/enrich

name: enrich
description: "Use when the agent needs access to information beyond its training data — knowledge sources, RAG pipelines, or grounding data."
argument-hint: "[knowledge domain or source]"
category: enhancement
version: 2.0.0
user-invocable: true

MANDATORY PREPARATION

Invoke /agent-workflow — it contains workflow principles, anti-patterns, and the **Context Gathering Protocol**. Follow the protocol before proceeding — if no workflow context exists yet, you MUST run /teach-maestro first. Consult the knowledge-systems reference in the agent-workflow skill for RAG architecture, chunking strategies, and retrieval patterns.

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Add knowledge sources to ground the workflow in facts. Without grounding, agents hallucinate. With grounding, they cite sources.

Knowledge Source Assessment

Identify what knowledge the workflow needs:

| Knowledge Type | Source | Update Frequency | Access Pattern | |---------------|--------|-----------------|----------------| | Domain docs | Internal docs, specs | Monthly | Semantic search | | Code context | Codebase | Real-time | Code search | | User data | Database, CRM | Real-time | Structured query | | External data | APIs, web | Real-time | API call | | Historical | Logs, past interactions | Daily | Time-range query |

Add RAG Pipeline

For document-based knowledge (consult the knowledge-systems reference in the agent-workflow skill):

1. **Select documents**: Identify the authoritative source documents 2. **Chunk strategy**: Choose chunking based on document type (semantic > token-based) 3. **Embed**: Use appropriate embedding model for the domain 4. **Index**: Store in vector database with metadata 5. **Retrieve**: Implement hybrid search (semantic + keyword) 6. **Inject**: Add retrieved context to the prompt with source attribution

Add Structured Data

For database-backed knowledge:

1. **Define the query interface**: Natural language → structured query 2. **Add guardrails**: Read-only access, query complexity limits 3. **Format results**: Transform raw data into context the model can use 4. **Attribute**: Include data source and freshness in the context

Add Real-Time Data

For live information:

1. **Identify APIs**: What external services provide the needed data 2. **Cache strategy**: How often does the data change? Cache accordingly 3. **Fallback**: What happens when the API is down? 4. **Attribution**: Include data timestamp and source

Enrichment Checklist

• [ ] Every knowledge source has attribution (source, date, confidence)
• [ ] Retrieval quality tested independently of generation quality
• [ ] Chunk sizes tested and optimized for the document types
• [ ] Fallbacks exist for all external knowledge sources
• [ ] Knowledge base has a refresh/update strategy
• [ ] PII is handled appropriately in knowledge sources

Recommended Next Step

After enrichment, run `/evaluate` to test retrieval quality, or `/iterate` to set up continuous monitoring of knowledge freshness.

**NEVER**:

• Index everything without curation (garbage in = garbage out)
• Skip source attribution (hallucination without attribution is undetectable)
• Build RAG without testing retrieval quality first
• Use fixed chunk sizes for all document types
• Assume embedding similarity equals relevance