enrich

Original：🇺🇸 English

Translated

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

2installs

Sourcesharpdeveye/maestro

Added on2026-04-11

NPX Install

npx skill4agent add sharpdeveye/maestro enrich

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 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):

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

Add Structured Data

For database-backed knowledge:

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

Add Real-Time Data

For live information:

Identify APIs: What external services provide the needed data
Cache strategy: How often does the data change? Cache accordingly
Fallback: What happens when the API is down?
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