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Skill Release Audit

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Pre-publish quality and safety auditor for AI agent skills (SKILL.md + scripts/ + references/ format used by Claude Code, Cursor, OpenAI Codex, GitHub Copilo...

What it does

Pre-publish quality and safety auditor for AI agent skills (SKILL.md + scripts/ + references/ format used by Claude Code, Cursor, OpenAI Codex, GitHub Copilot, OpenClaw, ClawHub, and compatible SkillHub registries). Six static-check modules (no LLM, no network by default): (1) syntax and logic correctness, (2) feature completeness, (3) edge-case and error handling, (4) data safety (detects files written inside the skill dir that would be lost on update), (5) dependency declaration vs code, (6) SKILL.md documentation standards. Per-registry rule profiles via `--target`. Pure reporter — never edits your files, never publishes. Use when publishing a skill, modifying an existing skill, or diagnosing why a skill behaves unexpectedly — run it as the last machine-checkable gate before release. Trigger phrases: "skill release audit", "audit before publishing", "pre-release check", "release gate", "skill safety check", "发版前检查", "skill 发布检查", "审查这个 skill 能不能发版".

The skill document

skill-release-audit

A six-module static inspector that catches mechanical problems machines can verify — broken Python syntax, undeclared env vars, files written inside the skill dir that would be lost on update, missing README/LICENSE for GitHub targets, mismatched docs and scripts. Pairs with glic-check which does cognitive UGLIC review; together they cover both layers (see Part of build-better-skills).

Prints a structured ✅/⚠️/❌ report per module with actionable fixes. Does not auto-fix. All fixes require user confirmation.


Agent Behavior Rules (must-read)

This skill is an Auditor, not a Fixer.

After running the check, the agent must follow these rules strictly:

  1. Show the full report first — every module result, every warning / error with its specific description.
  2. Suggest fixes per finding, but only as suggestions, not actions.
  3. Wait for explicit user confirmation before touching any file.
  4. Never modify SKILL.md, scripts/, references/, or any other file without confirmation.
  5. Never delete files, even ones that look like leftovers — always ask first.

Correct output template:

📋 Check report: 
[full output of healthcheck.py]

---
Issues found. Please confirm what to do:

✅ Can be ignored (false positives):
- Module 1: cache.json path warning — SKILL.md mentions it as descriptive text,
  not a file reference.

⚠️ Suggested fixes (awaiting your confirmation):
1. Module 3: auto_update.py _log() missing try/except — suggest wrapping.
2. Module 2: guide.py not listed in SKILL.md's module table — suggest adding.

Tell me which to fix and which to ignore. I'll act after you confirm.

Usage

# Full check — report only, does NOT modify your environment (default)
python scripts/healthcheck.py 

# Opt in to auto pip-install missing Python deps (off by default)
python scripts/healthcheck.py  --auto-install

# Tune per-package install timeout (only with --auto-install)
python scripts/healthcheck.py  --auto-install --install-timeout 120

# Validate against a specific publishing target (tunes checks, does NOT publish)
python scripts/healthcheck.py  --target clawhub

# Report output language: zh or en (default: auto-detected from $LC_ALL / $LANG)
python scripts/healthcheck.py  --lang en

# Run specific modules only (e.g. deps + docs)
python scripts/healthcheck.py  --modules 5,6

Default behavior is report only, do not install — an auditor should not mutate the user's environment. Missing required deps are reported as ERROR; with --auto-install, install failures are reported as WARN (usually network/registry issues, not skill bugs).

Publishing target (--target)

Different registries have different rules. --target tunes which checks fire and at what severity — it does not publish.

targetUse caseNotes
generic (default)Cross-registry minimumLoosest, smallest common set
clawhubclawhub.comname+description required, strict slug, 50MB cap, declaration-vs-code consistency
anthropicAnthropic-compatible base specname≤64 / description≤1024, body recommended <5k tokens
githubOpen-sourcing to GitHubRequires README + LICENSE
skillhubPrivate SkillHub (vendor-compatibility layers)Same as clawhub but version required (WARN)

Profiles live in profiles/ as JSON; add your own by dropping a new .json there. See references/hub-specs.md for per-registry specifications encoded by these profiles.

Report language

Default: auto-detected from $LC_ALL / $LC_MESSAGES / $LANG. Falls back to zh (legacy default). Currently supported: zh / en.

  • CLI: --lang zh / --lang en
  • Env: SKILL_AUDIT_LANG=en (lower priority than --lang)

All user-facing text is centralized in scripts/i18n.py as a bilingual key-table — add a new check by adding one zh/en entry; do not hardcode strings in module files.

Exit codes: 0 = no errors (may have warnings), 1 = errors found, 2 = invalid input.

The six modules

ModuleChecksScript
1. Syntax & logicPython AST parse, Bash -n syntax check, internal reference paths, leftover TODO/FIXMEscripts/check_logic.py
2. Feature coveragescripts / references mentioned in SKILL.md, stub-file detectionscripts/check_features.py
3. Edge casestry/except coverage, HTTP timeout, response-status handling, Bash set -escripts/check_edges.py
4. Data safetyFiles written inside the skill dir (lost on update)scripts/check_data_safety.py
5. DependenciesPython / Node / Bash deps, optional --auto-install, declaration-vs-code env checkscripts/check_deps.py
6. Documentationdescription quality, frontmatter discipline, slug rules, target-specific required files (README / LICENSE)scripts/check_docs.py

Data safety hint

Skill directories are overwritten on update. All persistent data must live outside the skill dir. The auditor probes the runtime environment (OpenClaw / Claude Code / Codex / plain checkout) and suggests a portable path:

from pathlib import Path
DATA_DIR = Path.home() / ".skill-data" / ""   # example, auto-tuned

See references/safe-paths.md for the full resolution order.

Declaration vs code env check

Mirrors registry-side security analysis: if your code reads an env var (os.environ["X"], os.getenv(...), process.env.X) that isn't declared in frontmatter under metadata.openclaw.requires.env / primaryEnv / envVars, the auditor flags a metadata mismatch (severity is profile-driven). This catches the most common cause of post-publish runtime failures.

Dependency declaration convention

In SKILL.md body, add a dependency section:

## Dependencies

Auto-installed on first use: requests, pyyaml

System commands (install manually): jq (`brew install jq`)

See references/dep-patterns.md.

Dependencies

Core uses Python 3.8+ standard library only — no external installs needed.

Optional enhancement: pyyaml (auto-detected). With PyYAML, frontmatter parsing handles multi-line / list / nested fields strictly. Without it, a tolerant built-in parser handles inline JSON values (e.g. metadata: {"key": "value"}) so the same checks still work — no functionality loss.

Part of build-better-skills

This skill belongs to the build-better-skills suite. For the full lifecycle map (Install → Audit → Release → Testing → Sediment), all sibling skills, and their current status, see the Stages table on the suite repo home — kept as the single source of truth (this file does not duplicate it).

Related skills

Generic skill-quality auditor for any agent skill (Claude, OpenClaw, Cursor, etc.). Runs a 7-dimension static analysis (D1 process closure & idempotency, D2 tool/command conventions, D3 portability & defense, D4 skill usability, D5 security & op risk, D6 code & doc quality, D7 dependency & footprint) with explicit ERR / WARN severity, 120-point scoring (pass line 90 + zero ERR), and an opt-in `--fix` workflow that always backs up first. Two depths: L1 static (~2 min) and L2 dryRun (~5 min, read-only hub + reachability checks). Strict red lines — read-only by default, never executes the audited skill's writes. Use when the user asks to "audit a skill", "check skill quality", "is this skill ready to ship", "lint my skill", or runs this tool by name. Triggers also: "审计这个 Skill"、"检查 Skill 质量"、"Skill 能上线吗"、 "skill-deep-audit"、"审一下 xxx skill"。

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