Agent Skills Hub

ring:writing-skills

A framework for applying Test-Driven Development to process documentation, ensuring agent reliability by using pressure scenarios to identify and patch rationalization loopholes.

project-migrate

Intelligently migrate existing brownfield projects to the AgenticDev structure using AI-powered analysis to reorganize documentation, generate rich frontmatter, and preserve git history.

nia

Research agent for Nia: index/search remote codebases, docs, and packages. Optimizes AI context by prioritizing full source indexing over web fetches to reduce hallucinations.

claude-starter

An AI-powered skill that automatically retrieves relevant project context from your RAG knowledge base for complex coding tasks.

skill-engineer

Orchestrates multi-agent iterative refinement for high-quality OpenClaw skill development, ensuring rigorous testing and lifecycle management.

rag-implementation

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

wiki-architect

Analyzes codebases to generate hierarchical documentation, onboarding guides, and architectural mapping, helping teams understand and document their projects efficiently.

brave-search

Headless web search and content extraction using Brave Search API. Perform documentation lookups, factual research, and web data retrieval without a browser.

writing-skills

A Test-Driven Development (TDD) framework for writing agent skills, using pressure scenarios to ensure documentation guides agent behavior effectively.

content-research-writer

Your collaborative writing partner for research, outlining, drafting, and feedback. Perfect for technical documentation, blog posts, and articles with proper citation management.

chromium-docs

Search and reference Chromium documentation, including design docs, APIs, and development guides. Use to locate, browse, or learn about architecture, GPU, network, security, and testing concepts within the Chromium codebase.

basic-usage

Essential guide to llmemory for document storage and search: installation, database setup with pgvector, document ingestion, hybrid/semantic retrieval, and building RAG systems with multi-tenant support.