How do I install the RAG Knowledge skill?

Run npx skillmds@latest add vstorm-co/rag-knowledge-2 in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is RAG Knowledge safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with RAG Knowledge?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is RAG Knowledge free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published RAG Knowledge?

vstorm-co (@vstorm-co) published this skill. Their other Agent Skills are listed on their SkillMD profile.

RAG Knowledge by vstorm-co — Agent Skill

Name: RAG Knowledge
Author: vstorm-co

RAG Knowledge

Work with the RAG knowledge base — ingest documents, run semantic search, manage collections, or add a sync source/connector (Google Drive, S3). Use when populating or debugging the knowledge base, tuning retrieval, or adding a new document source. This project uses {{ cookiecutter.vector_store }} + {{ cookiecutter.embedding_provider }} embeddings.

vstorm-co Updated Sep 22, 2026

File contents

RAG Knowledge Base ({{ cookiecutter.vector_store }})

The RAG stack lives in backend/app/services/rag/ (ingestion, vectorstore, embeddings, connectors). Retrieval is exposed to the agent as the search_knowledge_base tool, and to operators via the CLI and the dashboard.

CLI (run from `backend/`)

uv run {{ cookiecutter.project_slug }} cmd rag-ingest ./docs/ --collection docs --recursive   # ingest files/folder
uv run {{ cookiecutter.project_slug }} cmd rag-search "your question" --collection docs        # semantic search
uv run {{ cookiecutter.project_slug }} cmd rag-collections                                     # list collections
uv run {{ cookiecutter.project_slug }} cmd rag-stats                                           # chunk/vector counts
uv run {{ cookiecutter.project_slug }} cmd rag-drop <collection> --yes                         # delete a collection

Ingestion = parse → chunk → embed → upsert into {{ cookiecutter.vector_store }}. Re-ingesting the same source updates it (use --no-replace / --sync-mode to control dedupe).

Sync sources (connectors)

Connectors keep a collection in sync with an external source (Google Drive, S3/MinIO) on a schedule, and can be managed per-organization from the dashboard (/orgs/[id]/integrations) or via CLI:

uv run {{ cookiecutter.project_slug }} cmd rag-sources                  # list
uv run {{ cookiecutter.project_slug }} cmd rag-source-add               # add (interactive)
uv run {{ cookiecutter.project_slug }} cmd rag-source-sync --all        # trigger a sync

Connector credentials are encrypted at rest with CHANNEL_ENCRYPTION_KEY (Fernet).

Adding a new connector type

Implement a connector in backend/app/services/rag/connectors/ following the existing Google Drive / S3 connectors, register it in the connector registry, and expose its config fields. See docs/howto/add-sync-connector.md and docs/howto/configure-sync-sources.md.

Tuning retrieval

Chunk size/overlap and parser (PyMuPDF / LlamaParse) are configured via env — see docs/configuration.md and docs/rag.md.
Reranking (Cohere or local CrossEncoder) improves result ordering when enabled.
If search returns poor results: confirm the collection is populated (rag-stats), check the active collection in the chat's KB selector, and verify the embedding provider/key.

Rules

Embedding provider and dimensions are fixed per project ({{ cookiecutter.embedding_provider }}) — don't mix embeddings across a collection; re-ingest if you change them.
Heavy ingestion runs as a background job, not inline in a request.
See docs/rag.md for the full pipeline reference.

---name: rag-knowledge-2description: Work with the RAG knowledge base — ingest documents, run semantic search, manage collections, or add a sync source/connector (Google Drive, S3). Use when populating or debugging the knowledge base, tuning retrieval, or adding a new document source. This project uses {{ cookiecutter.vector_store }} + {{ cookiecutter.embedding_provider }} embeddings.---# RAG Knowledge Base ({{ cookiecutter.vector_store }})The RAG stack lives in `backend/app/services/rag/` (ingestion, vectorstore, embeddings, connectors). Retrieval is exposed to the agent as the `search_knowledge_base` tool, and to operators via the CLI and the dashboard.## CLI (run from `backend/`)```bashuv run {{ cookiecutter.project_slug }} cmd rag-ingest ./docs/ --collection docs --recursive # ingest files/folderuv run {{ cookiecutter.project_slug }} cmd rag-search "your question" --collection docs # semantic searchuv run {{ cookiecutter.project_slug }} cmd rag-collections # list collectionsuv run {{ cookiecutter.project_slug }} cmd rag-stats # chunk/vector countsuv run {{ cookiecutter.project_slug }} cmd rag-drop <collection> --yes # delete a collection```Ingestion = parse → chunk → embed → upsert into {{ cookiecutter.vector_store }}. Re-ingesting the same source updates it (use `--no-replace` / `--sync-mode` to control dedupe).## Sync sources (connectors)Connectors keep a collection in sync with an external source (Google Drive, S3/MinIO) on a schedule, and can be managed per-organization from the dashboard (`/orgs/[id]/integrations`) or via CLI:```bashuv run {{ cookiecutter.project_slug }} cmd rag-sources # listuv run {{ cookiecutter.project_slug }} cmd rag-source-add # add (interactive)uv run {{ cookiecutter.project_slug }} cmd rag-source-sync --all # trigger a sync```Connector credentials are encrypted at rest with `CHANNEL_ENCRYPTION_KEY` (Fernet).## Adding a new connector typeImplement a connector in `backend/app/services/rag/connectors/` following the existing Google Drive / S3 connectors, register it in the connector registry, and expose its config fields. See `docs/howto/add-sync-connector.md` and `docs/howto/configure-sync-sources.md`.## Tuning retrieval- Chunk size/overlap and parser (PyMuPDF / LlamaParse) are configured via env — see `docs/configuration.md` and `docs/rag.md`.- Reranking (Cohere or local CrossEncoder) improves result ordering when enabled.- If search returns poor results: confirm the collection is populated (`rag-stats`), check the active collection in the chat's KB selector, and verify the embedding provider/key.## Rules- Embedding provider and dimensions are fixed per project ({{ cookiecutter.embedding_provider }}) — don't mix embeddings across a collection; re-ingest if you change them.- Heavy ingestion runs as a background job, not inline in a request.- See `docs/rag.md` for the full pipeline reference.

Frequently asked questions