> browse/registry

> 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.

> picoclaw

Set up, configure, and manage PicoClaw — an ultra-lightweight personal AI assistant built in Go. Use when the user mentions "picoclaw," "pico claw," "lightweight AI assistant," or wants to deploy a personal AI agent on low-resource hardware (Raspberry Pi, RISC-V boards). Covers installation, LLM provider configuration, messaging gateway setup (Telegram, Discord, Slack, LINE, DingTalk), scheduled tasks, heartbeat, workspace layout, security sandbox, and Docker deployment.

> pydantic-ai

You are an expert in PydanticAI, the Python agent framework built by the Pydantic team. You help developers create type-safe AI agents with structured outputs, dependency injection, tool definitions, streaming, and model-agnostic design — leveraging Pydantic for validation and type safety throughout the agent lifecycle.

> 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.

> 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.

> claude-code

Build software with Claude Code, the AI-powered CLI coding agent. Use when a user asks to set up Claude Code, configure CLAUDE.md project files, use slash commands, manage permissions, create hooks, or optimize agentic coding workflows.

> 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.

> browserbase

You are an expert in BrowserBase, the cloud platform for running headless browsers at scale. You help developers deploy browser-based automations, AI agents, and web scraping pipelines using managed Chromium instances with residential proxies, session recording, stealth mode, and parallel execution — without managing browser infrastructure.

> 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.

> ai-pentesting

Run autonomous AI-driven penetration tests on web applications using tools like Shannon, PentAGI, and similar frameworks. Use when tasks involve setting up automated penetration testing pipelines, combining AI agents with security tools (nmap, subfinder, nuclei, sqlmap), building autonomous exploit chains, generating pentest reports with proof-of-concept exploits, or integrating AI pentesting into CI/CD pipelines. Covers the full pentest lifecycle from reconnaissance to reporting using AI orches

> 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.

> smolagents

You are an expert in smolagents, Hugging Face's minimalist agent framework. You help developers build AI agents that write and execute Python code to solve tasks, use tools from the Hugging Face Hub, chain multiple agents together, and run on any LLM (OpenAI, Anthropic, local models) — providing a simple, code-first approach to building agents without complex abstractions.

> ag2

You are an expert in AG2 (formerly AutoGen), the open-source multi-agent conversation framework. You help developers build systems where multiple AI agents collaborate through structured conversations — with tool use, human-in-the-loop, code execution, group chat orchestration, and nested conversations — for complex tasks like software development, research, and data analysis.

> agent-swarm-orchestration

Coordinate multiple AI agents working together on complex tasks — routing, handoffs, consensus, memory sharing, and quality gates. Use when tasks involve building multi-agent systems, coordinating specialist agents in a pipeline, implementing agent-to-agent communication, designing swarm architectures, setting up agent orchestration frameworks, or building autonomous agent teams with supervision and quality control. Covers hierarchical, mesh, and pipeline topologies.

> dmux-workflows

Multi-agent orchestration using dmux (tmux pane manager for AI agents). Patterns for parallel agent workflows across Claude Code, Codex, OpenCode, and other harnesses. Use when running multiple agent sessions in parallel or coordinating multi-agent development workflows.

> agent-designer

Use when the user asks to design multi-agent systems, create agent architectures, define agent communication patterns, or build autonomous agent workflows.

> langchain-architecture

Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.

> data-analyst

Data visualization, report generation, SQL queries, and spreadsheet automation. Transform your AI agent into a data-savvy analyst that turns raw data into actionable insights.

> ai-first-engineering

团队中人工智能代理生成大部分实施输出的工程运营模型。

> model-hierarchy

Cost-optimize AI agent operations by routing tasks to appropriate models based on complexity. Use this skill when: (1) deciding which model to use for a task, (2) spawning sub-agents, (3) considering cost efficiency, (4) the current model feels like overkill for the task. Triggers: "model routing", "cost optimization", "which model", "too expensive", "spawn agent".