Total 50,708 skills, AI & Machine Learning has 8496 skills
Showing 12 of 8496 skills
Planning agent that creates implementation plans and handoffs from conversation context
Improve skills and workflows by analyzing run artifacts and execution logs (events.jsonl/state.json) under runs/ (or OpenSpec changes/). Use when you want to iterate on skills based on real runs: find failure modes, bottlenecks, unclear prompts, missing I/O contracts, and propose concrete edits.
Generates illustrations for articles
Conduct web research and material downloading for each node. Read node-list.txt, launch multiple sub-agents to perform parallel web research on node content, deeply retrieve relevant webpages/articles/blogs/literature, download and save them locally, and output a download.txt file to record the material sources for each node. Suitable for document writing scenarios that require extensive background information, data verification, and reference sources.
Asks Gemini CLI for coding assistance. Use for getting a second opinion, code generation, debugging, or delegating coding tasks.
XGBoost gradient boosting library. Use for tabular ML.
Expert in building comprehensive AI systems, integrating LLMs, RAG architectures, and autonomous agents into production applications. Use when building AI-powered features, implementing LLM integrations, designing RAG pipelines, or deploying AI systems.
MLflow ML lifecycle management. Use for ML experiment tracking.
LlamaIndex data framework for LLMs. Use for RAG applications.
This skill should be used when user encounters "Tavily MCP error", "Tavily API key invalid", "web search not working", "Tavily failed", or needs help configuring Tavily integration.
Example skill demonstrating the Skills-as-Containers pattern with workflows, assets, and natural language routing. This is a teaching tool showing the complete PAI v1.2.0 architecture. USE WHEN user says 'show me an example', 'demonstrate the pattern', 'how do skills work', 'example skill'
Routes tasks to skills in skill-db and skill-library using semantic discovery. Triggers on specialized skill requirements, domain-specific tasks, or explicit skill requests. Uses skill-discovery, mcp-skillset, and skill-rag-router for semantic matching.