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Found 172 Skills
Access AI-generated documentation and insights for GitHub repositories via DeepWiki. This skill should be used when exploring unfamiliar codebases, understanding repository architecture, finding implementation patterns, or asking questions about how a GitHub project works. Supports any public GitHub repository.
Deep codebase analysis to generate or regenerate STYLE_GUIDE.md with full evidence citations. Use when /setup-ai's quick pass isn't thorough enough, when conventions have drifted, or after a major refactor. Produces a 17-section style guide citing specific files as evidence.
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`.
Start the dev server, discover API routes from the codebase, hit every endpoint, and report which ones return errors.
Analyze an unfamiliar codebase and generate a structured onboarding guide with architecture map, key entry points, conventions, and a starter CLAUDE.md. Use when joining a new project or setting up Claude Code for the first time in a repo.
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".
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".
Use this skill when you need to keep a repository's README.md file updated with project metadata, installation instructions, usage examples, and more. It automates synchronization by analyzing codebase patterns and dependencies.
Use this skill whenever the user asks to analyze, understand, or survey an entire project, codebase, or any collection of files. Trigger phrases include "analyze a large file", "process multiple files", "comprehend this problem", "take a look at these files", "familiarize yourself with this project", or any similar request, however phrased. Also activate when the task involves processing context that exceeds what can be reasoned about in a single pass, when encountering any input larger than ~50KB that requires detailed analysis, or when the user mentions "context comprehension" or "recursive comprehension". This skill TAKES PRIORITY over your default explore subagents for any project-wide or codebase-wide analysis task.
Find similar or analogous code patterns elsewhere in a codebase. Use when answering "Do we do something similar elsewhere?" or "What existing patterns match this?" Returns factual findings about similar code - locations, similarity type, and confidence.
Help generate or update the AGENTS.md file for guiding AI coding agents.
Create detailed, phased implementation plans through interactive research and iteration. Use when the user explicitly asks to "create a plan", "plan the implementation", or "design an approach" for a feature, refactor, or bug fix. Do not use for quick questions or simple tasks.