secure-workflow-guide 是做什麼的？
引導您完成 Trail of Bits 的 5 步驟安全開發工作流程。執行 Slither 掃描、檢查特殊功能（可升級性/ERC 符合性/代幣整合）、生成視覺化安全圖表、協助記錄用於模糊測試/驗證的安全屬性，並審查手動安全領域。
引導您完成 Trail of Bits 的 5 步驟安全開發工作流程：執行 Slither 掃描、檢查特殊功能（可升級性/ERC 符合性/代幣整合）、生成視覺化安全圖表、協助記錄用於模糊測試/驗證的安全屬性，並審查手動安全領域。
這是一個 AI 代理技能，旨在引導開發者遵循 Trail of Bits 的 5 步驟安全開發工作流程。它能自動執行 Slither 靜態分析掃描，檢查智能合約的特殊功能，例如可升級性、ERC 標準符合性以及代幣整合。該技能還可以生成視覺化的安全圖表，幫助開發者記錄安全屬性，以便進行模糊測試和形式化驗證，並提供手動安全審查領域的檢查清單。此技能支援在 Claude Code、Cursor 和 Codex 等 AI 編碼代理中使用，幫助開發者將安全性深度整合到開發流程中，降低智能合約漏洞的風險。
npx skills add https://github.com/trailofbits/skills/tree/main/skills/secure-workflow-guideInstalled? Explore more 影像 skills: steipete/songsee, affaan-m/frontend-design, mattpocock/improve-codebase-architecture · View all 6 →
引導您完成 Trail of Bits 的 5 步驟安全開發工作流程。執行 Slither 掃描、檢查特殊功能（可升級性/ERC 符合性/代幣整合）、生成視覺化安全圖表、協助記錄用於模糊測試/驗證的安全屬性，並審查手動安全領域。
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.
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.
Generate spectrograms and feature-panel visualizations from audio with the songsee CLI.
Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.
Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.
Add documentation for a new AI provider — usage docs, env vars, Docker config, image resources.
Tests in real browsers via Chrome DevTools MCP. Use when building or debugging anything that runs in a browser. Use when you need to inspect the DOM, capture console errors, analyze network requests, profile performance, or verify visual output with real runtime data. Requires the chrome-devtools MCP server to be configured.
This skill should be used when the user wants to visualize data. It intelligently selects the most suitable chart type from 26 available options, extracts parameters based on detailed specifications, and generates a chart image using a JavaScript script.