# Enrich

Use when the agent needs access to information beyond its training data, knowledge...

- Repository: https://github.com/sharpdeveye/maestro (592 stars)
- Category: AI and agents
- License: MIT
- Last updated: 2026-04-29
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add sharpdeveye/maestro --skill enrich -g -a claude-code -y`
Codex: `npx skills add sharpdeveye/maestro --skill enrich -g -a codex -y`
Cursor: `npx skills add sharpdeveye/maestro --skill enrich -g -a cursor -y`
Gemini CLI: `npx skills add sharpdeveye/maestro --skill enrich -g -a gemini-cli -y`

## Description

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

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

## 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 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:

The rest: https://github.com/sharpdeveye/maestro/blob/HEAD/source/skills/enrich/SKILL.md