Total 54,046 skills, AI & Machine Learning has 8990 skills
Showing 12 of 8990 skills
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.
Build agentic UIs using AG-UI protocol with Pydantic AI (Python backend) and CopilotKit (React/Next.js frontend). Use when creating AI-powered applications that need bidirectional agent-UI communication, shared state between frontend and backend, human-in-the-loop workflows, tool-based generative UI, or predictive state updates. Triggers on requests involving CopilotKit hooks (useCoAgent, useCopilotAction, useCoAgentStateRender), pydantic_ai with ag_ui adapters, or building chat interfaces with backend AI agents.
Local MCP memory server for AI coding assistants with verbatim recall, semantic search, and automatic session capture
Jailbreak LLMs: Parseltongue, GODMODE, ULTRAPLINIAN.
Decomposition playbook + anti-temptation rules for an orchestrator profile routing work through Kanban. The "don't do the work yourself" rule and the basic lifecycle are auto-injected into every kanban worker's system prompt; this skill is the deeper playbook when you're specifically playing the orchestrator role.
Extract text from images using OCR and vision AI with the performOCR() high-level API or the full VisionPipeline.
Human-in-the-loop safety controls — approval routing via human, LLM judge, or auto-approve with guardrail overrides.
Text-to-speech synthesis via Google Cloud Text-to-Speech API — MP3 output, configurable language and voice, voice listing.
On-device wake-word detection via Picovoice Porcupine — privacy-preserving, configurable built-in keywords, per-keyword sensitivity tuning.
Export and import agent configurations for sharing and backup
Speaker diarization — identifies and tracks who is speaking at each moment in an audio stream, using provider-delegated labels or local offline clustering.
Use when reviewing how skills performed during a session, when the user wants to analyze skill invocations and identify improvements, or when the user says "skill retro", "review skills", "how did skills do", "improve this skill", or "skill retrospective".