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Found 6,689 Skills
Implementation guide for 17+ agentic AI architectures using LangChain and LangGraph for building sophisticated AI agents
A curated collection of research papers and resources on agentic reasoning for Large Language Models, organized by planning, tool use, search, self-evolution, and multi-agent systems.
Guides engineering of multi-agent systems—agent roles and specialization, orchestration topologies (supervisor, peer-to-peer, hierarchical, blackboard), task decomposition and routing, inter-agent messaging (A2A-style patterns), shared vs partitioned state, fan-out/fan-in and DAG workflows, synchronization and consensus, conflict resolution, fault tolerance and retries across agents, cost/latency/token budgets, cross-agent observability, testing multi-agent flows, and deployment (queues, durable workflows). Framework-agnostic; high-level LangGraph, Deep Agents, and agenthub—not single-agent loops (agentic-ai-developer), ML training (ai-engineer), strategy-only whiteboard (enterprise-strategist), or PM planning (technical-program-manager). Use for multi-agent system, multi-agent engineer, agent orchestration, supervisor agent, agent topology, fan-out fan-in, agent handoff protocol, multi-agent workflow, agent coordination, blackboard pattern, hierarchical agents, A2A, agent DAG, multi-agent architecture.
Select and configure evaluation metrics for an AI agent. Guides through metric selection using use-case recommendations, custom LLM-based metric creation with prompt engineering, and agent default attachment. Use when user says "set up metrics", "configure metrics", "create a metric", "what metrics should I use", "add evaluation criteria", or "customize scoring".
Reference and consulting skill for OpenClaw — a messaging gateway that connects AI agents to multiple communication platforms (Telegram, Discord, Slack, WhatsApp, iMessage, and more). Use when working with OpenClaw configuration, channels, Gateway setup, skills, cron jobs, MCP servers, memory, OAuth, or troubleshooting. Also use when the user asks how to implement a use case on their OpenClaw bot (daily morning brief, research workflows, competitive radar, decision playbook), how to add a new channel, or how to connect the CodeAlive context engine. Triggers on requests like "configure openclaw", "add Discord to my bot", "set up morning brief", "gateway not starting", "connect CodeAlive search", "OAuth re-auth", or any close paraphrase. Companion of install-openclaw-to-yc — install both together.
Call the vss agent to run video understanding on video to answer a text question. Use when the user asks about video content, or about visual details that cannot be answered from conversation history, search hits, or metadata alone.
MCP server for real-time Three.js scene inspection, material editing, shader debugging, and performance monitoring from AI agents
Agent Design Consultant and Review Tool. Based on 12-Factor AgentOps best practices, it is used for: (1) Discussing Agent architecture design solutions; (2) Reviewing the design of existing Agents/Skills/workflows, identifying issues, and providing improvement suggestions. Trigger phrases: Review my agent, Help me analyze this skill, Agent design, Agent optimization, Help me review this workflow, What's wrong with this agent, How to design an agent, Agent architecture consultation.
This skill is used when users want to add QQ platform support to the official main branch of Hermes Agent, or explicitly mention requests like "add QQ channel to hermes main", "install QQ support as a skill to Hermes", or "enable the official version of Hermes to support QQ and file sending". This skill will update the current repository to a version that supports QQ Bot, QQ file sending, QQ platform configuration, and toolset integration.
Use when tasks are complex and require full microservices collaboration: The main agent acts as a pure Orchestrator, strictly prohibited from writing code personally, and is responsible for accurately assigning responsibilities such as positioning, planning, coding, testing, and review to corresponding sub-agents (explorer, planner, worker, verifier, reviewer, fixer). This Skill enforces microservices workflow discipline, requiring full Chinese communication, minimal routing output, and minimized context transfer.
Scaffold the Mimas agent instruction file tree for any repository — AGENTS.md at root, subdomain CONTEXT.md files, and the full agents-docs/ hierarchy (a sibling of any existing docs/, kept separate so human-maintained project docs stay untouched). Every file is tailored to the repo's actual tech stack, git platform, and conventions. Use this skill whenever someone wants to set up agent instructions, onboard a repo for AI-assisted development, add AGENTS.md / CONTEXT.md files, create engineering docs for agents, or mentions "set up agentic repository" or "mimas template". Even if they just say "set up this repo for agents" or "add agent docs", this is the skill to use.
Build a complete agent-readable Obsidian vault for a Tailwind-based web codebase, eight flat top-level domain docs (PRODUCT/RUNTIME/ARCHITECTURE/DATA/AUTH/ENGINEERING/TESTING/DESIGN), folder-level deep specs, bidirectional wikilinks for graph navigation, and a `DESIGN.md` that conforms to the google-labs-code/design.md spec with tokens derived from `tailwind.config.{ts,js}` or the v4 `@theme` block. Use when asked to "set up project docs", "write project documentation", "create an Obsidian vault from this repo", "document this codebase for agents", "add a DESIGN.md", or "make the design system machine-readable".