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Found 140 Skills
Generate project documentation from codebase analysis — ARCHITECTURE.md, API_ENDPOINTS.md, DATABASE_SCHEMA.md. Reads source code, schema files, routes, and config to produce accurate, structured docs. Use when starting a project, onboarding contributors, or when docs are missing or stale. Triggers: 'generate docs', 'document architecture', 'create api docs', 'document schema', 'project documentation', 'write architecture doc'.
Resolve implementation ambiguities before planning begins. Two modes: Discussion mode surfaces gray areas with concrete options for greenfield work. Assumptions mode reads the codebase, forms evidence-based opinions, and asks the user to correct only what's wrong (brownfield work). Use for "discuss ambiguities", "resolve gray areas", "clarify before planning", "assumptions mode", "what are the gray areas", "before we plan". Do NOT use for broad design exploration (use feature-design) or for planning itself (use feature-plan).
Statistical rule discovery through measurement of Go codebases: Count patterns, derive confidence-scored rules, produce Style Vector fingerprint. Use when analyzing codebase conventions, extracting implicit coding rules, profiling a repo before onboarding or PR automation. Use for "analyze codebase", "find coding patterns", "what conventions does this repo use", "extract rules", or "codebase DNA". Do NOT use for code review, bug fixes, refactoring, or performance optimization.
Analyze a codebase to figure out how it should be tested with Antithesis: map the system, identify failure-prone areas and testable properties, and produce the research artifacts needed for workload and environment planning.
Runs a trailmark summary analysis on a codebase. Returns language detection, entry point count, and dependency graph shape. Use when vivisect or galvanize needs a quick structural overview. Triggers: trailmark summary, code summary, structural overview.
Progressively gather requirements through automated codebase discovery and yes/no questions, then generate a comprehensive requirements spec. Use when starting a new feature, planning a build, or when you need structured requirements before implementation.
Verify documentation coverage and generate missing docs interactively
[Hyper] Create or refactor a project README.md by carefully reading the codebase. Detects project shape (CLI, library, web app, monorepo, plugin, framework, docs site, service), entry points, scripts, configuration, license, and existing docs, then produces a structured README in the project's primary documentation language. Use when the user wants a new README, a refactor of a stale README, or a section update grounded in the actual code.
Route durable graph-building requests into one honest mode: assistant-native install, local Python build, incremental refresh, graph query follow-up, or a graphify-style structural fallback for markdown-heavy corpora. Use when the user wants `GRAPH_REPORT.md`, `graph.json`, `graph.html`, repo/corpus relationship tracing, mixed code+docs+asset graphing, or graph-backed architecture understanding that should persist across sessions. Route simple locate/reference work to `codebase-search`, narrative knowledge-base work to `llm-wiki`, and project-memory handoff to `opencontext`.
Universal security and robustness scanner for any codebase. Use when auditing code for vulnerabilities, security issues, bugs, or robustness problems. Automatically detects tech stack, creates custom audit plans, and performs recursive deep analysis.
Research a specific system and create or update its blueprints/ documentation
Use when analyzing repositories, conducting deep research on codebases, performing architecture reviews, or exploring large projects. Use when the user wants to research or analyze a git repo, a GitHub link, or a repository URL.