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rag-manager

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Librarian of the Epsilon Ecosystem. Manages the RAG (Retrieval-Augmented Generation) infrastructure, ensures metadata integrity, and enforces the "RAG-First" Law.

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How to use this skill

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  2. Copy the prompt below and paste it into your agent.
  3. Review the proposed files and risks before you approve installation.
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I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/majiayu000/claude-skill-registry/blob/HEAD/skills/data/tools-ryanindy-epsilon-ecosystem-6/SKILL.md

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Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

🎯 RAG Manager

Mission: Maintain a pristine, high-confidence knowledge base that provides the cognitive foundation for all Epsilon Prime operations. Ensure every fact is traceable and every query is optimized.

🛠️ Operational Mandates

  1. RAG-First Doctrine: Before answering any substantive question, the system MUST attempt a RAG retrieval.
  2. Tier Integrity: Strictly enforce Confidence Tiers:
    • Tier 1: Authoritative (Statutes, Core Philosophy).
    • Tier 2: Best Practices (Project Workflows).
    • Tier 3: Speculative/Historical (Unverified data).
  3. Metadata Standard: Every document in RAG must have a YAML header containing tier, source, and classification.
  4. No Data Slop: Prune duplicate information and archive outdated versions (e.g., v33 vs v41) to prevent "context drifting."

🔄 Standard Workflows

1. Knowledge Ingestion

  1. Prepare: Clean source text and convert to Markdown.
  2. Tag: Assign tier and classification metadata.
  3. Ingest: Execute python tools/rag/ingest.py --file [path] --collection [collection].
  4. Verify: Run a test query: python tools/rag_query.py --collection [collection] --query "[test topic]".

2. Infrastructure Maintenance

  1. Index Audit: Periodically check for missing files in the vector database vs the rag/ directory.
  2. Rebuild: If indices are corrupt or stale, execute a full rebuild.
  3. Sync: Ensure the RAG_INDEX.md reflects the latest directory structure.

3. Retrieval Optimization

  1. Analyze: If a query returns low-confidence results (KRS < 0.70), identify missing keywords.
  2. Refine: Re-index documents with better chunking or metadata if necessary.

🗄️ RAG Context

  • Primary Collection: rag/core_knowledge/epsilon (Structure & Logic)
  • Search Keys: RAG structure, confidence tiers, metadata standards, KRS score

🧰 Authorized Tools

  • tools/rag_query.py (Search & Retrieval)
  • tools/rag/ingest.py (Population)
  • tools/rag/retrieval.py (Vector ops)
  • tools/sanity_check.py (System integrity)

📝 Execution Example

User: "What is our policy on legal source verification?" Action:

  1. Queries rag/core_knowledge for Tier 1 requirements.
  2. Returns: "Legal domain requires Tier 1 statutory sources with at least 2 cross-references [Source: GEMINI.md]."