skill-comply تایید شده

Visualize whether skills, rules, and agent definitions are actually followed — auto-generates scenarios at 3 prompt strictness levels, runs agents, classifies behavioral sequences, and reports compliance rates with full tool call timelines

80از ۱۰۰
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// نصب مهارت

نصب مهارت

مهارت‌ها کدهای شخص ثالث از مخازن عمومی GitHub هستند. SkillHub الگوهای مخرب شناخته‌شده را اسکن می‌کند اما نمی‌تواند امنیت را تضمین کند. قبل از نصب، کد منبع را بررسی کنید.

نصب سراسری (سطح کاربر):

npx skillhub install affaan-m/everything-claude-code/skill-comply

نصب در پروژه فعلی:

npx skillhub install affaan-m/everything-claude-code/skill-comply --project

مسیر پیشنهادی: ~/.claude/skills/skill-comply/

بررسی هوش مصنوعی

80
از ۱۰۰
کیفیت دستورالعمل80
دقت توضیحات80
کاربردی بودن81
صحت فنی75

Exceptional meta-skill — the only automated compliance testing tool for Claude Code skills reviewed so far. Prompt independence measurement (testing without explicit trigger support) is a novel concept. LLM-based classification is more reliable than regex.

betacomplexClaude Code developers and tool authors who ship skills and want to verify they're followed.Verifying skill adoption after deployment; regression testing rules; measuring agent definition compliance; periodic quality audits.
بررسی‌شده توسط claude-code در تاریخ ۱۴۰۵/۱/۲۴

محتوای SKILL.md

---
name: skill-comply
description: Visualize whether skills, rules, and agent definitions are actually followed — auto-generates scenarios at 3 prompt strictness levels, runs agents, classifies behavioral sequences, and reports compliance rates with full tool call timelines
origin: ECC
tools: Read, Bash
---

# skill-comply: Automated Compliance Measurement

Measures whether coding agents actually follow skills, rules, or agent definitions by:
1. Auto-generating expected behavioral sequences (specs) from any .md file
2. Auto-generating scenarios with decreasing prompt strictness (supportive → neutral → competing)
3. Running `claude -p` and capturing tool call traces via stream-json
4. Classifying tool calls against spec steps using LLM (not regex)
5. Checking temporal ordering deterministically
6. Generating self-contained reports with spec, prompts, and timelines

## Supported Targets

- **Skills** (`skills/*/SKILL.md`): Workflow skills like search-first, TDD guides
- **Rules** (`rules/common/*.md`): Mandatory rules like testing.md, security.md, git-workflow.md
- **Agent definitions** (`agents/*.md`): Whether an agent gets invoked when expected (internal workflow verification not yet supported)

## When to Activate

- User runs `/skill-comply <path>`
- User asks "is this rule actually being followed?"
- After adding new rules/skills, to verify agent compliance
- Periodically as part of quality maintenance

## Usage

```bash
# Full run
uv run python -m scripts.run ~/.claude/rules/common/testing.md

# Dry run (no cost, spec + scenarios only)
uv run python -m scripts.run --dry-run ~/.claude/skills/search-first/SKILL.md

# Custom models
uv run python -m scripts.run --gen-model haiku --model sonnet <path>
```

## Key Concept: Prompt Independence

Measures whether a skill/rule is followed even when the prompt doesn't explicitly support it.

## Report Contents

Reports are self-contained and include:
1. Expected behavioral sequence (auto-generated spec)
2. Scenario prompts (what was asked at each strictness level)
3. Compliance scores per scenario
4. Tool call timelines with LLM classification labels

### Advanced (optional)

For users familiar with hooks, reports also include hook promotion recommendations for steps with low compliance. This is informational — the main value is the compliance visibility itself.