Loading...
Loading...
Found 1,686 Skills
Explore-first wave pipeline. Decomposes requirement into exploration angles, runs wave exploration via spawn_agents_on_csv, synthesizes findings into execution tasks with cross-phase context linking (E*→T*), then wave-executes via spawn_agents_on_csv.
Use when working with Anthropic Claude Agent SDK. Provides architecture guidance, implementation patterns, best practices, and common pitfalls.
Create new Agent Skills from templates with best-practice structure, pre-populated SKILL.md, and optional scripts/assets directories.
Run the sefirot loop and confirm with the user if there are any questions
Integration patterns for Mapbox MCP Server in AI applications and agent frameworks. Covers runtime integration with pydantic-ai, mastra, LangChain, and custom agents. Use when building AI-powered applications that need geospatial capabilities.
Interact with the Paperclip control plane API to manage tasks, coordinate with other agents, and follow company governance. Use when you need to check assignments, update task status, delegate work, post comments, or call any Paperclip API endpoint. Do NOT use for the actual domain work itself (writing code, research, etc.) — only for Paperclip coordination.
OpenClaw learning expert that retrieves and synthesizes information from official documentation (https://docs.openclaw.ai) and GitHub repository (https://github.com/openclaw/openclaw). Use this skill whenever the user asks questions about OpenClaw, including installation, configuration, API usage, concepts, troubleshooting, best practices, or any OpenClaw-related inquiries. Triggers include OpenClaw questions about features, implementation, usage, setup, or any openclaw-related topics.
Register AI agents on Ethereum mainnet using ERC-8004 (Trustless Agents). Use when the user wants to register their agent identity on-chain, create an agent profile, claim an agent NFT, set up agent reputation, or make their agent discoverable. Handles bridging ETH to mainnet, IPFS upload, and on-chain registration.
Apply DriveMind, the calm reliability layer for AI agents. Use when a task needs steady follow-through, clearer progress, stronger persistence without recklessness, explicit safety boundaries, human-in-the-loop collaboration, post-task review, reusable memory, or when the user says things like 'keep pushing', 'don’t stop too early', 'be steady', 'if risk is unclear ask me', 'review this after', or 'write down the lesson'.
Patterns for building AI agents that learn from their own execution, detect failure modes, and improve autonomously. Covers feedback loops, performance regression detection, memory curation, skill extraction, and meta-learning architectures. Use when building agents that need to get better over time, managing auto-memory, or designing self-correcting systems.
A guided, zero-friction installer and maintenance assistant for OpenClaw. Use this skill when the user wants to install OpenClaw, set up OpenClaw on a local machine or remote server, connect OpenClaw to DingTalk, get OpenClaw skill recommendations for their use case, or perform post-installation maintenance (health checks, troubleshooting, installing new skills, changing AI models, adding chat channels, updating OpenClaw). Handles full environment detection, installation, optional DingTalk integration, scene-based skill recommendations, and daily maintenance — all interactively, with no wasted steps.
Uses persistent markdown files for general planning, progress tracking, and knowledge storage (Manus-style workflow). Use for multi-step tasks, research projects, or general organization WITHOUT mentioning PRD. For PRD-specific work, use prd-planner skill instead.