Total 56,150 skills, AI & Machine Learning has 9352 skills
Showing 12 of 9352 skills
Look up Gemini API documentation, SDK patterns, and current best practices when building with Google Gemini. Maps topics to local cached docs and live sources, provides correct @google/genai patterns, and highlights deprecated vs current API usage. Trigger with 'gemini docs', 'gemini guide', 'how to use gemini', 'gemini SDK', '@google/genai', or when building code that imports from @google/genai or google-genai.
Guide for creating Claude Code skills to document @rytass packages (建立套件文件 skill 指南). Use when creating new package documentation skills, writing SKILL.md files, or designing skill structure.
Execute plan files by launching multiple parallel subagents to complete tasks simultaneously. Triggers on explicit "/parallel-task" commands.
Team composition knowledge for Claude Code Agent Teams - when to suggest teams, optimal sizing, spawn prompt patterns
This skill should be used when the user provides a strategy, plan, or decision document and wants to surface hidden assumptions and blind spots using the Known/Unknown 4-quadrant framework. Trigger on "known unknown", "4분면 분석", "blind spots", "뭘 놓치고 있지", "뭘 모르는지 모르겠어", "전략 점검", "전략 분석", "assumption check", "가정 점검", "quadrant analysis", "what am I missing". Strategy-level blind spot analysis with hypothesis-driven questioning. For requirement clarification use vague; for content-vs-form reframing use metamedium.
Classify domain (tech, finance, legal).
Uses a local model to describe something about an image
Agent skill for java-tron — TRON Protocol Java implementation (FullNode, build, run, APIs, modular architecture, custom actuators).
Transcribe audio to text using local whisper.cpp. Use when user wants to convert audio/video to text, get transcription, or speech-to-text.
Prompt for creating detailed feature implementation plans, following Epoch monorepo structure.
Template for creating new skills. Copy this file and customize for your use case.
Interactive onboarding workflow that interviews users to understand their coding goals and generates PR-ready implementation plans. Use when starting a new development task to ensure clear requirements and structured execution.