RAG Tool

rag-tool

Store and search text or markdown as semantic chunks in a reusable local vector store. Use when a user wants to ingest documents or retrieve relevant passages from previously stored material.

planetis-m/rag-tool·0installs1starsSecurity

SKILL.md

Full skill instructions

RAG Tool

Use one command for the requested operation:

cvstore --doc=DOC --kind=source|derived [--source=RELATIVEPATH] INPUT.txt
cvquery [--doc=DOC] [--kind=source|derived] [--page=N] [--label=TEXT] QUERY

Readiness

Run cvstore --help and cvquery --help.
If either command is missing or fails to start, read references/chunkvec-install.md, install the binary or missing runtime dependency, and retry.
After installing the tools, tell the user how to configure DEEPINFRA_API_KEY or config.json before ingest or search.

Execution

Request unrestricted network or escalated execution directly when running cvstore or cvquery. Do not run sandboxed network probes first.
Do not inspect environment variables, shell profiles, or filesystem files to discover API keys. If authentication fails, ask the user to configure the key, then retry.

Mode Selection

For ingest requests, read references/store-mode.md.
For retrieval requests, read references/search-mode.md.
Read both only when the request spans both operations.

Exit Codes

0: the requested work succeeded.
2: cvstore finished with permanent chunk failures; successful chunks were still stored. Report the partial failure.
3: startup or runtime failure. Report the error.

Stable Doc IDs

Derive lowercase kebab-case doc ids from one stable document identity. Prefer the source file stem, then an explicit logical title.
Keep the same doc for the original document and its derived notes, quiz, or flashcards. Express artifact type with kind, not a new doc.
If pasted text has no stable file or title, ask for a short logical name before storing.

Install skill

Copy and run in your terminal

npx -y skills add https://github.com/planetis-m/rag-tool --skill rag-tool

View on GitHub

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