Total 54,043 skills, AI & Machine Learning has 8989 skills
Showing 12 of 8989 skills
Implements high-performance local machine learning inference in the browser using ONNX Runtime Web. Use this skill when the user needs privacy-first, low-latency, or offline AI capabilities (e.g., image classification, object detection, or NLP) without server-side processing.
Enables Claude to create, manage, and analyze Microsoft Forms surveys and quizzes via Playwright MCP
Enables Claude to create, organize, and manage notes in Microsoft OneNote via Playwright MCP
Use when working with AWS Strands Agents SDK or Amazon Bedrock AgentCore platform for building AI agents. Provides architecture guidance, implementation patterns, deployment strategies, observability, quality evaluations, multi-agent orchestration, and MCP server integration.
Design MCP resources to expose content for LLM consumption. Use when creating static or dynamic resources in xmcp.
PocketFlow framework for building LLM applications with graph-based abstractions, design patterns, and agentic coding workflows
Proactively summarize and consolidate knowledge from AI conversation sessions. Auto-triggers when: (1) Starting a new session after meaningful previous work, (2) Session contains significant learnings worth preserving. Captures debugging insights, architecture decisions, patterns, configs, and lessons learned into structured knowledge documents. Explicit triggers: 'summarize', 'consolidate', 'save knowledge', 'document this'.
Inspect observation space and inventory encoding.
Exa AI-native semantic search via Composio API. Use when: (1) Searching the web with natural language queries (2) Getting citation-backed answers to research questions (3) Finding pages similar to a given URL (4) Retrieving full content from search results Exa understands meaning - queries don't need exact keyword matches.
Operate and evolve agent-memory-workbench with replay-first memory, minimal JSON edits, and a strict two-branch policy (normal + human-verification).
This skill generates a structured chapter outline for intelligent textbooks by analyzing course descriptions, learning graphs, and concept dependencies. Use this skill after the learning graph has been created and before generating chapter content, to design an optimal chapter structure that respects concept dependencies and distributes content evenly across all of the chapter in a book.
Audit, clean, and optimize Clawdbot's vector memory (LanceDB). Use when memory is bloated with junk, token usage is high from irrelevant auto-recalls, or setting up memory maintenance automation.