量綱分析 是做什麼的？
為程式碼庫添加量綱分析註解，記錄單位、維度與小數位縮放。適用於要求註解單位、執行量綱分析，或在 DeFi 協議、鏈下程式碼等涉及算術運算的區塊鏈相關程式碼庫中尋找漏洞的情境。可預防維度不匹配，並及早發現公式錯誤。
為程式碼庫添加量綱分析註解，記錄單位、維度與小數位縮放。適用於要求註解單位、執行量綱分析，或在 DeFi 協議、鏈下程式碼等涉及算術運算的區塊鏈相關程式碼庫中尋找漏洞的情境。可預防維度不匹配，並及早發現公式錯誤。
此技能旨在為程式碼庫自動注入量綱分析註解，明確標示變數或常數的單位、維度及小數位數，特別適用於 DeFi 協議、鏈下服務或任何涉及加密貨幣與數學運算的區塊鏈專案。它能幫助開發者與審計者快速識別因單位混淆（例如將 wei 誤認為 ether）或維度不一致導致的漏洞，並在早期攔截公式邏輯錯誤。該技能支援多種 AI Agent 平台（如 Claude Code、Cursor），其運作方式是根據程式碼中的算術運算式與約定俗成的命名規則，自動推斷並生成對應的註解，讓程式碼的意圖與量綱關係更透明。透過標準化的量綱註解，團隊可以減少溝通成本並提升程式碼的可維護性。
npx skills add https://github.com/trailofbits/skills/tree/main/skills/dimensional-analysisInstalled? 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 →
為程式碼庫添加量綱分析註解，記錄單位、維度與小數位縮放。適用於要求註解單位、執行量綱分析，或在 DeFi 協議、鏈下程式碼等涉及算術運算的區塊鏈相關程式碼庫中尋找漏洞的情境。可預防維度不匹配，並及早發現公式錯誤。
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.
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.