Total 32,885 skills, AI & Machine Learning has 5309 skills
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Package a agent skill into a complete GitHub repository ready for distribution via skills.sh. Generates README, LICENSE, plugin.json, marketplace.json, .gitignore, and the proper directory structure. Optionally initializes a git repo and creates a GitHub repository. This skill should be used when publishing a skill, packaging a skill for distribution, preparing a skill repo, or when the user says 'publish skill', 'package skill', 'release skill', '发布技能', '打包 skill'.
Use Collabute MCP for organization-specific context retrieval and proposal-safe actions. Trigger when users ask about meetings, memory, Linear, Slack, or Vercel data from their Collabute workspace, or ask to create tasks from meeting context.
Official Notion Model Context Protocol Server for workspace interaction.
Frames coding-agent work sessions with explicit intent capture and drift monitoring. Use when a session transitions from planning/Q&A to implementation for coding tasks, refactors, feature builds, bug fixes, or other multi-step execution where scope drift is a risk.
Initializes and maintains architecture artifacts with handoff-first context loading, lazy scoped updates, and compact JSON handoff output for workflow-driven invocations.
Implements tracker subtasks tagged `implement`, publishes/updates the PR, and routes review using handoff-first context loading, lazy artifact reads, and rework_mode support.
N coordinated agents on shared task list (compatibility facade over team)
Searches and retrieves MLflow documentation from the official docs site. Use when the user asks about MLflow features, APIs, integrations (LangGraph, LangChain, OpenAI, etc.), tracing, tracking, or requests to look up MLflow documentation. Triggers on "how do I use MLflow with X", "find MLflow docs for Y", "MLflow API for Z".
Analyzes an MLflow session — a sequence of traces from a multi-turn chat conversation or interaction. Use when the user asks to debug a chat conversation, review session or chat history, find where a multi-turn chat went wrong, or analyze patterns across turns. Triggers on "analyze this session", "what happened in this conversation", "debug session", "review chat history", "where did this chat go wrong", "session traces", "analyze chat", "debug this chat".
Use this when you need to EVALUATE OR IMPROVE or OPTIMIZE an existing LLM agent's output quality - including improving tool selection accuracy, answer quality, reducing costs, or fixing issues where the agent gives wrong/incomplete responses. Evaluates agents systematically using MLflow evaluation with datasets, scorers, and tracing. Covers end-to-end evaluation workflow or individual components (tracing setup, dataset creation, scorer definition, evaluation execution).
Analyzes a single MLflow trace to answer a user query about it. Use when the user provides a trace ID and asks to debug, investigate, find issues, root-cause errors, understand behavior, or analyze quality. Triggers on "analyze this trace", "what went wrong with this trace", "debug trace", "investigate trace", "why did this trace fail", "root cause this trace".
Instruments Python and TypeScript code with MLflow Tracing for observability. Triggers on questions about adding tracing, instrumenting agents/LLM apps, getting started with MLflow tracing, or tracing specific frameworks (LangGraph, LangChain, OpenAI, DSPy, CrewAI, AutoGen). Examples - "How do I add tracing?", "How to instrument my agent?", "How to trace my LangChain app?", "Getting started with MLflow tracing", "Trace my TypeScript app"