Total 50,543 skills, AI & Machine Learning has 8484 skills
Showing 12 of 8484 skills
Automate content creation from research to video generation using AI-powered content pipeline with Claude, OpenAI, and Remotion
Apply when context is filling up: large outputs, long files, repeated reads, fan-out planning. Route bulk to subagents; keep summaries in the main thread, not raw payloads.
Use when asked to install, deploy, run, validate, troubleshoot, or stop NVIDIA AI-Q Blueprint infrastructure.
Router for NVIDIA NuRec/NRE: USDZ rendering, NCore conversion, 3DGS, gRPC sensor sim, PhysicalAI HF datasets. Do NOT use for SimReady or infra setup.
Designs, builds, debugs, and documents OpenClaw workflows, skills, and AI assistant configurations. Use when the user mentions "OpenClaw," "personal AI assistant," "local AI," "ClawdHub," "openclaw skills," "chat platform AI," or wants to set up AI assistants across WhatsApp, Telegram, Discord, or Slack.
Building & extending Pi — authoring TypeScript extensions (ExtensionAPI, registerTool, registerProvider, /commands, UI hooks), publishing as npm/git packages (pi-package), embedding via JSON-RPC mode (--mode rpc/json, JSONL framing, AgentSession SDK), and developing inside the pi_agent_rust repo. Use for any "how do I build a Pi extension/package/SDK client" question.
When the user wants to build or improve a sales bot's ability to orchestrate SMS, email, voice, and chat without overwhelming prospects. Also use when the user mentions "omnichannel," "cross-channel," "channel orchestration," "multi-touch sequences," or "coordinating outreach."
Install context files from registry. Use when user runs /install-context, says "install context", "setup context", or when context is missing and the user needs to get started.
Use for 'why does X work this way', 'why we picked Y', design rationale, regressions, postmortems, or data-backed thresholds. Discovers available MCPs and queries each evidence category (source control, issue tracker, long-form docs, real-time chat, infrastructure observability, error tracking, product analytics warehouse) in parallel, then returns a cited read on decisions and tradeoffs. Use how for runtime behavior.
Build and maintain an executable context layer for data and analytics agents using ktx's semantic layer, wiki knowledge, and MCP integration
Token-efficient MCP adapter for Pi coding agent that enables MCP server integration without burning context window
Execute Python code in isolated rootless containers with MCP server proxying to reduce context bloat from 30K to 200 tokens