
    if not route_lines:
        p.error("provide --routes and/or --route")
    spec = load_spec(a.spec)
    code = parse_routes(route_lines)
    findings = compare(spec, code)
    if a.json:
        print(json.dumps(findings, indent=2))
    else:
        print(format_report(findings))
    return 0 if not findings else 2


if __name__ == "__main__":
    sys.exit(main())

---
name: accessibility-color-contrast-auditor
description: Audits foreground/background color pairs for WCAG 2.x contrast (AA and AAA for normal and large text), reports ratios and pass/fail, and notes color-blindness risks. Use when checking UI palettes, design tokens, or asking "is this gray-on-white readable?". Includes a stdlib Python contrast checker with --self-test.
---

# Accessibility Color Contrast Auditor

You help designers and frontend engineers check whether text and UI colors meet WCAG 2.x contrast ratios. You compute relative luminance contrast for foreground/background pairs, map results to AA/AAA for normal and large text, and flag pairs that rely on hue alone for meaning.

## Files in this skill

- `scripts/contrast_check.py` - computes contrast ratios for hex pairs and prints a pass table (Python 3 standard library only)
- `references/wcag-contrast.md` - thresholds and what counts as large text
- `references/color-blindness-notes.md` - why hue-only status colors fail real users
- `templates/audit-table.md` - markdown table for design reviews
- `examples/palette.json` - sample token pairs

## Workflow

### 1. Collect pairs
Ask for hex colors (`#RGB`, `#RRGGBB`, or `#RRGGBBAA` with alpha ignored against an opaque background). Note which is text/icon (fg) and which is surface (bg).

### 2. Run the checker
```
python3 scripts/contrast_check.py --fg '#1a1a1a' --bg '#ffffff'
python3 scripts/contrast_check.py --palette examples/palette.json
python3 scripts/contrast_check.py --self-test
```

### 3. Interpret
- Normal text AA needs **4.5:1**; AAA needs **7:1**.
- Large text (18pt+ or 14pt+ bold) AA needs **3:1**; AAA needs **4.5:1**.
- Non-text UI components / graphical objects that convey meaning need **3:1** against adjacent colors (WCAG 2.1 SC 1.4.11).

### 4. Answer
For each pair: ratio (one decimal), AA/AAA normal, AA/AAA large, and a short fix suggestion (darken fg, lighten bg, or add weight/size). Mention color-blindness if status depends on red vs green alone.

## Rules

- Use the WCAG relative luminance formula; do not approximate with "eyeballing".
- Ignore marketing claims about "accessible mode" unless ratios are shown.
- If alpha blending is involved, ask for the effective flattened hex on the real background.
FILE:references/wcag-contrast.md
# WCAG contrast thresholds (WCAG 2.1 / 2.2)

| Content | AA | AAA |
| --- | --- | --- |
| Normal text | 4.5:1 | 7:1 |
| Large text | 3:1 | 4.5:1 |
| UI components & graphical objects (1.4.11) | 3:1 | — |

## Large text

Large text is at least **18 CSS pt** (typically 24px) regular, or **14 CSS pt** (typically 18.66px) bold.

## Relative luminance

For sRGB channel C_srgb in 0..1:

- if C_srgb <= 0.04045: C = C_srgb / 12.92
- else: C = ((C_srgb + 0.055) / 1.055) ^ 2.4

L = 0.2126 R + 0.7152 G + 0.0722 B

Contrast ratio = (L_lighter + 0.05) / (L_darker + 0.05)

## Notes

- Placeholder / disabled text is exempt from text contrast SCs but still benefits from clarity.
- Focus indicators need 3:1 against adjacent colors (2.4.11 / 2.4.13 depending on level).
FILE:references/color-blindness-notes.md
# Color blindness notes for palettes

Contrast ratio alone is not enough when meaning is carried only by hue.

## Common pitfalls

- **Red vs green status** without icon or label: deuteranomaly/protanomaly users may not tell them apart.
- **Chart series** distinguished only by color: add patterns, shapes, or direct labels.
- **Link color** same lightness as body text: underline or weight must remain when color is removed.
- **Error borders** that are only a pale red tint: ensure 3:1 against the background and include text/icon.

## Safer patterns

1. Pair color with text ("Error", "Success") or icon.
2. Use lightness differences, not only hue shifts.
3. Test a grayscale screenshot of the UI.
4. Avoid conveying required-field state with red alone.

## Deuteranopia-friendly tip

Prefer blue/orange or blue/brown pairs over red/green for categorical data when you must use two hues.
FILE:templates/audit-table.md
# Contrast audit

- **Product / screen**:
- **Date**:
- **Standard**: WCAG 2.2 AA (default) / AAA where noted

| Token / use | FG | BG | Ratio | AA normal | AAA normal | AA large | Notes / fix |
| --- | --- | --- | --- | --- | --- | --- | --- |
| body text | `#1a1a1a` | `#ffffff` | 16.1 | pass | pass | pass | |
| muted caption | `#9ca3af` | `#ffffff` | 2.5 | fail | fail | fail | Darken to ~`#6b7280` |

## Decisions

- [ ] Update tokens
- [ ] Accept large-text-only usage
- [ ] Add non-color cue
FILE:examples/palette.json
{
  "pairs": [
    {"name": "body_on_white", "fg": "#1a1a1a", "bg": "#ffffff"},
    {"name": "muted_on_white", "fg": "#9ca3af", "bg": "#ffffff"},
    {"name": "link_on_white", "fg": "#2563eb", "bg": "#ffffff"},
    {"name": "button_label", "fg": "#ffffff", "bg": "#2563eb"},
    {"name": "error_on_white", "fg": "#dc2626", "bg": "#ffffff"},
    {"name": "white_on_light_gray", "fg": "#ffffff", "bg": "#e5e7eb"}
  ]
}
FILE:scripts/contrast_check.py
#!/usr/bin/env python3
"""WCAG contrast ratio checker for hex fg/bg pairs.

Usage:
    python3 contrast_check.py --fg '#1a1a1a' --bg '#ffffff'
    python3 contrast_check.py --palette palette.json
    python3 contrast_check.py --self-test

Python 3 standard library only.
"""
from __future__ import annotations

import argparse
import json
import math
import sys
from typing import Dict, List, Tuple


def parse_hex(color: str) -> Tuple[float, float, float]:
    c = color.strip().lstrip("#")
    if len(c) == 3:
        c = "".join(ch * 2 for ch in c)
    if len(c) == 8:
        c = c[:6]  # ignore alpha
    if len(c) != 6:
        raise ValueError(f"Bad hex color: {color!r}")
    r = int(c[0:2], 16) / 255.0
    g = int(c[2:4], 16) / 255.0
    b = int(c[4:6], 16) / 255.0
    return r, g, b


def channel(c: float) -> float:
    if c <= 0.04045:
        return c / 12.92
    return ((c + 0.055) / 1.055) ** 2.4


def relative_luminance(rgb: Tuple[float, float, float]) -> float:
    r, g, b = rgb
    return 0.2126 * channel(r) + 0.7152 * channel(g) + 0.0722 * channel(b)


def contrast_ratio(fg: str, bg: str) -> float:
    l1 = relative_luminance(parse_hex(fg))
    l2 = relative_luminance(parse_hex(bg))
    lighter, darker = max(l1, l2), min(l1, l2)
    return (lighter + 0.05) / (darker + 0.05)


def evaluate(ratio: float) -> Dict[str, bool]:
    return {
        "aa_normal": ratio >= 4.5,
        "aaa_normal": ratio >= 7.0,
        "aa_large": ratio >= 3.0,
        "aaa_large": ratio >= 4.5,
        "ui_3_1": ratio >= 3.0,
    }


def format_row(name: str, fg: str, bg: str) -> str:
    ratio = contrast_ratio(fg, bg)
    ev = evaluate(ratio)
    flags = " ".join(
        f"{k}={'PASS' if v else 'FAIL'}"
        for k, v in ev.items()
        if k in ("aa_normal", "aaa_normal", "aa_large")
    )
    return f"{name}: {fg} on {bg} -> {ratio:.2f}:1  {flags}"


def self_test() -> int:
    cases = [
        ("black_white", contrast_ratio("#000000", "#ffffff"), 21.0),
        ("white_black", contrast_ratio("#ffffff", "#000000"), 21.0),
        ("body", contrast_ratio("#1a1a1a", "#ffffff") > 12.0, True),
        ("muted_fail_aa", evaluate(contrast_ratio("#9ca3af", "#ffffff"))["aa_normal"], False),
        ("link_aa", evaluate(contrast_ratio("#2563eb", "#ffffff"))["aa_normal"], True),
        ("same_color", abs(contrast_ratio("#ffffff", "#ffffff") - 1.0) < 1e-9, True),
        ("short_hex", abs(contrast_ratio("#000", "#fff") - 21.0) < 1e-9, True),
        ("parse3", parse_hex("#abc") == parse_hex("#aabbcc"), True),
        ("ui_pass", evaluate(3.0)["ui_3_1"], True),
        ("aaa_border", evaluate(7.0)["aaa_normal"], True),
        ("just_under_aa", evaluate(4.49)["aa_normal"], False),
    ]
    failed = 0
    for name, got, expected in cases:
        if isinstance(expected, float):
            ok = abs(float(got) - expected) < 0.05
        else:
            ok = got == expected
        failed += not ok
        print(f"{'PASS' if ok else 'FAIL'}  {name}: got={got!r} expected={expected!r}")
    print(f"\n{len(cases) - failed}/{len(cases)} passed")
    return 1 if failed else 0


def main(argv=None) -> int:
    p = argparse.ArgumentParser(description="Check WCAG contrast for fg/bg hex pairs.")
    p.add_argument("--fg")
    p.add_argument("--bg")
    p.add_argument("--palette", help="JSON file with {pairs:[{name,fg,bg}]}")
    p.add_argument("--json", action="store_true")
    p.add_argument("--self-test", action="store_true")
    a = p.parse_args(argv)
    if a.self_test:
        return self_test()
    rows = []
    if a.palette:
        data = json.load(open(a.palette, encoding="utf-8"))
        for pair in data.get("pairs", []):
            rows.append(pair)
    elif a.fg and a.bg:
        rows.append({"name": "pair", "fg": a.fg, "bg": a.bg})
    else:
        p.error("provide --fg and --bg, or --palette, or --self-test")
    out = []
    any_fail = False
    for row in rows:
        ratio = contrast_ratio(row["fg"], row["bg"])
        ev = evaluate(ratio)
        if not ev["aa_normal"]:
            any_fail = True
        item = {"name": row.get("name", "pair"), "fg": row["fg"], "bg": row["bg"], "ratio": round(ratio, 2), **ev}
        out.append(item)
        if not a.json:
            print(format_row(item["name"], item["fg"], item["bg"]))
    if a.json:
        print(json.dumps(out, indent=2))
    return 1 if any_fail else 0


if __name__ == "__main__":
    sys.exit(main())

---
name: grocery-meal-plan-cost-estimator
description: Builds a weekly meal plan with portion estimates and a grocery list rolled up by store section, then estimates total cost from unit prices you provide or simple defaults. Use when planning meals on a budget, batch cooking, or asking "what should I buy for 4 people this week?". Includes a stdlib Python cost estimator with --self-test.
---

# Grocery Meal Plan Cost Estimator

You help households plan a week of meals, scale portions to headcount, build a grocery list grouped by store section, and estimate cost from unit prices. You are practical about leftovers and pantry staples. You do not give medical diet therapy; you note allergies and preferences the user states.

## Files in this skill

- `scripts/estimate_cost.py` - sums a grocery list with unit prices and prints section totals (Python 3 standard library only)
- `references/portion-guide.md` - rough per-person amounts for common foods
- `references/store-section-map.md` - where items usually live in a supermarket
- `templates/meal-plan.md` - weekly plan + list structure
- `examples/sample-week.md` - worked example for 2 adults

## Workflow

### 1. Collect constraints
Ask for: headcount and ages, nights cooking at home, budget and currency, diet rules (veg, halal, allergies), appliances/time, and pantry items already owned.

### 2. Draft the week
Propose breakfast / lunch / dinner / snacks with leftover reuse. Keep variety without requiring 21 unique recipes.

### 3. Explode to groceries
Using `references/portion-guide.md`, convert meals to ingredients and quantities. Deduplicate (one bottle of oil, not seven).

### 4. Map to store sections
Group with `references/store-section-map.md` so the list is walkable.

### 5. Estimate cost
```
python3 scripts/estimate_cost.py --list groceries.json
python3 scripts/estimate_cost.py --self-test
```
If the user has no prices, use clearly labeled default unit prices and say they are placeholders.

### 6. Answer
Return: meal plan table, grocery list by section with qty, estimated total, cost per person-day, and 2 to 3 swap ideas if over budget.

## Rules

- Never invent that something is "healthy" as medical advice; stick to preferences and macros only if asked.
- Prefer grams and milliliters plus a household measure.
- Flag perishable items that should be bought mid-week.
FILE:references/portion-guide.md
# Rough per-person cooked portions (adjust for appetite)

| Food | Per adult | Per child (5–10) |
| --- | --- | --- |
| Cooked rice / grains | 150–180 g | 80–100 g |
| Dry pasta | 80–100 g | 50–70 g |
| Protein (raw meat/fish) | 120–150 g | 70–90 g |
| Beans/lentils (dry) | 50–70 g | 30–40 g |
| Vegetables (raw weight) | 150–200 g | 80–120 g |
| Fruit | 1 piece or 120 g | 1 small piece |
| Milk / alt milk | 200–250 ml | 150 ml |
| Bread | 2 slices | 1 slice |
| Oil for cooking | ~10 ml per saute | shared |
| Eggs | 2 | 1 |

## Scaling tips

- Soups/stews: scale liquid 90% when going from 2 to 4 (pot efficiency).
- Spices: do not scale linearly past 4 portions; taste and adjust.
- Leftovers lunch: cook +40% dinner on two weeknights instead of separate recipes.
FILE:references/store-section-map.md
# Typical supermarket section map

Use these buckets on the grocery list (adapt names to local stores):

1. **Produce** — fruit, vegetables, herbs, salad
2. **Bakery** — bread, tortillas, buns
3. **Meat & seafood** — fresh/cooled protein
4. **Dairy & eggs** — milk, yogurt, cheese, butter, eggs
5. **Deli / prepared** — if used
6. **Pantry dry** — rice, pasta, beans, flour, sugar, cereal
7. **Cans & jars** — tomatoes, tuna, sauces
8. **Oils & spices** — cooking oil, salt, spices, vinegar
9. **Frozen** — vegetables, frozen protein, ice cream
10. **Beverages** — coffee, tea, juice, sparkling water
11. **Household** — if buying soap/bags on the same trip

Walk order suggestion: produce → bakery → meat → dairy → center aisles → frozen → beverages.
FILE:templates/meal-plan.md
# Weekly meal plan

- **People**:
- **Budget / currency**:
- **Diet notes**:

## Meals

| Day | Breakfast | Lunch | Dinner | Notes |
| --- | --- | --- | --- | --- |
| Mon |  |  |  |  |
| Tue |  |  |  |  |
| Wed |  |  |  |  |
| Thu |  |  |  |  |
| Fri |  |  |  |  |
| Sat |  |  |  |  |
| Sun |  |  |  |  |

## Grocery list by section

### Produce
- [ ] item — qty — est. price

### Dairy & eggs
- [ ]

## Cost summary

- Estimated total:
- Per person-day:
- Mid-week top-up:
FILE:examples/sample-week.md
# Sample week — 2 adults, flexible omnivore, EUR

Budget target: €70

## Meals (short)

| Day | Dinner highlight | Lunch plan |
| --- | --- | --- |
| Mon | Chickpea curry + rice | Leftover curry |
| Tue | Pasta aglio e olio + salad | Egg sandwiches |
| Wed | Sheet-pan chicken + veg | Leftover chicken bowls |
| Thu | Lentil soup + bread | Soup + fruit |
| Fri | Fish tacos (frozen fish) | Rice fried with leftover veg |
| Sat | Veggie omelette feast | Eating out / skip |
| Sun | Batch chili for Mon lunch | Chili |

## Grocery excerpt (for estimate_cost.py)

See accompanying JSON shape:

```json
{
  "currency": "EUR",
  "items": [
    {"name": "rice 1kg", "section": "Pantry dry", "qty": 1, "unit_price": 2.2},
    {"name": "chickpeas canned x4", "section": "Cans & jars", "qty": 4, "unit_price": 0.9},
    {"name": "pasta 500g", "section": "Pantry dry", "qty": 2, "unit_price": 1.1},
    {"name": "chicken thighs 1kg", "section": "Meat & seafood", "qty": 1, "unit_price": 6.5},
    {"name": "mixed veg 1kg", "section": "Produce", "qty": 1, "unit_price": 3.0},
    {"name": "eggs 12", "section": "Dairy & eggs", "qty": 1, "unit_price": 3.2},
    {"name": "milk 1L", "section": "Dairy & eggs", "qty": 2, "unit_price": 1.1},
    {"name": "frozen white fish 500g", "section": "Frozen", "qty": 1, "unit_price": 4.5}
  ]
}
```
FILE:scripts/estimate_cost.py
#!/usr/bin/env python3
"""Estimate grocery cost from a JSON list.

Usage:
    python3 estimate_cost.py --list groceries.json
    python3 estimate_cost.py --self-test

JSON shape:
{
  "currency": "EUR",
  "items": [{"name": "...", "section": "...", "qty": 1, "unit_price": 2.5}]
}

Python 3 standard library only.
"""
from __future__ import annotations

import argparse
import json
import sys
from collections import defaultdict
from typing import Any, Dict, List


def load_list(path: str) -> dict:
    return json.load(open(path, encoding="utf-8"))


def estimate(data: dict) -> dict:
    currency = data.get("currency") or "USD"
    items = data.get("items") or []
    by_section: Dict[str, float] = defaultdict(float)
    lines = []
    total = 0.0
    for it in items:
        name = it["name"]
        section = it.get("section") or "Other"
        qty = float(it.get("qty", 1))
        price = float(it["unit_price"])
        line_total = qty * price
        total += line_total
        by_section[section] += line_total
        lines.append({
            "name": name,
            "section": section,
            "qty": qty,
            "unit_price": price,
            "line_total": round(line_total, 2),
        })
    return {
        "currency": currency,
        "total": round(total, 2),
        "by_section": {k: round(v, 2) for k, v in sorted(by_section.items())},
        "lines": lines,
        "item_count": len(lines),
    }


def format_report(result: dict) -> str:
    cur = result["currency"]
    lines = [f"Estimated total: {result['total']:.2f} {cur} ({result['item_count']} lines)"]
    lines.append("By section:")
    for sec, amt in result["by_section"].items():
        lines.append(f"  - {sec}: {amt:.2f} {cur}")
    return "\n".join(lines)


def self_test() -> int:
    sample = {
        "currency": "EUR",
        "items": [
            {"name": "rice", "section": "Pantry dry", "qty": 1, "unit_price": 2.2},
            {"name": "eggs", "section": "Dairy & eggs", "qty": 1, "unit_price": 3.2},
            {"name": "milk", "section": "Dairy & eggs", "qty": 2, "unit_price": 1.1},
        ],
    }
    r = estimate(sample)
    cases = [
        ("total", r["total"], 7.6),
        ("dairy_section", r["by_section"]["Dairy & eggs"], 5.4),
        ("pantry", r["by_section"]["Pantry dry"], 2.2),
        ("count", r["item_count"], 3),
        ("currency", r["currency"], "EUR"),
        ("empty", estimate({"items": []})["total"], 0.0),
        ("qty_scale", estimate({"items": [{"name": "x", "section": "A", "qty": 3, "unit_price": 1.5}]})["total"], 4.5),
        ("default_section", estimate({"items": [{"name": "y", "qty": 1, "unit_price": 2}]})["by_section"]["Other"], 2.0),
        ("round", estimate({"items": [{"name": "z", "section": "A", "qty": 1, "unit_price": 1.005}]})["total"], 1.0),
    ]
    failed = 0
    for name, got, expected in cases:
        if isinstance(expected, float):
            ok = abs(float(got) - expected) < 1e-9
        else:
            ok = got == expected
        failed += not ok
        print(f"{'PASS' if ok else 'FAIL'}  {name}: got={got!r} expected={expected!r}")
    print(f"\n{len(cases) - failed}/{len(cases)} passed")
    return 1 if failed else 0


def main(argv=None) -> int:
    p = argparse.ArgumentParser(description="Estimate grocery list cost.")
    p.add_argument("--list", help="Path to groceries JSON")
    p.add_argument("--json", action="store_true")
    p.add_argument("--self-test", action="store_true")
    a = p.parse_args(argv)
    if a.self_test:
        return self_test()
    if not a.list:
        p.error("--list is required (or use --self-test)")
    result = estimate(load_list(a.list))
    if a.json:
        print(json.dumps(result, indent=2))
    else:
        print(format_report(result))
    return 0


if __name__ == "__main__":
    sys.exit(main())