Total 50,678 skills, AI & Machine Learning has 8495 skills
Showing 12 of 8495 skills
Shared foundation for Oracle & Corrector agents. Establishes the source hierarchy for resolving conflicts between documentation, code, and specs. Load this skill first when investigating how the system works.
Goal-based workflow orchestration - routes tasks to specialist agents based on user goals
Use this agent when you need to gather comprehensive documentation and best practices for frameworks, libraries, or dependencies in your project. This includes fetching official documentation, exploring source code, identifying version-specific constraints, and understanding implementation patterns. <example>Context: The user needs to understand how to properly implement a new feature using a specific library. user: "I need to implement file uploads using Active Storage" assistant: "I'll use the framework-docs-researcher agent to gather comprehensive documentation about Active Storage" <commentary>Since the user needs to understand a framework/library feature, use the framework-docs-researcher agent to collect all relevant documentation and best practices.</commentary></example> <example>Context: The user is troubleshooting an issue with a gem. user: "Why is the turbo-rails gem not working as expected?" assistant: "Let me use the framework-docs-researcher agent to investigate the turbo-rails documentation...
Unified team skill for issue resolution. All roles invoke this skill with --role arg for role-specific execution. Triggers on "team issue".
Use when the user says "vm", "voice mode", "team", "coordinate", or needs to orchestrate multiple agents working on related tasks in parallel
Form a high-level investment committee consisting of three virtual experts modeled after legendary investors (Buffett, Wood, Druckenmiller) to conduct independent multi-round adversarial debates. True independent thinking is achieved through physically isolated Gemini API calls, and final resolutions are formed via voting. Use when evaluating investment decisions, reviewing stock research reports, or seeking multi-perspective analysis on public companies.
Convert mixed-format datasheets and hardware reference files (PDF, DOCX, HTML, Markdown, XLSX/CSV) into normalized Markdown knowledge files for AI coding agents. Use when a user asks to ingest datasheets, register maps, pinout/timing sheets, revision histories, or internal hardware notes before searching datasheet content or generating code. Produce RAG-ready section chunks, anchors, image references, and metadata under .context/knowledge.
AI Native Camp Day 4 Wrap & Analyze. session-wrap 스킬을 직접 만들고, history-insight와 session-analyzer로 세션을 분석한다. "4일차", "Day 4", "wrap", "세션 분석", "session wrap", "세션 래핑" 요청에 사용.
AI agent-focused RSS feed discovery tool with JSON output. Use when Claude needs to discover RSS/Atom feeds from websites for monitoring, aggregation, or content syndication purposes. Triggered by: "find RSS feed", "discover RSS", "find Atom feed", "get RSS URLs", "find feeds from [URL]", or when working with content aggregation, feed readers, or RSS monitoring workflows.
Audit and manage the full project context landscape: CLAUDE.md memory hierarchy, project documentation, markdown footprint, and content overlap. Detects project type, scores quality, flags stale docs, and reports total context cost. Trigger with 'audit context', 'audit memory', 'update CLAUDE.md', 'restructure memory', 'session capture', 'check project docs', 'markdown footprint', or 'what docs does this project need'.
Vertex Ai Deployer - Auto-activating skill for ML Deployment. Triggers on: vertex ai deployer, vertex ai deployer Part of the ML Deployment skill category.
Stores decisions and patterns in knowledge graph. Use when saving patterns, remembering outcomes, or recording decisions.