Skill Scanner

A best-effort security scanner for AI Agent Skills that detects prompt injection, data exfiltration, and malicious code patterns. It combines pattern-based detection (YAML + YARA-X), AST and dataflow analysis, an optional LLM-as-a-judge, and a bounded CEL decision layer over typed detector facts.

Important: This scanner provides best-effort detection, not comprehensive or complete coverage. A scan that returns no findings does not guarantee that a skill is free of all threats. See Scope and Limitations below.

Supports OpenAI Codex Skills and Cursor Agent Skills formats following the Agent Skills specification. With --lenient, also scans non-standard formats such as Claude Code .claude/commands/*.md and flat markdown skill repos.

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Highlights

• Multi-Engine Detection - Static analysis, behavioral dataflow, LLM semantic analysis, and cloud-based scanning for layered, best-effort coverage
• Typed CEL Decisions - The core scanner uses the official cel-go v0.32.0 runtime to correlate bounded facts after deterministic detection and before optional LLM analysis
• Measured Presets - low-noise and quiet presets and LLM caps, with recall, false-positive rate and F1 published for each (Recommended Settings)
• CI/CD Ready - SARIF output for GitHub Code Scanning, reusable GitHub Actions workflow, exit codes for build failures
• Pre-commit Hook - Standard pre-commit framework integration to scan skills before every commit
• Extensible - Plugin architecture for custom analyzers

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Scope and Limitations

Skill Scanner is a detection tool. It identifies known and probable risk patterns, but it does not certify security.

Key limitations:

• No findings ≠ no risk. A scan that returns "No findings" indicates that no known threat patterns were detected. It does not guarantee that a skill is secure, benign, or free of vulnerabilities.
• Coverage is inherently incomplete. The scanner combines signature-based detection, LLM-based semantic analysis, behavioral dataflow analysis, optional cloud services, and configurable rule packs. While this approach improves coverage, no automated tool can detect every technique, especially novel or zero-day attacks.
• False positives and false negatives can occur. Presets, the LLM judge and scoped suppressions reduce noise, but no configuration eliminates all incorrect classifications. Pick a measured setup from Recommended Settings and tune the scan policy to your risk tolerance.
• Human review remains essential. Automated scanning is one component of a defense-in-depth strategy. High-risk or production deployments should pair scanner results with manual code review and/or threat modeling.

Measured results

On held-out skills (MaliciousSkillBench's frozen test split, 839 malicious and 545 harmless):