Total 56,723 skills, AI & Machine Learning has 9434 skills
Showing 12 of 9434 skills
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.
Get a second opinion from Gemini on code, architecture, debugging, or security. Uses gemini-coach CLI with AI-to-AI prompting for clear, actionable analysis. Trigger with 'ask gemini', 'gemini review', 'second opinion', 'peer review', or 'consult gemini'.
Guide for creating effective agent skills. Use PROACTIVELY when creating new skills or refactoring bloated ones. Teaches progressive disclosure, 200-line rule, and 3-tier loading system.
Configure LangChain local development workflow with hot reload and testing. Use when setting up development environment, configuring test fixtures, or establishing a rapid iteration workflow for LangChain apps. Trigger with phrases like "langchain dev setup", "langchain local development", "langchain testing", "langchain development workflow".
Create phased implementation plans from research findings. Invoke when a task needs an implementation plan.
Integrate OpenAI Agents SDK with You.com MCP server - Hosted and Streamable HTTP support for Python and TypeScript. - MANDATORY TRIGGERS: OpenAI Agents SDK, OpenAI agents, openai-agents, @openai/agents, integrating OpenAI with MCP - Use when: developer mentions OpenAI Agents SDK, needs MCP integration with OpenAI agents
Build complete document knowledge bases with PDF text extraction, OCR for scanned documents, vector embeddings, and semantic search. Use this for creating searchable document libraries from folders of PDFs, technical standards, or any document collection.
Initialize and configure LangGraph projects with proper structure, langgraph.json configuration, environment variables, and dependency management. Use when users want to (1) create a new LangGraph project, (2) set up langgraph.json for deployment, (3) configure environment variables for LLM providers, (4) initialize project structure for agents, (5) set up local development with LangGraph Studio, (6) configure dependencies (pyproject.toml, requirements.txt, package.json), or (7) troubleshoot project configuration issues.
Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when: keywords, file_patterns, code_patterns.
Run LLMs and AI models on Cloudflare's GPU network with Workers AI. Includes Llama 4, Gemma 3, Mistral 3.1, Flux images, BGE embeddings, streaming, and AI Gateway. Handles 2025 breaking changes. Prevents 7 documented errors. Use when: implementing LLM inference, images, RAG, or troubleshooting AI_ERROR, rate limits, max_tokens, BGE pooling, context window, neuron billing, Miniflare AI binding, NSFW filter, num_steps.
Use when creating, updating, or improving agent skills.
Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems. The challenge isn't just speech recognition and synthesis, it's achieving natural conversation flow with sub-800ms latency while handling interruptions, background noise, and emotional nuance. This skill covers two architectures: speech-to-speech (OpenAI Realtime API, lowest latency, most natural) and pipeline (STT→LLM→TTS, more control, easier to debug). Key insight: latency is the constraint. Hu