enrich

Original：🇺🇸 English
Translated

Use when the agent needs access to information beyond its training data — knowledge sources, RAG pipelines, or grounding data.

8installs
Sourcesharpdeveye/maestro
Added on

NPX Install

npx skill4agent add sharpdeveye/maestro enrich

Tags

Translated version includes tags in frontmatter
ragai-agentknowledge-managementretrieval-systemdata-grounding

SKILL.md Content

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MANDATORY PREPARATION

Invoke {{command_prefix}}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 {{command_prefix}}teach-maestro first. Consult the knowledge-systems reference in the agent-workflow skill for RAG architecture, chunking strategies, and retrieval patterns.
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 TypeSourceUpdate FrequencyAccess Pattern
Domain docsInternal docs, specsMonthlySemantic search
Code contextCodebaseReal-timeCode search
User dataDatabase, CRMReal-timeStructured query
External dataAPIs, webReal-timeAPI call
HistoricalLogs, past interactionsDailyTime-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 
{{command_prefix}}evaluate
to test retrieval quality, or 
{{command_prefix}}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