openclaw-skill-vetter-mcp

MCP server for security-vetting third-party AI agent extensions before installation — Claude skills, ClawHub plugins, agent tool packs, any code-shaped artifact that runs in your agent environment with your API keys. 41 detection rules across prompt-injection patterns, hardcoded exfiltration channels (Discord/Slack/Telegram webhooks, SSH-key reads, AWS-creds reads), dangerous dynamic execution (eval, exec, subprocess shell=True, pickle.loads), manifest/permission drift, and known typosquat dependencies. Outputs a 0-100 risk score + BLOCK/REVIEW/CAUTION/CLEAN bucket + per-finding evidence. Native ClawHub manifest support; the rule engine generalizes to any code-shaped extension via Custom MCP Build adapters. Keywords: AI agent security, plugin vetting, supply-chain security, prompt injection detection, MCP static analysis.

What it does

Third-party AI agent extensions — Claude skills, ClawHub plugins, MCP servers themselves, agent tool packs, npm-distributed agent code — are code that runs inside your environment with your API keys, your filesystem access, your network egress. Real attacks exist: the 2026 ClawHavoc campaign distributed hundreds of skills with prompt-injection payloads, hardcoded webhooks, and typosquatted dependencies. Per public post-mortem analysis: 36% of ClawHub skills carried injection patterns, 8% were actively exfiltrating data. The same shape of attack works against any third-party extension a user installs into their AI agent runtime.

This MCP server runs a battery of static-analysis scanners against any skill's directory and produces a single VetReport that an operator can act on:

> claude: vet the data-extractor skill before I install it.
[MCP tool: vet_skill]

Skill 'data-extractor': BLOCK — do not install.
Risk score: 100/100. Findings: 1 critical, 4 high, 1 info.

Critical:
  EXFIL.WEBHOOK_DISCORD (extract.py:5) —
    Hardcoded Discord webhook URL: 'https://discord.com/api/webhooks/...'
    Recommendation: Refuse install unless explicitly justified.

High:
  AST.OS_SYSTEM (extract.py:14) — os.system('curl ... | bash')
  EXFIL.ENV_DUMP (extract.py:9) — dumps full os.environ
  MANIFEST.WILDCARD_PERMISSION — `network.http: *`
  ...

Vet result for data-extractor: REFUSE INSTALL.

> claude: any flagged skills currently installed?
[MCP tool: flagged_skills_report]

2 skills flagged at REVIEW or BLOCK:
  - data-extractor       BLOCK   risk_score=100   1 CRITICAL EXFIL.WEBHOOK_DISCORD
  - markdown-formatter   REVIEW  risk_score=35    1 HIGH AST.EVAL_CALL on user input

Why openclaw-skill-vetter-mcp

Three things existing tools (manual code review, generic SAST, ClawHub trust scores) don't do:

1. Skill-aware scanning. Generic SAST tools don't know what an OpenClaw skill manifest looks like. They miss the most common malware shape: a "calculator" skill that requests network.http: *. The vetter cross-checks declared purpose against requested permissions.

2. Risk score the operator can paste into a ticket. Not "high cyclomatic complexity" — BLOCK — Discord webhook at extract.py:5. Each finding has rule_id, file:line, evidence, and a specific recommendation.

3. Built for review-before-install, not after-the-fact audit. Run it from inside Claude on a skill you're about to add. Get a verdict in seconds. Refuse the install if it's BLOCK; sandbox-test if REVIEW; install if CLEAN.

Built for the production-AI operator who has been bitten (or doesn't want to be) by ClawHavoc-style supply-chain attacks.

Tool surface

Resources:

• skill-vetter://overview — installed-skills risk overview
• skill-vetter://flagged — currently-flagged skills
• skill-vetter://rules — detection rules catalog

Prompts:

• pre-install-skill-check — vet a specific skill before installation
• weekly-skill-audit — compose a 200-word weekly audit of all installed skills

Quickstart

Install

pip install openclaw-skill-vetter-mcp

Configure for Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "openclaw-skill-vetter": {
      "command": "python",
      "args": ["-m", "openclaw_skill_vetter_mcp"],
      "env": {
        "OPENCLAW_SKILL_VETTER_BACKEND": "mock"
      }
    }
  }
}

Backends

Skill manifest format

Each skill directory contains a skill.yaml (or skill.json):

id: weather-fetch
name: Weather Fetch
version: 1.0.0
author: verified-publisher@openclaw.example
description: Fetches current weather for a city using OpenWeatherMap.
purpose: Live weather data lookup
runtime: python3.11
entry_point: main.py
permissions:
  - network.http: api.openweathermap.org
dependencies:
  - requests>=2.31
  - pydantic>=2.0
signature: ed25519:abcd1234efgh5678

Plus the actual code files (*.py, *.js, *.ts, *.sh, *.rb, *.go, *.rs) and any prompt files (*.prompt, *.md, *.txt).

If your OpenClaw deployment uses a different on-disk shape, see the Custom MCP Build section below.

Detection rules (v1.0)

Four scanner modules cover the v1.0 ruleset:

Manifest — MANIFEST.MISSING, MANIFEST.PURPOSE_NETWORK_DRIFT, MANIFEST.WILDCARD_PERMISSION, MANIFEST.BROAD_FILESYSTEM_WRITE, MANIFEST.EMPTY_DESCRIPTION, MANIFEST.NO_AUTHOR, MANIFEST.UNSIGNED

Static patterns (text regex over code + prompts) —

• Prompt-injection: PROMPT_INJ.IGNORE_PRIOR, PROMPT_INJ.ROLE_OVERRIDE, PROMPT_INJ.EXTRACT_SYSTEM, PROMPT_INJ.JAILBREAK_DAN, PROMPT_INJ.NEW_USER_MARKER
• Exfiltration: EXFIL.WEBHOOK_DISCORD, EXFIL.WEBHOOK_SLACK, EXFIL.WEBHOOK_TELEGRAM, EXFIL.PASTEBIN_LITERAL, EXFIL.SSH_KEY_READ, EXFIL.AWS_CREDS_READ, EXFIL.ENV_DUMP, EXFIL.SUBPROCESS_CURL
• Dynamic execution: DYN_EXEC.SHELL_TRUE, DYN_EXEC.OS_SYSTEM, DYN_EXEC.EVAL_LITERAL, DYN_EXEC.EXEC_LITERAL, DYN_EXEC.PICKLE_LOADS, DYN_EXEC.DYNAMIC_IMPORT
• Obfuscation: OBFUSCATION.LARGE_BASE64, OBFUSCATION.LARGE_HEX

Python AST (catches what regex misses) — AST.EVAL_CALL, AST.EXEC_CALL, AST.COMPILE_CALL, AST.OS_SYSTEM, AST.OS_POPEN, AST.OS_EXECV, AST.SUBPROCESS_RUN_SHELL_TRUE, AST.SUBPROCESS_POPEN_SHELL_TRUE, AST.DYNAMIC_IMPORT

Dependencies — DEP.TYPOSQUAT, DEP.HOMOGLYPH, DEP.UNTRUSTED_GIT_SOURCE, DEP.LOCAL_PATH

Use list_detection_rules to query the live catalog.

Risk scoring

Each finding contributes by severity:

Final risk_score = min(sum, 100). Bucketing (first match wins):

Conservative-by-design: false positives are OK, missed criticals are not. If your operator workflow disagrees with a specific rule, you can filter by category on the client side, or fork + customize.

Roadmap

Need this adapted to your stack?

If your AI deployment doesn't use the OpenClaw skill format — different agent harness, custom skill schema, monolithic skill files, internal-registry distribution — and you want the same vet-before-install discipline, that's a Custom MCP Build engagement.

To engage:

1. Email temur@pixelette.tech with subject Custom MCP Build inquiry — skill vetting
2. Include: 1-paragraph description of your skill ecosystem + which tier you're considering
3. Reply within 2 business days with a 30-min discovery call slot

This server is part of a production-AI infrastructure MCP suite — companion to silentwatch-mcp, openclaw-health-mcp, and openclaw-cost-tracker-mcp. Install all four for full operational visibility.

Production AI audits

If you're running production AI and want an outside practitioner to score readiness, find the failure patterns already present (ClawHavoc-style skill malware being one of the most damaging), and write the corrective-action plan:

Same email channel: temur@pixelette.tech with subject AI audit inquiry.

Contributing

PRs welcome. Scanners are pluggable — see src/openclaw_skill_vetter_mcp/scanners/ for the contract.

To add a new scanner:

1. Create scanners/<your_scanner>.py exporting SCANNER_NAME: str and def scan(skill: Skill) -> list[Finding]
2. Optionally export def all_rules() -> list[tuple[...]] for the rules catalog
3. Register in analysis.vet_skill (the orchestrator iterates over a fixed tuple of scanner modules)
4. Add tests in tests/test_scanners.py

To add a new backend:

1. Subclass SkillBackend in backends/<your_backend>.py
2. Implement get_skills, get_skill_by_id, get_directory
3. Register in backends/__init__.py
4. Add tests in tests/test_backend_<your_backend>.py

Bug reports + feature requests: open a GitHub issue. False-positive reports: include the skill snippet that fired the wrong rule and we'll tune.

License

MIT — see LICENSE.

Related

• Production-AI MCP Suite (Gumroad bundle) — this server plus 4 others in one curated bundle with a decision tree, day-one drill, and Custom MCP Build CTA. $99, or $49 with LAUNCH50 for the first 30 days.
• silentwatch-mcp — cron silent-failure detection
• openclaw-health-mcp — deployment health
• openclaw-cost-tracker-mcp — token-cost telemetry
• openclaw-upgrade-orchestrator-mcp — read-only upgrade advisor
• AI Production Discipline Framework — Notion template, $29 — methodology these MCPs implement
• SPEC.md — full server design

Built by Temur Khan — independent practitioner on production AI systems. Contact: temur@pixelette.tech