SkillSpector: Security scanner for AI agent skills.
What problem it solves
AI agent skills execute with implicit trust and often receive minimal vetting before installation. SkillSpector helps users assess whether a skill contains vulnerabilities, malicious patterns, or other security risks before installing it.
Key features
Multi-format scanning — Scan agent skills supplied as Git repositories, URLs, zip files, directories, or individual files.
Source: README
Broad vulnerability detection — Analyze skills for vulnerabilities across 17 categories, including prompt injection, data exfiltration, privilege escalation, supply-chain risks, tool misuse, and dangerous code.
Source: README
Static and semantic analysis — Use fast static analysis as the first stage, with optional LLM semantic evaluation for deeper assessment.
Source: README
Multiple report formats — Produce terminal, JSON, Markdown, and SARIF reports for interactive review, automation, documentation, and CI/CD integration.
Source: README
Risk scoring and baseline suppression — Assign a 0-100 risk score with severity labels and recommendations, and use baselines to suppress accepted findings so subsequent scans surface new issues.
Source: README
Quick start
Copied verbatim from the project's README.
uv tool install git+https://github.com/NVIDIA/skillspector.git
# Update later: uv tool update skillspectorWho it may suit
This project may suit developers, security teams, and agent-skill maintainers who need to vet local, repository, URL, or archive-based skills before installation.
Topics to discuss
Which vulnerability categories should receive dedicated detectors and rule examples?
How should static findings and optional LLM assessments be combined in CI/CD policies?
What baseline review practices can keep accepted findings visible without obscuring new risks?
Written by ReadmeX AI from the project's README, source and public GitHub data; it may contain mistakes.
Read the original README https://github.com/NVIDIA/SkillSpector#readme
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