Enrich

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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: sharpdeveye
• Source: sharpdeveye/maestro
• License: MIT
• Homepage: https://maestroskills.dev

Install and usage instructions live in the source repository linked above.