rlama
Local RAG system management with RLAMA. Create semantic knowledge bases from local documents (PDF, MD, code, etc.), query them using natural language, and manage document lifecycles. This skill should be used when building local knowledge bases, searching personal documents, or performing document Q&A. Runs 100% locally with Ollama - no cloud, no data leaving your machine.
npx skills add majiayu000/claude-skill-registry --skill rlama-tdimino-claude-code-minoan --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# RLAMA - Local RAG System **RLAMA** (Retrieval-Augmented Language Model Adapter) provides fully local, offline RAG for semantic search over your documents. ## When to Use This Skill - Building knowledge bases from local documents - Searching personal notes, research papers, or code documentation - Document-based Q&A without sending data to the cloud - Indexing project documentation for quick semantic lookup - Creating searchable archives of PDFs, markdown, or code files ## Prerequisites RLAMA requires Ollama running locally: ```bash # Verify Ollama is running ollama list # If not running, start it brew services start ollama # macOS # or: ollama serve ``` ## Quick Reference ### Query a RAG (Most Common) Query an existing RAG system with a natural language question: ```bash # Non-interactive query (returns answer and exits) rlama run <rag-name> --query "your question here" # With more context chunks for complex questions rlama run <rag-name> --query "explain the authentication flow" --context-size 30 # Show which documents contributed to the answer rlama run <rag-name> --query "what are the API endpoints?" --show-context # Use a different model for answering rlama run <rag-name> --q
- When to Use This Skill
- Prerequisites
- Quick Reference
- Query a RAG (Most Common)
- Retrieve-Only Mode (Claude Synthesizes)
- Create a RAG
- List RAG Systems
- Manage Documents
- Delete a RAG
- Advanced Features
- Web Crawling
- Directory Watching
- Website Watching
- Reranking
Verify Ollama is running ollama list If not running, start it brew services start ollama # macOS Non-interactive query (returns answer and exits) rlama run <rag-name> --query "your question here" With more context chunks for complex questions rlama run <rag-name> --query "explain the authentication flow" --context-size 30 Show which documents contributed to the answer rlama run <rag-name> --query "what are the API endpoints?" --show-context
What does the rlama skill do?
Local RAG system management with RLAMA. Create semantic knowledge bases from local documents (PDF, MD, code, etc.), query them using natural language, and manage document lifecycles. This skill should be used when building local knowledge bases, searching personal documents, or performing document Q&A. Runs 100% locally with Ollama - no cloud, no data leaving your machine.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill rlama-tdimino-claude-code-minoan --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.