審計準備助手 是做什麼的？
使用 Trail of Bits 的檢查清單，為程式碼庫進行安全審查做好準備。協助設定審查目標、執行靜態分析工具、提高測試覆蓋率、移除無效代碼、確保可訪問性，並生成文件（流程圖、使用者故事、內聯註解）。
使用 Trail of Bits 的檢查清單，為程式碼庫進行安全審查做好準備。協助設定審查目標、執行靜態分析工具、提高測試覆蓋率、移除無效代碼、確保可訪問性，並生成文件（流程圖、使用者故事、內聯註解）。
這個技能「trailofbits/審計準備助手」旨在幫助開發者或 AI 代理，使用 Trail of Bits 的安全審查檢查清單，系統性地準備程式碼庫。其主要價值在於將安全審查的前置工作標準化，自動執行關鍵任務，如設定審查目標、執行靜態分析工具（例如 linter 或安全掃描器）、提升測試覆蓋率、清除無效代碼（dead code）、確保可訪問性（accessibility）合規，並生成必要的文件（如流程圖、使用者故事和內聯註解）。這個技能支援任何可執行命令列的開發環境，如 Claude Code、Cursor 或 Codex，透過解析專案結構並應用 Trail of Bits 的安全最佳實務來運作。它特別適合在安全審計前使用，能顯著減少手動準備工作，幫助團隊更有效率地找出潛在漏洞並提升程式碼品質。
npx skills add https://github.com/trailofbits/skills/tree/main/skills/audit-prep-assistantInstalled? 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 →
使用 Trail of Bits 的檢查清單，為程式碼庫進行安全審查做好準備。協助設定審查目標、執行靜態分析工具、提高測試覆蓋率、移除無效代碼、確保可訪問性，並生成文件（流程圖、使用者故事、內聯註解）。
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.
Augments Trailmark code graphs with external audit findings from SARIF static analysis results and weAudit annotation files. Maps findings to graph nodes by file and line overlap, creates severity-based subgraphs, and enables cross-referencing findings with pre-analysis data (blast radius, taint, etc.). Use when projecting SARIF results onto a code graph, overlaying weAudit annotations, cross-referencing Semgrep or CodeQL findings with call graph data, or visualizing audit findings in the context of code structure.
Enables ultra-granular, line-by-line code analysis to build deep architectural context before vulnerability or bug finding.
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.