mcpField
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enrich

enrich

github.com/sharpdeveyeAgent workflows592320 installsUpdated 26w ago
Repository

About enrich

enrich is a skill for grounding agent workflows in curated knowledge sources. It covers RAG pipelines, structured data, and live information, with attention to attribution, retrieval quality, and refresh strategies.

Key features

  1. 01Assesses knowledge needs and sources
  2. 02Builds document-based RAG pipelines
  3. 03Adds guarded structured-data queries
  4. 04Plans access to real-time information
  5. 05Checks attribution and retrieval quality

Use cases

  1. 01Grounding an agent in domain documents
  2. 02Connecting workflow context to database data
  3. 03Providing agents with up-to-date external information
  4. 04Reviewing retrieval and knowledge freshness

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/sharpdeveye/maestro --skill enrich

After installing, type/enrichin Claude Code to confirm it loaded.

Details

From the project README

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.

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 /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

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