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* fix(skills): move version into metadata and normalize to semver 29 skills declared `version` at the top level of their frontmatter. The schema reads it from `metadata`, so tooling that follows the schema either misses it or has to special-case the top level. Three motion skills also declared `version: 1.0`, which is not a valid semantic version; normalized to `1.0.0`. No behavioral change — frontmatter metadata only. * fix(skills): state activation triggers in skill descriptions 148 skills described what they cover but never named the situation that should trigger them. Since the description is what Claude matches against to decide whether to load a skill, a description without a trigger makes activation guesswork — the skill is either missed or loaded at the wrong time. Added a "Use when ..." clause to each, derived from the skill's own body (most already stated the trigger under "## When to Use" or in the opening line; that intent is now reflected in the frontmatter where it is actually read from). Descriptions were only appended to; no existing wording was removed. * fix(skills): sync activation triggers into the Codex skill mirror 10 of the skills whose descriptions changed are also mirrored under `.agents/skills/`, where the description was previously a verbatim copy. Left alone, the two surfaces would disagree about when the skill applies. Only the description line is synced; the Codex copies keep their reduced frontmatter, since that validator accepts only name, description, metadata, license, and allowed-tools. * fix(skills): correct three activation clauses from review - autonomous-loops: the clause pulled new loop work into a skill that its own body marks as a compatibility shim retained for one release. It now points at the canonical continuous-agent-loop instead. - continuous-learning: the description carried the v1 routing directive twice; collapsed to one. - homelab-pihole-dns: the clause fired on any broken home DNS. Narrowed to tasks that actually involve Pi-hole. * chore: retain current main lockfile --------- Co-authored-by: Çağrı Solakoğlu <cagri.solakoglu@vtcenerji.com> Co-authored-by: haelyra <49814733+haelyra@users.noreply.github.com>
2.4 KiB
2.4 KiB
name, description, metadata, tools
| name | description | metadata | tools | ||
|---|---|---|---|---|---|
| skill-comply | 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. Use when checking whether agents actually follow the skills, rules, and definitions they were given, rather than assuming they do. |
|
Read, Bash |
skill-comply: Automated Compliance Measurement
Measures whether coding agents actually follow skills, rules, or agent definitions by:
- Auto-generating expected behavioral sequences (specs) from any .md file
- Auto-generating scenarios with decreasing prompt strictness (supportive → neutral → competing)
- Running
claude -pand capturing tool call traces via stream-json - Classifying tool calls against spec steps using LLM (not regex)
- Checking temporal ordering deterministically
- 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
# 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:
- Expected behavioral sequence (auto-generated spec)
- Scenario prompts (what was asked at each strictness level)
- Compliance scores per scenario
- 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.