skill-review
A security scanner CLI for Claude Code Skill packages. It combines deterministic static pre-scanning with LLM-driven 7-layer analysis plus integrated tool-based verification to surface security risks before you install a Skill.
When to use
- Auditing a third-party Skill before installation
- Checking a skill directory for prompt injection, credential theft, data exfiltration, or hidden backdoors
- Evaluating supply chain risk of a Skill's npm/PyPI dependencies
- CI/CD integration to block high-risk Skills automatically
How it works
The scanner runs in two main steps:
-
Pre-scan (deterministic, no LLM) — walks all files and flags: symlinks, suspicious filenames (Unicode confusables, shell metacharacters), large files, binary executables, invisible characters, ANSI escape sequences, JS obfuscation patterns, and hardcoded URLs.
-
LLM Analysis — an Explore Agent reads each file and performs 7-layer analysis:
- Layer 1: Prompt Injection (direct injection, jailbreak, remote prompt loading)
- Layer 2: Malicious Behavior (credential theft, data exfiltration, sandbox escape)
- Layer 3: Dynamic Code Loading (remote execution via fetch+eval, curl|sh, etc.)
- Layer 4: Obfuscation & Binary (obfuscated scripts, compiled binaries)
- Layer 5: Dependencies & Supply Chain (npm/PyPI/CLI tool inventory, typosquat detection)
- Layer 6: System Modification (global installs, profile changes, cron jobs)
- Layer 7: Code Quality (hardcoded secrets, insecure configs, vulnerable code patterns)
During this analysis, the integrated `deepAnalysis` tool is used to verify dependencies, URLs, and binaries when deeper inspection is needed.
-
Deterministic Scoring — each finding is scored based on its risk score. The overall risk level (0-5 risk score mapped to safe/medium/high) and recommendation (install/caution/do_not_install) are computed deterministically, not by the LLM.
Installation
cd <skill-review-dir>
npm install
Configuration
Create .env and fill in your LLM provider details:
| Variable | Description | Default |
|---|---|---|
OPENAI_API_BASE |
LLM API base URL (OpenAI-compatible) | required |
OPENAI_API_KEY |
API key | required |
OPENAI_API_MODEL |
Model name | gpt-4o |
NPM_REGISTRY_URL |
npm registry for dependency checks | https://registry.npmjs.org |
PYPI_INDEX_URL |
PyPI index for dependency checks | https://pypi.org |
Alternatively, pass a JSON config file via --config.
Usage
# Standard scan (pre-scan + LLM analysis)
node index.mjs <skill-dir>
# Pre-scan only (no LLM, fast)
node index.mjs --pre <skill-dir>
# JSON output, save to file
node index.mjs --json -o report.json <skill-dir>
# Chinese language report
node index.mjs --lang zh <skill-dir>
# Verbose logs to stderr + log file
node index.mjs -v --log scan.log <skill-dir>
Options
| Option | Description |
|---|---|
<skill-dir> |
Path to the skill directory to scan (required, positional) |
--config <file> |
Path to JSON config file |
--pre |
Run pre-scan only (no LLM calls) |
--lang <lang> |
Report language (default: English) |
--json |
Output raw JSON instead of text report |
-o, --output <file> |
Save report to file (default: stdout) |
--log <file> |
Save detailed logs to file |
-v, --verbose |
Stream detailed logs to stderr |
-h, --help |
Show help |
Output
The text report shows each layer with a risk score (0-5), star rating, and up to 5 findings per layer. The JSON output contains the full structured result with all findings, layer scores, overall risk, and recommendation.
Risk levels: safe (0) / medium (1-4) / high (5)
Recommendations: install (safe) / caution (medium) / do_not_install (high)