“codebase analysis”的搜索结果

Analyze indexed codebases via graph database (neug) and vector index (zvec). Covers call graphs, dependencies, dead code, hotspots, module coupling, architecture reports, semantic search, impact analysis, bug root cause from GitHub issues, class diagrams (UML), and PR review (risk scoring, conflict detection, auto-merge candidates, labeling). Also covers creating, inspecting, and repairing a CodeScope index. Use for: code structure, who calls what, why something changed, similar functions, module boundaries, bug tracing, class relationships, PR risk/conflicts, or any question benefiting from a code knowledge graph. Applies when a `.codegraph` index exists in the workspace, or when the user wants to create one.

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Installs and authenticates the GitHub CLI (gh) in a Claude.ai container, then runs gh commands against it. Use for "install gh", "gh auth", "create an issue or PR from the CLI", "gh is not authenticated", or any GitHub operation you want to issue as a gh command rather than a raw API call. This skill is the transport rather than the analysis. To understand what a repository does or contains, use exploring-codebases; to add a repo to the session, accessing-github-repos.

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Guide for writing ast-grep rules to perform structural code search and analysis. This skill should be used when users need to search codebases using Abstract Syntax Tree (AST) patterns, find specific code structures, or perform complex code queries that go beyond simple text search, or when a simple grep/glob search is insufficient for structural code pattern matching.

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Use when indexing a code repository into Cognee's code graph or querying it (callers, impact analysis, paths, endpoints) from Codex.

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Use when indexing a code repository into Cognee's code graph or querying it (callers, impact analysis, paths, endpoints) from Antigravity.

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Pack repositories into AI-friendly files with Repomix (XML, Markdown, plain text). Use for codebase snapshots, LLM context preparation, security audits, third-party library analysis.

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Use when the user wants a cited, structured read of local documents and project knowledge. Triggers on: "analyze these docs", "scan my project for context", "read the docs folder", "summarize what's in .beagle/concepts/", "extract context from docs/", "what's in this folder", "go read everything in X and tell me what's there". Also invoked programmatically by other beagle skills (prfaq-beagle Ignition, brainstorm-beagle reference points, strategy-interview context grounding) via the companion contract. Does NOT trigger on codebase lookups ("find this function", "search the repo"), web research (use web-research), LLM-as-judge evaluation (use llm-judge), or document editing (use humanize-beagle). Produces a written scan plan, parallel-subagent findings, and a cited synthesis report on disk — never inline prose, never unsourced claims.

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Prepare your codebase 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 comprehensive documentation (flowcharts, user stories, inline comments). (project, gitignored)

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Autonomous feature research and gap analysis. Use when starting /add-new-feature or analyzing existing architecture documents. Explores codebase patterns, identifies ambiguities, and produces feature-context-{slug}.md for orchestrator RT-ICA phase. Does NOT make technical decisions.

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SAM-style feature initiation workflow — discovery through codebase analysis, architecture spec, task decomposition, validation, and context manifest. Use when a user asks to add a feature, plan a feature, or convert an idea into an executable SAM plan.

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Register a plan artifact via the MCP backlog server. Use when you produce a document or report that downstream agents or worktree-isolated environments need to retrieve — feature-context, codebase-analysis, architect, T0-baseline, TN-verification, or research artifacts. Triggers include "store an artifact", "register a plan artifact", "write a report to the backlog", "upload artifact content".

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Semantic code understanding with IDE-like symbol operations. Use when: (1) Large codebase analysis (>50 files), (2) Symbol-level operations (find, rename, refactor), (3) Cross-file reference tracking, (4) Project memory and session persistence, (5) Multi-language semantic navigation. Triggers: "find symbol", "rename function", "find references", "symbol overview", "project memory". IMPORTANT: Prioritize Serena's symbolic tools over file-based grep/read for code exploration.

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Unified code intelligence — Skeleton Index (zero-dep, <4s) + AST knowledge graph (CodeGraph) + architecture diagrams (Mermaid) + smart context builder. Pre-indexes code structure so AI agents understand any codebase instantly. 95% token compression for onboarding. 30% fewer tokens for deep analysis.

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Strategic analysis gate for existing products — multi-dimensional evaluation (tech, product, design, business) using Design Thinking + 9 Windows (TRIZ) + Double Diamond. Outputs 2-3 qualified options with recommendations. Use BEFORE cm-planning for complex initiatives and enhancements on existing codebases.

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Explores codebases to find relevant files, trace execution paths, and map architecture with team communication capabilities for collaborative analysis (converted from agent)

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Generates MkDocs-flavored or standard GitHub-flavored Markdown documentation from codebase analysis findings including API references, architecture guides, standalone markdown files, and change summaries (converted from agent)

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Four-stage alchemical code transmutation — nigredo (decomposition), albedo (purification), citrinitas (illumination), rubedo (synthesis) — with meditate and heal checkpoints between stages. Transforms tangled or legacy code into optimized, well-structured output through systematic material analysis. Use when transforming legacy code into modern equivalents, refactoring deeply tangled modules where incremental fixes keep failing, converting a codebase between paradigms, or when simpler refactoring approaches have stalled and a full-cycle transformation is needed.

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Use when the user wants to use Google Gemini for analysis, large files or codebases, sandbox execution, or brainstorming. Uses headless Gemini CLI scripts (no MCP). Triggers on "use Gemini", "analyze with Gemini", "large file", "sandbox", "brainstorm with Gemini".

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Validate Rust code quality with rustfmt, clippy, cargo check, and security analysis. Use for Rust codebases to ensure idiomatic Rust code and best practices.

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Validate Python code quality with formatting, type checking, linting, and security analysis. Use for Python codebases to ensure PEP 8 compliance, type safety, and code quality.

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Validate TypeScript/JavaScript code quality with ESLint, Prettier, type checking, and security analysis. Use for TypeScript/JS codebases to ensure code quality and standards.

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Full ASVS 5.0 compliance assessment against a codebase and/or architecture diagrams. Reads all 346 controls from the companion CSV, performs targeted code analysis per control, and produces a complete matrix marked COMPLIANT / NON_COMPLIANT / NOT_RELEVANT — with per-control reasoning and evidence (code snippets, file:line references, diagram observations). Outputs a reviewed CSV matrix and a self-contained HTML evidence report.

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White-box source code security review structured around OWASP ASVS 5.0 (427 verification requirements across 16 chapters). Reads and understands application source code to build a security-aware knowledge base that enriches all downstream skills. Covers: tech stack identification, route/endpoint mapping, authentication and authorization architecture, dangerous function patterns, source-to-sink data flow tracing, IaC review, dependency analysis, ASVS compliance mapping, and LLM integration security (prompt injection, tool abuse, output handling, RAG poisoning, MCP server patterns). When LLM/AI framework usage is detected, automatically reviews OWASP LLM Top 10 patterns from source code and chains into /ai-redteam with white-box context for live endpoint testing. Chains into /pentester, /threat-modeling, /web-exploit, /api-security, /cloud-security, /analyze-cve, /credential-audit, and /ai-redteam — providing white-box context that transforms black-box testing into targeted, informed assessment.

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Synthesize project documentation and codebase into comprehensive roadmap status, gaps analysis, and blockers. Use when assessing project health, identifying blockers, tracking progress, comparing plan vs reality, documenting risks, or planning next milestones. Triggers: "roadmap", "project status", "blockers", "risks", "progress", "next milestone", "gaps", "what's done".

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