rag-skills

RAG Orchestration Skills

Skills that orchestrate the full RAG pipeline: question → retrieval → BigQuery semantic layer → structured answer.

The semantic layer is a set of BigQuery tables enriched with LLM-generated categories, topics, embeddings, and sentiment scores. Skills in this repo embed a user's question, retrieve the most relevant context, and synthesize a grounded answer — no hallucination, sources cited.

Customer Experience first. Extensible to any domain with a semantic layer in BigQuery.

Install

/plugin marketplace add https://github.com/mozilla/rag-skills.git
/plugin install rag-skills

Skills

Skill	Domain	Sources
cx-rag-researcher	Customer Experience	SUMO/Kitsune forums, Zendesk tickets, Mozilla Knowledge Base

Prerequisites

Requires Google Cloud credentials to query BigQuery:

gcloud auth application-default login
gcloud config set project <project-name-from-DE>

Local Development

./scripts/dev-setup.sh        # symlink skills for local testing
./scripts/dev-setup.sh --clean  # remove symlinks

Running Tests

Unit tests run automatically on every PR via GitHub Actions. To run them locally:

python -m pytest tests/test_embed.py tests/test_vector_search.py -v

Contributing Changes to the cx-rag-researcher Agent

If you modify agents/cx-rag-researcher.md, you must run the golden set evaluation before opening a PR.

Step 1 — Run all test questions through the agent:

python tests/golden_set/run_agent.py

Step 2 — Evaluate the responses with the LLM judge:

python tests/golden_set/evaluate.py

Step 3 — Commit the report with your PR:

git add tests/golden_set/report.json

GitHub Actions will verify the report exists and all questions passed. PRs with failures will be blocked.

Adding a New Domain

1. Create skills/<domain>-rag/SKILL.md with frontmatter (name, description) and orchestration instructions.
2. Add optional scripts/orchestrator.py for retrieval logic.
3. Register in .claude-plugin/marketplace.json under skills.
4. Commit and push to main.