audit-augmentation 是做什麼的？
將 SARIF 靜態分析結果和 weAudit 註釋文件中的外部稽核發現，增強至 Trailmark 程式碼圖譜。透過檔案和行號的重疊，將發現結果對應到圖譜節點，建立基於嚴重性的子圖譜，並支援與預分析資料（爆炸半徑、污染分析等）進行交叉比對。當你需要將 SARIF 結果投射到程式碼圖譜、覆疊 weAudit 註釋、將 Semgrep 或 CodeQL 的發現與呼叫圖譜資料交叉比對，或在程式碼結構的脈絡中視覺化稽核發現時，即可使用此技能。
將 SARIF 靜態分析結果和 weAudit 註釋文件中的外部稽核發現，增強至 Trailmark 程式碼圖譜。將發現結果按檔案和行號對應到圖譜節點，建立基於嚴重性的子圖譜，並支援與預分析資料進行交叉比對。
這項技能用於整合外部稽核發現到 Trailmark 程式碼圖譜中，支援兩種主要輸入格式：SARIF 靜態分析結果（如 Semgrep、CodeQL）和 weAudit 註釋文件。它透過檔案和行號的重疊，自動將發現結果映射到程式碼圖譜的對應節點上，並根據嚴重性等級建立子圖譜，方便你快速聚焦高風險區塊。此外，它還能將這些稽核發現與先前的分析資料（如爆炸半徑、污染分析等）進行交叉比對，讓你從程式碼結構和依賴關係的角度更深入地理解安全性問題。此技能適用於任何支援 Trailmark 程式碼圖譜的平台，例如使用 Claude Code 進行程式碼審查時，可以將靜態分析工具的輸出直接疊加到圖譜上，視覺化地呈現問題所在的上下文。
npx skills add https://github.com/trailofbits/skills/tree/main/skills/audit-augmentationInstalled? Explore more 研究與資料分析 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
將 SARIF 靜態分析結果和 weAudit 註釋文件中的外部稽核發現，增強至 Trailmark 程式碼圖譜。透過檔案和行號的重疊，將發現結果對應到圖譜節點，建立基於嚴重性的子圖譜，並支援與預分析資料（爆炸半徑、污染分析等）進行交叉比對。當你需要將 SARIF 結果投射到程式碼圖譜、覆疊 weAudit 註釋、將 Semgrep 或 CodeQL 的發現與呼叫圖譜資料交叉比對，或在程式碼結構的脈絡中視覺化稽核發現時，即可使用此技能。
This repo contains 20 individual skills — each has its own dedicated page.
AddressSanitizer detects memory errors during fuzzing. Use when fuzzing C/C++ code to find buffer overflows and use-after-free bugs.
AFL++ is a fork of AFL with better fuzzing performance and advanced features. Use for multi-core fuzzing of C/C++ projects.
Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches AI agents running in CI/CD pipelines, including env var intermediary patterns, direct expression injection, dangerous sandbox configurations, and wildcard user allowlists. Use when reviewing workflow files that invoke AI coding agents, auditing CI/CD pipeline security for prompt injection risks, or evaluating agentic action configurations.
Scans Algorand smart contracts for 11 common vulnerabilities including rekeying attacks, unchecked transaction fees, missing field validations, and access control issues. Use when auditing Algorand projects (TEAL/PyTeal).
Clarify requirements before implementing. Use when serious doubts arise.
Atheris is a coverage-guided Python fuzzer based on libFuzzer. Use for fuzzing pure Python code and Python C extensions.
Enables ultra-granular, line-by-line code analysis to build deep architectural context before vulnerability or bug finding.
Prepares codebases for security review using Trail of Bits' checklist. Helps set review goals, runs static analysis tools, increases test coverage, removes dead code, ensures accessibility, and generates documentation (flowcharts, user stories, inline comments).
Searches and explores Burp Suite project files (.burp) from the command line. Use when searching response headers or bodies with regex patterns, extracting security audit findings, dumping proxy history or site map data, or analyzing HTTP traffic captured in a Burp project.
Scans Cairo/StarkNet smart contracts for 6 critical vulnerabilities including felt252 arithmetic overflow, L1-L2 messaging issues, address conversion problems, and signature replay. Use when auditing StarkNet projects.
cargo-fuzz is the de facto fuzzing tool for Rust projects using Cargo. Use for fuzzing Rust code with libFuzzer backend.
Diagnose and fix Claude in Chrome MCP extension connectivity issues. Use when mcp__claude-in-chrome__* tools fail, return "Browser extension is not connected", or behave erratically.
Diagnose and fix Claude in Chrome MCP extension connectivity issues. Use when mcp__claude-in-chrome__* tools fail, return "Browser extension is not connected", or behave erratically.
Runs an autonomous review-and-fix improvement loop over any code target — a skill, plugin, module, or directory — using a reviewer the user names: any installed skill or agent. Keeps a cross-round findings ledger, escalates when fixes stop converging, and guards scope mechanically. Use when asked to 'improve this code until review passes', 'run an improvement loop with <reviewer>', or to iterate review-and-fix with a specific reviewer. For skills prefer the skill-improver entry; for a branch prefer pr-improver.
Systematic code maturity assessment using Trail of Bits' 9-category framework. Analyzes codebase for arithmetic safety, auditing practices, access controls, complexity, decentralization, documentation, MEV risks, low-level code, and testing. Produces professional scorecard with evidence-based ratings and actionable recommendations.
Scans a codebase for security vulnerabilities using CodeQL's interprocedural data flow and taint tracking analysis. Triggers on "run codeql", "codeql scan", "codeql analysis", "build codeql database", or "find vulnerabilities with codeql". Supports "run all" (security-and-quality + security-experimental suites) and "important only" (high-precision security findings) scan modes. Also handles creating data extension models and processing CodeQL SARIF output.
Detects timing side-channel vulnerabilities in cryptographic code. Use when implementing or reviewing crypto code, encountering division on secrets, secret-dependent branches, or constant-time programming questions in C, C++, Go, Rust, Swift, Java, Kotlin, C#, PHP, JavaScript, TypeScript, Python, or Ruby.
Constant-time testing detects timing side channels in cryptographic code. Use when auditing crypto implementations for timing vulnerabilities.
Scans Cosmos SDK blockchain modules and CosmWasm contracts for consensus-critical vulnerabilities — chain halts, fund loss, state divergence. 25 core + 16 IBC + 10 EVM + 3 CosmWasm patterns. Use when auditing custom x/ modules, reviewing IBC integrations, or assessing pre-launch chain security. Updated for SDK v0.53.x.
Coverage analysis measures code exercised during fuzzing. Use when assessing harness effectiveness or identifying fuzzing blockers.
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Agent skill repository discovered by 10x-chat research.
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.