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Found 88 Skills
Transform legacy codebases into AI-ready projects with Claude Code configurations. Use when (1) analyzing old projects to generate AI coding configurations, (2) creating CLAUDE.md, skills, subagents, slash commands, hooks, or rules for existing projects, (3) user wants to enable vibe coding for a codebase, (4) onboarding new team members with AI-assisted development, (5) user mentions "make project AI-ready", "generate Claude config", or "create coding standards for AI".
Generate hierarchical AGENTS.md structures for codebases. Use when user asks to create AGENTS.md files, analyze codebase for AI agent documentation, set up AI-friendly project documentation, or generate context files for AI coding assistants. Triggers on "create AGENTS.md", "generate agents", "analyze codebase for AI", "AI documentation setup", "hierarchical agents".
Interactive onboarding for new AgentOps users. Guided RPI cycle on your actual codebase in under 10 minutes. Triggers: "quickstart", "get started", "onboarding", "how do I start".
Generate comprehensive Product Requirement Plans (PRPs) for feature implementation with thorough codebase analysis and external research. Use when the user requests a PRP, PRD, or detailed implementation plan for a new feature. Conducts systematic research, identifies patterns, and creates executable validation gates for one-pass implementation success.
Based on the Recursive Language Models (RLM) research by Zhang, Kraska, and Khattab (2025), this skill provides strategies for handling tasks that exceed comfortable context limits through programmatic decomposition and recursive self-invocation. Triggers on phrases like "analyze all files", "process this large document", "aggregate information from", "search across the codebase", or tasks involving 10+ files or 50k+ tokens.
Plan a multi-file refactor with proper sequencing and rollback steps
Agent skill for scout-explorer - invoke with $agent-scout-explorer
Retrieve and explore DeepWiki-generated documentation for public GitHub repositories. Use when listing repository documentation topics, reading DeepWiki pages, or asking focused questions about a codebase that needs current repository structure, architecture notes, or component explanations.
Use when you need a fast, reliable architecture or impact view in a large unfamiliar repo, especially under time pressure or tight context budgets where manual grep or folder inference would be risky.
When a user asks how long a task will take, requests a time estimate, or before starting any non-trivial coding task, immediately run scripts/estimate_task.py to analyze the codebase scope and provide a data-driven time estimate. Show the estimate breakdown, risk factors, and checkpoint recommendations without asking.
Automatically generate HarmonyOS design documents including architecture design documents and functional design documents based on PRD documents. It analyzes the existing code structure of OpenHarmony before generation to ensure compatibility with the current architecture. Chapter 2 of the architecture design document must be Competitor Solution Analysis, which should be placed after the Requirement Background. Applicable to user requests: (1) Generate architecture design document, (2) Generate functional design document, (3) Generate design document from PRD, (4) Create system architecture design, (5) Write functional specification, (6) Analyze OH code structure. Keywords: architecture design, functional design, design doc, competitor solution analysis, OpenHarmony code analysis, architecture design, functional design, design document generation, OH code analysis, analyze codebase, competitor analysis
Use after 2 consecutive failed attempts at solving a problem - STOP guessing and research documentation, codebase, and online resources before resuming