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Found 21 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
Certification coach for Flows apps. Captures the app name, value case, persona, problem, and design intent through a structured conversation and writes App-Brief.md at the repo root. This is the FIRST step of the Flows app certification flow — run it immediately after `npx @cognite/cli apps create`, before building. Use when the user asks to start an app brief, run the certification coach, fill out the app brief, or run flows-app-brief.
Use this skill to create CodeTour .tour files — persona-targeted, step-by-step walkthroughs that link to real files and line numbers. Trigger for: "create a tour", "make a code tour", "generate a tour", "onboarding tour", "tour for this PR", "tour for this bug", "RCA tour", "architecture tour", "explain how X works", "vibe check", "PR review tour", "contributor guide", "help someone ramp up", or any request for a structured walkthrough through code. Supports 20 developer personas (new joiner, bug fixer, architect, PR reviewer, vibecoder, security reviewer, and more), all CodeTour step types (file/line, selection, pattern, uri, commands, view), and tour-level fields (ref, isPrimary, nextTour). Works with any repository in any language.
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.
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.
Create, optimize, and maintain AGENTS.md and CLAUDE.md files using progressive disclosure. Use when: User wants to create AGENTS.md/CLAUDE.md, optimize existing AI documentation, implement progressive disclosure, detect project structure (monorepo/polyrepo), or prevent documentation bloat. Triggers on: "create agents.md", "update AGENTS.md", "AI documentation", "project context", "monorepo documentation", "progressive disclosure", "Claude Code context", or when AI repeatedly asks the same questions about the project.
Create or update root and nested AGENTS.md files that document scoped conventions, monorepo module maps, cross-domain workflows, and (optionally) per-module feature maps (feature -> paths, entrypoints, tests, docs). Use when the user asks for AGENTS.md, nested agent instructions, or a module/feature map.
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.
Research a specific system and create or update its blueprints/ documentation
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.
Bootstrap a Memory Bank for a new or existing repository, then route into PRD-driven or brownfield workflows.
Review recent repository changes and decide whether AGENTS.md or other project-level documentation needs a high-level update. Use when finishing a feature, fix, refactor, or architectural change and you need to preserve repo-shaping guidance such as new patterns, constraints, workflows, validation rules, or onboarding-relevant gotchas without adding low-level implementation detail.