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Found 27 Skills
Read and search GitHub repository documentation via gitmcp.io MCP service. **WHEN TO USE:** - User provides a GitHub URL - User mentions a specific repo in owner/repo format - User asks "what does this repo do?", "read the docs for X repo", or similar - User wants to search code or docs within a repo
Use when you need durable, repo-specific context (architecture, domain model, how a flow works, standards, gotchas, decisions) before engineering or reviewing, or when you have learned something worth recording. Reads and writes an in-repo Obsidian knowledge base (docs/<repo>-vault/), synced by git.
Use when scaffolding the agent knowledge layer (ARCHITECTURE.md, QUALITY_SCORE.md, docs/) for a repo.
Update repo documentation and agent-facing guidance such as AGENTS.md, README.md, docs/, specs, plans, and runbooks. Use when code, skill, or infrastructure changes risk doc drift or when documentation needs cleanup or restructuring. Do not use for code review, runtime verification, or `agent-readiness` setup.
Build or update an interactive isometric map of a repository's architecture — buildings sized by real measurements, neighborhoods by subsystem, animated flows tracing actual call paths, and a drift counter that fails CI when the map falls behind the code. Use when someone wants to see, explain, or onboard people to how a codebase fits together, or asks for an architecture diagram, system map, codebase overview, or "show me how this repo works". Adapts to the repo's design system; re-run to refresh.
Intelligent README.md generation prompt that analyzes project documentation structure and creates comprehensive repository documentation. Scans .github/copilot directory files and copilot-instructions.md to extract project information, technology stack, architecture, development workflow, coding standards, and testing approaches while generating well-structured markdown documentation with proper formatting, cross-references, and developer-focused content.
Create, optimize, update, and validate AGENTS.md files with maximum token efficiency. Use when the user asks to (1) create new AGENTS.md files for any repository, (2) optimize/condense existing AGENTS.md to reduce token count, (3) update/refresh AGENTS.md to sync with codebase changes, (4) validate AGENTS.md quality and completeness, or (5) improve AGENTS.md files to be more effective for AI agents. Always generates token-efficient, condensed output focused on actionable commands and patterns while maintaining model-agnostic language.
Generate project-level AGENTS.md guides that capture conventions, workflows, and required follow-up tasks. Use when a repository needs clear agent onboarding covering structure, tooling, testing, task flow, README expectations, and conventional commit summaries.
Write or revise AGENTS.md per embedded output contract. Use when creating Agent entry for new projects, auditing existing AGENTS.md, or adopting the AI Cortex entry format.
Generate FEATURES.md at the repo root by reading CONTEXT.md and docs/adr/, then enumerating the user-facing features the domain implies. Use after /grill-with-docs has settled the domain language and before /to-prd writes per-feature specs. Bridges the product→engineering gap between domain understanding and feature specification — the missing step that mattpocock's chain doesn't cover natively.
Create and maintain Architecture Decision Records (ADRs) in git repos. Use when you need to propose, write, update, accept/reject, deprecate, or supersede an ADR; bootstrap an adr folder and index; or enforce ADR conventions (status, dates, links, and filenames) for markdown decision logs.
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