LLMs are entering security teams' toolchains, but the missing piece is a curated, auditable set of reusable behaviors that make those models useful and safe for real-world audits. Trail of Bits Skills packages security-focused "skills" for Claude Code so teams can invoke repeatable, testable LLM-driven analyses instead of ad-hoc prompts.
What Sets It Apart
- Security-first skill catalog: Rather than a general-purpose plugin store, the repo groups skills around security tasks — smart contract checks, static-analysis adapters, supply-chain auditors, malware/YARA authoring, and other focused capabilities — so you get domain-relevant actions rather than generic completions. This reduces prompt engineering and speeds up onboarding for analysts.
- Workflowable and composable: Skills are organized to be composed into audit flows (context building, differential review, false-positive verification, rule creation). That makes it practical to combine static tooling (Semgrep/CodeQL-style analyses) with LLM-driven summarization, triage, and remediation suggestions.
- Engineering-friendly integration: The project includes developer tooling and Codex-sidecar support patterns so teams can test and iterate on skills locally and integrate them into CI or developer environments without rebuilding from scratch.
Who It's For and Trade-offs
Great fit if you: security engineers, audit teams, or consultants who want reproducible LLM-assisted analysis steps and a curated starting point for integrating Claude Code into audits. It helps when you need structured LLM behavior (e.g., rule generation, triage, contextual reviews) tied to existing scanners and artifacts. Look elsewhere if you: require a standalone, non-MCP application (Skills depend on Claude Code/MCP integration) or need turnkey vulnerability scanning with certified results. Skills are adapters and facilitators — human review and standard security tooling remain essential. Expect ongoing maintenance to keep adapters up to date with third-party tools and evolving model behaviors.
Where It Fits
Think of this repository as a middleware layer: it doesn’t replace Semgrep/CodeQL or dedicated fuzzers, but it wraps and orchestrates them inside LLM-driven workflows so outputs become more actionable (prioritized findings, human-readable summaries, remediation suggestions). For teams already experimenting with agentized workflows or Claude Code, it significantly lowers the friction of moving from exploratory prompts to reusable audit steps.
