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Found 2,167 Skills
Initialize a new repository with AGENTS.md
High-fidelity HTML design and prototype creation skill for AI coding agents — slide decks, interactive prototypes, landing pages, UI mockups, animations, and brand style cloning.
Generative ideation engine. Takes a domain, trend, question, or constraint and produces 15-30 novel possibilities — things that might be true, businesses that could exist, futures that could unfold. Spawns a team of 6 specialist agents — Signal Scout, Analogist, Inverter, Combinator, Contrarian, Futurist — who each generate ideas from a distinct creative angle. The lead cross-pollinates across agents, finds unexpected combinations, and ranks the output by novelty × plausibility. Use when the user says "brainstorm", "what could exist", "what's possible", "generate ideas", "what might be true", "possibilities", or presents a domain and wants divergent exploration rather than evaluation of a specific idea.
Install and manage AI agent skills from Python/JS libraries so agents always use up-to-date patterns
A building experience: create, test, validate, refine, and publish extraction workflows based on existing or new Nimble agents. For users who want to invest in a durable, reusable workflow for a specific domain — not get data immediately. Trigger phrases: "set up extraction for X site", "I need to extract from this site regularly", "build an agent for", "create a reusable scraper", "generate a Nimble agent", "refine my agent", "add a field to my agent", or when the user wants to run extraction at scale. For getting data immediately, use nimble-web-expert instead.
Maintain repository integrity and documentation. Use for auditing structure, checking config validity, and reviewing inventory. Use proactively to validate the repository or sync documentation. Examples: - user: "Validate the repo" → run audit_repo.py - user: "Check agents" → run audit_repo.py, review errors - user: "Update documentation" → run sync_docs.py - user: "Check for issues" → run full audit
Designs and refactors software codebases to be AI-friendly by aligning the filesystem with domain/feature boundaries, creating deep (greybox) modules with small public interfaces, enforcing import boundaries, and tightening tests/feedback loops. Use when the user asks to "make the codebase AI-ready", "reduce coupling", "introduce deep modules", "create module boundaries", "restructure folders by feature", "define service interfaces", or "plan a refactor + tests so AI agents can work safely".
Configure AI agents via the imperative SDK / REST API — for no-code dashboard setups, webhook-based tools, and knowledge bases.
Deploy and manage security hardening for high-privilege autonomous AI agents (OpenClaw) using zero-trust architecture and automated defense matrices
Production-grade engineering skills for AI coding agents - lifecycle commands, workflow automation, and best practices for software development.
Run a local observability and control dashboard for OpenClaw AI agents with real-time collaboration, task management, and safety-first defaults.
Train personalized AI agents with reinforcement learning from conversational feedback using OpenClaw-RL's async framework