Agent Skills for local RAG (Retrieval-Augmented Generation) using memvid-sdk.
These skills follow the Agent Skills specification so they can be used by any skills-compatible agent, including Claude Code and Codex CLI.
Install via Claude Code plugin marketplace:
/plugin marketplace add fa-ina-tic/memvid-rag
/plugin install memvid-rag@memvid-rag-skills
Place the contents of this repository in a /.claude folder at your project root. See the Claude Skills documentation for details.
Copy the skills/ directory to ~/.codex/skills following the Agent Skills specification.
- Python 3.8+
- memvid-sdk: Install with
pip install memvid-sdk - API Key (for cloud embeddings): Set one of the following environment variables:
COHERE_API_KEY- Cohere (default)OPENAI_API_KEY- OpenAIVOYAGE_API_KEY- Voyage AINVIDIA_API_KEY- NVIDIA
This plugin enables semantic search over PDF documents:
| Command | Description |
|---|---|
/memvid-rag:create |
Create a new knowledge.mv2 index file |
/memvid-rag:index |
Index PDF documents with vector embeddings |
/memvid-rag:search |
Search indexed documents semantically |
/memvid-rag:status |
Show RAG system statistics |
Initialize a new knowledge base:
/memvid-rag:create
Add PDF documents to the knowledge base:
/memvid-rag:index ./documents/research-paper.pdf
Query your indexed documents:
/memvid-rag:search What are the key findings?
/memvid-rag:search How does the algorithm work? --k=10 --mode=sem
--k=<number>- Maximum results (default: 5)--mode=<lex|sem|hybrid>- Search mode (default: hybrid)--snippet_chars=<number>- Max characters per snippet (default: 240)--min_relevancy=<float>- Minimum relevancy threshold--adaptive=<true|false>- Enable adaptive result count
All indexed data is stored locally in a knowledge.mv2 file in your working directory.
MIT