ClawGuard 🛡️

---

name: clawguard

description: "Security scanner for ClawHub skills. Analyze before you install."

license: "MIT"

metadata:

{ "openclaw": { "emoji": "🛡️", "requires": { "bins": ["uv"] } } }

---

# ClawGuard 🛡️

**Scan ClawHub skills for security risks before installing.**

ClawGuard performs static code analysis on ClawHub skills to detect:

Usage

Scan by skill name

Download and scan a skill from ClawHub:

uv run {baseDir}/scripts/scan.py --skill <skill-name>

Scan local directory

Scan a skill directory on your local filesystem:

uv run {baseDir}/scripts/scan.py --path /path/to/skill

JSON output

Get results in JSON format:

uv run {baseDir}/scripts/scan.py --skill <skill-name> --json

Examples

Scan the GitHub skill:

uv run {baseDir}/scripts/scan.py --skill github

Scan a local skill:

uv run {baseDir}/scripts/scan.py --path ~/.openclaw/skills/my-skill

Risk Levels

Exit Codes

Requirements

How It Works

1. **Pattern Matching**: Regex-based detection of dangerous code patterns

2. **AST Analysis**: Python AST parsing for eval/exec detection

3. **URL Extraction**: Identifies all network endpoints

4. **Risk Scoring**: Weighted severity scoring (0-100)

What It Detects

| Category | Weight | Examples |

|----------|--------|---------|

| Network exfiltration | 25 | POST to unknown URL with data |

| Credential access | 20 | Reading API keys, tokens |

| Shell execution | 15 | exec(), subprocess, system() |

| File destruction | 15 | rm -rf, unlink, rmdir |

| Obfuscation | 15 | eval(), atob(), Buffer.from |

| Hidden files | 10 | Dotfiles, hidden directories |

Limitations

Best Practices

1. **Always scan before installing** untrusted skills

2. **Review CAUTION-level findings** manually

3. **Check network endpoints** for unknown domains

4. **Never install DANGEROUS skills** without verification

5. **Report suspicious skills** to ClawHub moderators

License

MIT