> browse/registry

> copilotkit

You are an expert in CopilotKit, the open-source framework for building in-app AI copilots. You help developers add AI-powered features to React applications — chat sidebars, AI-assisted text editing, contextual suggestions, and autonomous agents that can read app state, call actions, and modify the UI — turning any React app into an AI-native experience.

> gas-town-workflow

Gas Town style multi-agent coordination with role-mapped task routing and convoy-based work decomposition. Use when the user explicitly requests Gas Town workflow, convoy-style task decomposition, theatrical role-mapped coordination, or this repo's specific agent role system. Do not trigger on generic multi-agent or parallel task requests.

> ai-eval-ci

Run AI agent and LLM evaluations in CI/CD pipelines — automated quality gates that fail the build when AI output quality drops. Use when someone asks to "test my AI agent", "add evals to CI", "catch prompt regressions", "compare models", "evaluate LLM output quality", "set up AI quality gates", or "benchmark my agent before deploying". Covers eval frameworks (Cobalt, Promptfoo, Braintrust), LLM-as-judge scoring, threshold-based assertions, and GitHub Actions integration.

> e2b

You are an expert in E2B, the cloud platform for running AI-generated code in secure sandboxes. You help developers give AI agents the ability to execute code, install packages, read/write files, and run long processes in isolated cloud environments — each sandbox is a lightweight VM that boots in ~150ms with full Linux, filesystem, and networking.

> agent-sandbox

Run AI agent code safely in isolated sandboxes with resource limits, audit trails, and kill switches. Use when someone asks to "sandbox my agent", "run agent code safely", "add guardrails to AI agent", "isolate agent execution", "audit agent actions", "prevent agent from deleting files", "restrict agent permissions", or "add safety controls to AI coding agent". Covers Docker isolation, filesystem restrictions, network policies, resource locking, and comprehensive audit logging.

> browser-use

You are an expert in Browser Use, the Python library that lets AI agents control a web browser. You help developers build agents that can navigate websites, fill forms, click buttons, extract data, and complete multi-step web tasks — using vision and DOM understanding to interact with any website like a human would.

> crewai

You are an expert in CrewAI, the framework for orchestrating autonomous AI agents working together as a crew. You help developers define agents with specific roles, goals, and tools, then organize them into crews that collaborate on complex tasks — with sequential, parallel, and hierarchical process types, memory, delegation between agents, and integration with LangChain tools.

> openclaw

Deploy and manage OpenClaw, a self-hosted gateway bridging messaging platforms to AI coding agents. Use when a user asks to set up OpenClaw, connect WhatsApp or Telegram or Discord to an AI agent, configure multi-agent routing, schedule cron jobs in OpenClaw, set up webhooks, manage OpenClaw channels, pair a messaging account, configure heartbeats, spawn sub-agents, or troubleshoot OpenClaw gateway issues. Covers installation, channel setup, agent configuration, cron scheduling, webhooks, and su

> openai-agents

You are an expert in the OpenAI Agents SDK (formerly Swarm), the official framework for building multi-agent systems. You help developers create agents with tool calling, guardrails, agent handoffs, streaming, tracing, and MCP integration — building production-grade AI agents that coordinate, delegate tasks, and execute tools with built-in safety controls.

> session-handoff

Creates comprehensive handoff documents for seamless AI agent session transfers. Triggered when: (1) user requests handoff/memory/context save, (2) context window approaches capacity, (3) major task milestone completed, (4) work session ending, (5) user says 'save state', 'create handoff', 'I need to pause', 'context is getting full', (6) resuming work with 'load handoff', 'resume from', 'continue where we left off'. Proactively suggests handoffs after substantial work (multiple file edits, comp

> error-debugging-multi-agent-review

Use when working with error debugging multi agent review

> autonomous-multi-ai-agents

Coordinate multiple AI agents that can chat, use voice, and edit code/files/folders simultaneously while avoiding destructive collisions. Agents may share resources, but a coordinator manages turn-taking, merges, and reviews so parallel work stays fast, safe, and coherent. Agents communicate via AI chat, team chat, and collab chat channels to coordinate plans and changes.

> android-emulator-skill

Production-ready scripts for Android app testing, building, and automation. Provides semantic UI navigation, build automation, log monitoring, and emulator lifecycle management. Optimized for AI agents with minimal token output.

> agent-reach

Give your AI agent eyes to see the entire internet. 7500+ GitHub stars. Search and read 14 platforms: Twitter/X, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu (小红书), Douyin (抖音), Weibo (微博), WeChat Articles (微信公众号), LinkedIn, Instagram, RSS, Exa web search, and any web page. One command install, zero config for 8 channels, agent-reach doctor for diagnostics. Use when: (1) user asks to search or read any of these platforms, (2) user shares a URL from any supported platform, (3) user asks to sear

> ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output.

> agentic-eval

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

Build conversational multi-agent systems with Microsoft AutoGen.

Build collaborative AI agent teams with CrewAI. Roles, tasks, and crew orchestration.

> agent-browser

Headless browser automation CLI optimized for AI agents with accessibility tree snapshots and ref-based element selection

> bug-bounty

Complete bug bounty workflow — recon (subdomain enumeration, asset discovery, fingerprinting, HackerOne scope, source code audit), pre-hunt learning (disclosed reports, tech stack research, mind maps, threat modeling), vulnerability hunting (IDOR, SSRF, XSS, auth bypass, CSRF, race conditions, SQLi, XXE, file upload, business logic, GraphQL, HTTP smuggling, cache poisoning, OAuth, timing side-channels, OIDC, SSTI, subdomain takeover, cloud misconfig, ATO chains, agentic AI), LLM/AI security test