Total 50,341 skills, AI & Machine Learning has 8461 skills
Showing 12 of 8461 skills
Ingest video, audio, PDF, book, screenshot, and GitHub repo content into the brain. Multi-format handling with entity extraction and backlink propagation. Covers video-ingest, youtube-ingest, and book-ingest subtypes.
Compress an agent's routing file (RESOLVER.md or AGENTS.md) by converting granular skill-per-row tables into functional-area dispatchers. Each area lists sub-skills in a "(dispatcher for: ...)" clause. The LLM reads one area entry and routes to the correct sub-skill. Proven via held-out A/B eval: dispatcher pattern outperforms naive pipe-table compression.
Always-on ambient signal capture. Fires on every inbound message to detect original thinking and entity mentions. Spawn as a cheap sub-agent in parallel, never block the main response.
Route content to specialized ingestion skills. Detects input type and delegates.
Take any book (EPUB/PDF), produce a personalized chapter-by-chapter analysis with two-column tables. Left column preserves the chapter content; right column maps every idea to the reader's actual life using brain context. Output is a single brain page at media/books/<slug>-personalized.md plus an optional PDF via brain-pdf.
Multi-agent trading analysis for a stock ticker. Runs technical, news, fundamentals, and macro analysts in parallel, then an adversarial bull/bear debate, then Research Manager, Trader, and Portfolio Manager to produce a final BUY/SELL/HOLD decision with entry, stop, and sizing.
Using the Pi terminal agent — workspace setup, sessions, /commands, compaction, settings.json/AGENTS.md, skill discovery, providers/models, plus theme/keybinding/prompt customization (SYSTEM.md, APPEND_SYSTEM.md, settings.json, keybindings.json). Use for any "how do I configure/run Pi" question.
Use this skill whenever the user is working with the Pydantic AI framework — including building AI agents, defining structured outputs with Pydantic models, wiring up tools/function calling, configuring model providers (OpenAI, Anthropic, Gemini, etc.), managing dependencies via agent context, handling streaming responses, or debugging agent runs. Trigger this skill even for adjacent tasks like "how do I make my agent return JSON", "set up a multi-step agent", "add a tool to my agent", or "validate LLM output with Pydantic" — any time Pydantic AI is mentioned or implied as the target framework.
Build LLM-powered chat apps with the right SDK — Anthropic SDK / Claude API (prompt caching, thinking, tool use, batch, files, citations, memory, model migrations) AND Vercel AI SDK (useChat, streamText, tool calls, UIMessage, ChatStatus, addToolOutput). Use when implementing chat interfaces, tuning Claude features, migrating between Claude model versions, or wiring up streaming with @ai-sdk/react.
MCP server for AI image & video generation with 9 models (GPT Image 2, Nanobanana 2, Flux 2, Midjourney V8.1, Veo 3.1, local ComfyUI), 1,446 curated prompts, and parallel batch orchestration
Execute Python code in isolated rootless containers with MCP server proxying for token-efficient agent workflows
Expert in using ktx, the executable context layer for data and analytics agents that enables accurate querying through MCP with skills, memory and a semantic layer