name: clawhub-skill-lint description: > Use this skill before publishing any ClawHub skill from this repo, or after editing a SKILL.md, to verify the skill won't be flagged or rejected by ClawHub's server-side moderation pipeline. The skill re-implements every static-scanner rule from openclaw/clawhub's convex/lib/moderationEngine.ts plus the frontmatter spec from docs/skill-format.md and runs them locally. Triggers: "lint the claws skills", "check the claws/openclaw skills", "scan before publish", "is the skill clean", "any scanner findings", /clawhub-skill-lint, before any clawhub sync or clawhub skill publish.

clawhub-skill-lint

A pre-publish validator for ClawHub skills. Catches publish-time failures locally — no network round-trip, no clawhub CLI required.

When to use

• Before running clawhub sync or clawhub skill publish.
• After editing any SKILL.md, README.md, or other text file in a ClawHub skill folder, to confirm no new patterns trigger the scanner.
• In CI as a pre-publish gate.
• Whenever the user asks "is this skill clean", "will this pass review", or "scan before I publish".

How to invoke

The skill bundles a Python linter at .claude/skills/clawhub-skill-lint/scripts/lint.py. Call it with one or more paths:

# Lint a single skill folder
python3 .claude/skills/clawhub-skill-lint/scripts/lint.py claws/openclaw/sageox-distill

# Lint every skill under a parent directory
python3 .claude/skills/clawhub-skill-lint/scripts/lint.py claws/openclaw

# Multiple paths in one invocation
python3 .claude/skills/clawhub-skill-lint/scripts/lint.py claws/openclaw/sageox-distill claws/openclaw/sageox-summary

# Machine-readable JSON output (for CI)
python3 .claude/skills/clawhub-skill-lint/scripts/lint.py --json claws/openclaw

# Treat warnings as errors
python3 .claude/skills/clawhub-skill-lint/scripts/lint.py --strict claws/openclaw

The linter discovers skill folders by looking for SKILL.md (or skill.md). A path that contains SKILL.md directly is treated as a single skill; any other directory is scanned for child folders.

Exit codes:

How to interpret results

Each finding has:

• rule_id — short stable identifier (e.g. static.malicious_install_prompt, frontmatter.missing_required_field)
• severity — critical, warn, or info
• file — path inside the skill folder (or SKILL.md / metadata for frontmatter findings)
• line — 1-indexed line number where the rule first matched
• message — human-readable explanation
• evidence — truncated snippet of the matching text

Critical findings will hard-block publish. Examples: malicious install prompt, frontmatter missing required field, slug regex fail, install kind not in {brew, node, go, uv}, bundle size > 50 MB.

Warnings are advisory but worth fixing — they signal patterns the scanner may flag as suspicious (which doesn't block but adds a warning banner on the skill page). Examples: always: true in frontmatter, URL shortener references, prompt-injection bait phrases.

Info findings are observations that don't affect publishability.

Workflow when findings are reported

1. Read each finding out loud to the user, grouped by severity.
2. For each critical finding:
   • Show the file:line and the offending evidence.
   • Propose a concrete fix (e.g., "rephrase 'for macOS:' to 'macOS:' to avoid triggering the terminal-instruction precondition" or "add version: 0.1.0 to the frontmatter").
   • Apply the fix only after the user confirms.
3. Re-run the linter after fixes to confirm clean.
4. Only proceed to publish when the linter exits 0.

Source-of-truth references

The linter is kept in sync with these upstream files. If ClawHub updates its rules, update scripts/lint.py to match:

• openclaw/clawhub docs/skill-format.md — frontmatter schema, slug regex, bundle limits, install kinds
• openclaw/clawhub convex/lib/moderationEngine.ts — static scanner rules (the canonical source of truth for hasMaliciousInstallPrompt and friends)
• openclaw/clawhub docs/security.md — moderation pipeline overview

What this linter does NOT cover

• VirusTotal hash lookup — server-side, runs against a live SHA-256 DB. Not reproducible locally.
• VT Code Insight (Gemini LLM scan) — opaque, server-side. The only way to get the LLM verdict is to publish under a throwaway slug and query /api/v1/skills/<slug>/scan. See claws/openclaw/PUBLISHING.md § "Throwaway-slug pre-flight" for the workflow.
• Behavioral correctness of the skill — the linter only looks at patterns. Whether the skill actually does what it says is a separate test (load it into a real OpenClaw via skills.load.extraDirs).

A clean lint result is necessary but not sufficient for a successful publish. Always also do at least one throwaway-slug publish before claiming the canonical slug.