Automation
ai-skill-scanner
Scan OpenBot/Clawdbot skills for security vulnerabilities
---
name: skill-scanner
description: Scan OpenBot/Clawdbot skills for security vulnerabilities, malicious code, and suspicious patterns before installing them. Use when a user wants to audit a skill, check if a ClawHub skill is safe, scan for credential exfiltration, detect prompt injection, or review skill security. Triggers on security audit, skill safety check, malware scan, or trust verification.
---
# Skill Security Scanner
Scan skills for malicious patterns before installation. Detects credential exfiltration, suspicious network calls, obfuscated code, prompt injection, and other red flags.
## Quick Start
```bash
# Scan a local skill folder
python3 scripts/scan.py /path/to/skill
# Verbose output (show matched lines)
python3 scripts/scan.py /path/to/skill --verbose
# JSON output (for automation)
python3 scripts/scan.py /path/to/skill --json
```
## Workflow: Scan Before Install
1. Download or locate the skill folder
2. Run `python3 scripts/scan.py <skill-path> --verbose`
3. Review findings by severity (CRITICAL/HIGH = do not install)
4. Report results to user with recommendation
## Score Interpretation
| Score | Meaning | Recommendation |
|-------|---------|----------------|
| CLEAN | No issues found | Safe to install |
| INFO | Minor notes only | Safe to install |
| REVIEW | Medium-severity findings | Review manually before installing |
| SUSPICIOUS | High-severity findings | Do NOT install without thorough manual review |
| DANGEROUS | Critical findings detected | Do NOT install — likely malicious |
## Exit Codes
- `0` = CLEAN/INFO
- `1` = REVIEW
- `2` = SUSPICIOUS
- `3` = DANGEROUS
## Rules Reference
See `references/rules.md` for full list of detection rules, severity levels, and whitelisted domains.
## Limitations
- Pattern-based detection — cannot catch all obfuscation techniques
- No runtime analysis — only static scanning
- False positives possible for legitimate tools that access network/files
- Always combine with manual review for HIGH/MEDIUM findings
automation
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