Total 50,539 skills, AI & Machine Learning has 8483 skills
Showing 12 of 8483 skills
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.
Deep Agents framework — architectural decisions (when to use Deep Agents vs alternatives, backend strategies, subagent design, middleware approaches) AND code review (bugs, anti-patterns, improvements when reviewing Deep Agents code). Use when working with Deep Agents — designing a new system or reviewing existing code.
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
Pre-build reality check for AI coding agents — scan GitHub, HN, npm, PyPI, Product Hunt to validate ideas before building
Configure, extend, or contribute to Hermes Agent.
Play Pokemon via headless emulator + RAM reads.