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Found 2,484 Skills
Patterns and architectures for building AI agents and workflows with LLMs. Use when designing systems that involve tool use, multi-step reasoning, autonomous decision-making, or orchestration of LLM-driven tasks.
Coordinate AI agent teams via a Kanban task board with local JSON storage. Enables multi-agent workflows with a Team Lead assigning work and Worker Agents executing tasks via heartbeat polling. Perfect for building AI agent command centers.
Create and improve OpenAkita skills. It is used when you need to: (1) create new skills for repetitive tasks, (2) improve existing skills, (3) encapsulate temporary scripts into reusable skills. Skills are the core mechanism of OpenAkita's self-evolution.
Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.
Load groove workflow context into the conversation. Run at the start of every session.
Build AI agents and automate Claude Code programmatically using the Claude Agent SDK and headless CLI mode. Use this skill when you need to build an agent, create a Claude agent, make a bot, work with the agent SDK, run Claude in headless mode, write programmatic agent code, automate with Claude, create an MCP server builder, or query Claude programmatically. Covers the Python SDK, the claude -p headless interface, custom tool creation with SDK MCP servers, hooks for deterministic control, session management, and CLI flag reference. Authentication uses existing ~/.claude/ config — no API keys required.
This skill should be used when the user asks to "set up agent todos", "initialize the todo store", "configure todos", "run todo init", "set up .agent-todos.local.json", or wants to set up or reconfigure the todo store for this project.
A meta-skill that understands task requirements, dynamically selects appropriate skills, tracks successful skill combinations using agent-memory-mcp, and prevents skill overuse for simple tasks.
Expert skill for using OpenViking, the open-source context database for AI Agents that manages memory, resources, and skills via a filesystem paradigm.
Skill Map Viewer. Scans all installed skills and renders a visual overview — you can check the name, version, description, and category at a glance. This tool is triggered when the user says 'skills', '技能', '技能地图', 'skill map', '我有哪些技能', '看看技能', '列出技能', 'list skills'. It also activates when the user asks about available or installed skills.
CrewAI task design and configuration. Use when creating, configuring, or debugging crewAI tasks — writing descriptions and expected_output, setting up task dependencies with context, configuring output formats (output_pydantic, output_json, output_file), using guardrails for validation, enabling human_input, async execution, markdown formatting, or debugging task execution issues.
Creates detailed, sectionized, TDD-oriented implementation plans through research, stakeholder interviews, and multi-LLM review. Use when planning features that need thorough pre-implementation analysis.