
brucemao3/safeskillscan-readyclaw
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Multi-stage security scanner for AI agent skill files — regex, AST, semantic embeddings, and LLM audit in one pipeline.
Обзор
Multi-stage security scanner for AI Agent skill files (SKILL.md). Designed to detect malicious patterns in community-contributed skill packages before they are installed into AI coding assistants (Claude Code, OpenClaw, etc.). Each stage filters sequentially. Skills rejected at an earlier stage are not re-evaluated by later stages. Place skill files in data/files/ with one directory per skill: Or provide a SQLite database at data/skills.db with a skill_content table containing slug and skill_md columns. For large-scale scans (10k+ skills), use the batch runners: Each job gets its own database and report under jobs/{job_id}/. Detection rules are defined in YAML under skill_scanner/rules/:
README
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Developed by https://x.com/SlimeVerse_
Preconfigured OpenClaw website:https://www.readyclaw.org/en
SafeSkillScan
Multi-stage security scanner for AI Agent skill files (SKILL.md).
Designed to detect malicious patterns in community-contributed skill packages before they are installed into AI coding assistants (Claude Code, OpenClaw, etc.).
Pipeline Overview
Stage 0: Intake → Normalize, dedup, encoding/size checks
Stage 1: Rules + AST → Regex rules, IOC domain matching, AST analysis
Stage 2: Semantic → Embedding-based coherence, structural entropy
Stage 3: LLM Audit → Multi-model voting (Claude, GPT, Gemini)
Each stage filters sequentially. Skills rejected at an earlier stage are not re-evaluated by later stages.
Threat Classes
| Class | Name |
|---|---|
| 1 | Credential Theft |
| 2 | Data Exfiltration |
| 3 | Home Directory Destruction |
| 4 | Download & Execute |
| 5 | Prompt Injection / Agent Hijack |
| 6 | Crypto Wallet Fraud |
| 7 | Surveillance / Keylogging |
Quick Start
1. Install
python -m venv .venv
source .venv/bin/activate
pip install -r requirements-scanner.txt
2. Configure
cp .env.example .env
# Edit .env and add your API keys (required for Stage 3 only)
3. Prepare Data
Place skill files in data/files/ with one directory per skill:
data/
└── files/
├── my-skill/
│ └── SKILL.md
├── another-skill/
│ └── SKILL.md
└── ...
Or provide a SQLite database at data/skills.db with a skill_content table
containing slug and skill_md columns.
4. Run
Single skill scan:
python -m skill_scanner scan --slug my-skill
Full scan (Stage 0-2, no API keys needed):
python -m skill_scanner scan --all --stage 2
Full pipeline with LLM audit:
python -m skill_scanner scan --all
View results:
python -m skill_scanner report --summary
python -m skill_scanner report --findings --severity CRITICAL
python -m skill_scanner report --export-csv results.csv
Batch Scanning
For large-scale scans (10k+ skills), use the batch runners:
# Stage 0-2 (deterministic + semantic)
python batch_scan.py
# Stage 3 (LLM audit, requires API keys)
python batch_scan_s3.py
Job System
Scan ad-hoc skill batches in isolation:
# Scan a .tar.gz batch
python job_scan.py input.tar.gz --name mybatch --stages 0,1,2,3 --concurrency 5
# List all jobs
python job_report.py --list
# Regenerate report
python job_report.py --job-id mybatch-abc123
Each job gets its own database and report under jobs/{job_id}/.
Rule Files
Detection rules are defined in YAML under skill_scanner/rules/:
| File | Coverage |
|---|---|
credential_access.yaml |
SSH keys, API tokens, browser credentials |
data_exfiltration.yaml |
External POST requests, webhook exfiltration |
Рекомендуемые инструменты
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Установка
npx skillfish add brucemao3/safeskillscan-readyclaw