Agent Skills Hub

chrome-devtools-mcp-manager

Manage OpenClaw's built-in Chrome browser and chrome-devtools-mcp integration for robust browser automation using the Model Context Protocol.

agent-system

Implementation patterns for MERIDIAN autonomous AI agents using Claude API, including BaseAgent lifecycle, structured tool use, token budget enforcement, and cron scheduling.

tasks-documentation

A specialized skill for generating high-quality technical documentation, code comments, API specs, and README patterns. Automates standard documentation workflows for C# and TypeScript projects.

zarr-python

Python skill for high-performance storage of chunked N-dimensional arrays using Zarr, supporting cloud storage (S3/GCS), parallel I/O, and integration with NumPy, Dask, and Xarray.

bookstack

Manage your knowledge base on BookStack programmatically. Perform CRUD operations on books, chapters, and pages, and conduct full-text searches via the REST API.

netbox-integration-best-practices

Best practices for building integrations with NetBox REST and GraphQL APIs. Optimize performance, authentication, and architectural patterns for NetBox automations.

tdd-workflow

A rigorous TDD workflow agent that enforces test-first development, ensuring 80%+ code coverage across unit, integration, and E2E tests for features, bug fixes, and refactoring.

building-ai-agent-on-cloudflare

Build stateful AI agents on Cloudflare Workers using the Agents SDK. Features real-time WebSockets, persistent state management, scheduled background tasks, and native tool integration for production-ready deployments.

pytest-runner

Automate pytest execution with built-in environment verification, failure analysis, coverage reporting, and intelligent test discovery.

jupyter-notebook-testing

Test Adobe EDS blocks interactively in the browser with Jupyter notebooks. Features ES6 imports, overlay previews, responsive device testing, and zero-dependency execution.

temporal

Automate the migration of Netflix Conductor workflows to Temporal Python, including server orchestration, worker management, and workflow troubleshooting.

rag-implementation

Build production-grade RAG systems using vector databases, semantic search, and LangGraph to ground LLMs in external knowledge.