Skill-Scan — Security Auditor for Agent Skills Multi-layered security scanner for OpenClaw skill packages. Detects malicious code, evasion techniques, prompt injection, and misaligned behavior through static analysis and optional LLM-powered deep inspection. Run this BEFORE installing or enabling any untrusted skill. Features 6 analysis layers — pattern matching, AST/evasion, prompt injection, LLM deep analysis, alignment verification, meta-analysis 60+ detection rules — execution threats, credential theft, data exfiltration, obfuscation, behavioral signatures Context-aware scoring — reduces false positives for legitimate API skills ClawHub integration — scan skills directly from the registry by slug Multiple output modes — text report (default), --json , --compact , --quiet Exit codes — 0 for safe, 1 for risky (easy scripting integration) When to Use MANDATORY before installing or enabling: Skills from ClawHub (any skill not authored by you) Skills shared by other users or teams Skills from public repositories Any skill package you haven't personally reviewed RECOMMENDED for periodic audits of already-installed skills. Quick Start

Scan a local skill directory

skill-scan scan /path/to/skill

Scan a skill from ClawHub before installing it

skill-scan scan-hub some-skill-slug

Batch scan all installed skills

skill-scan batch /path/to/skills-directory

JSON output for programmatic use

skill-scan scan-hub some-skill-slug --json

Quiet mode (just score + verdict)

skill-scan scan-hub some-skill-slug --quiet Risk Scoring Risk Score Action LOW 80-100 Safe to install MEDIUM 50-79 Review findings before installing HIGH 20-49 Do NOT install — serious threats detected CRITICAL 0-19 Do NOT install — multiple critical threats Exit Codes 0 — LOW risk (safe to proceed) 1 — MEDIUM or higher (block installation) LLM Options skill-scan scan-hub slug --llm

Always run deep analysis (layers 4-5)

skill-scan scan-hub slug --llm-auto

LLM only if pattern scan finds MEDIUM+

skill-scan scan-hub slug --llm-only

Skip patterns, LLM analysis only

Provider auto-detected from environment:

OPENAI_API_KEY

-> gpt-4o-mini

ANTHROPIC_API_KEY

-> claude-sonnet-4-5

For the Agent

When the user asks to install a skill from ClawHub, follow this workflow:

Scan first

— run

skill-scan scan-hub

BEFORE installing

Check the result

:

If

LOW risk

(exit code 0): proceed with installation

If

MEDIUM risk

show findings to the user and ask whether to proceed
If
HIGH or CRITICAL risk: block installation and show the report Report — always show the user the scan summary (score, risk level, finding count) Alert Format When a scan finds MEDIUM or higher risk: 🛡️ Skill-Scan Alert: {RISK_LEVEL} ({SCORE}/100) Skill: {slug} Findings: {count} issue(s) detected Top threat: {highest severity finding title} Action: Installation blocked pending review. AGENTS.md Integration To integrate skill-scan into your agent's workflow, add ONE of the following templates to your AGENTS.md (or equivalent agent instructions file). Option A: Automatic Scanning (Recommended) All ClawHub skill installs are automatically scanned before installation. No manual step needed.

Skill-Scan — Automatic Pre-Install Security Scanning BEFORE installing any skill from ClawHub, you MUST scan it with skill-scan first. This is non-negotiable — never skip the scan.

Workflow 1. When the user asks to install a skill (e.g. clawhub install <slug> ), run the scan FIRST: ```bash skill-scan scan-hub < slug

Check the exit code and output : Exit code 0 (LOW risk) — proceed with clawhub install Exit code 1 (MEDIUM risk) — show findings to the user, ask whether to proceed Exit code 1 (HIGH/CRITICAL risk) — BLOCK installation, show the full report Never install a HIGH or CRITICAL risk skill without explicit user override Always show the scan summary to the user before proceeding with installation Periodic Audits When asked to audit installed skills, batch scan the skills directory: skill-scan batch { skillsDir } LLM Deep Analysis For high-stakes installs or when pattern scan returns MEDIUM, escalate to LLM analysis: skill-scan scan-hub < slug

--llm-auto

Option B: Manual On-Demand Scanning

Skills are only scanned when the user explicitly asks. Installation proceeds without automatic checks. ```markdown

Skill-Scan — On-Demand Skill Security Scanning

skill-scan is available to audit skill packages for security threats. Use it when the user requests a security scan.

Commands

Scan a ClawHub skill: skill-scan scan-hub <slug>
Scan a local skill: skill-scan scan /path/to/skill
Batch scan installed skills: skill-scan batch {skillsDir}
Deep analysis with LLM: add --llm or --llm-auto to any scan command

Risk Levels

LOW (80-100) — safe, no significant threats
MEDIUM (50-79) — review findings, use caution
HIGH (20-49) — serious threats, do not install
CRITICAL (0-19) — multiple critical threats, do not install

When the User Asks to Scan

Run the appropriate scan command
Show the scan summary (score, risk level, finding count)
If MEDIUM+, list the top findings with severity and description
Let the user decide what to do Install-Time Setup When this skill is first installed, ask the user: How should skill-scan integrate with your workflow? Automatic (Recommended) — Every clawhub install is scanned first. HIGH/CRITICAL installs are blocked automatically. Manual — Scanning only happens when you explicitly ask for it. Based on their choice, add the corresponding template (Option A or Option B above) to the project's AGENTS.md . Detection Categories Execution threats — eval() , exec() , child_process , dynamic imports Credential theft — .env access, API keys, tokens, private keys, wallet files Data exfiltration — fetch() , axios , requests , sockets, webhooks Filesystem manipulation — Write/delete/rename operations Obfuscation — Base64, hex, unicode encoding, string construction Prompt injection — Jailbreaks, invisible characters, homoglyphs, roleplay framing, encoded instructions Behavioral signatures — Compound patterns: data exfiltration, trojan skills, evasive malware, persistent backdoors Requirements Python 3.10+ httpx>=0.27 (for LLM API calls only) API key only needed for --llm modes (static analysis is self-contained)

安装