Enrich

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 4e163a7a8095 · 19 Oct 2026Report an issue or request a re-scan

• Publishersharpdeveye GitHub userOwner of the repository this skill is read from
• Source foldersharpdeveye/maestro/source/skills/enrichThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdict4e163a7a8095A new audit runs when the source changes
• Repository statusNot archivedLast push 173 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

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

---
name: enrich
description: "Use when the agent needs access to information beyond its training data — knowledge sources, RAG pipelines, or grounding data."
argument-hint: "[knowledge domain or source]"
category: enhancement
version: 2.0.0
user-invocable: true
---

## 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:

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

Excerpt, first 3,306 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/sharpdeveye/maestro
git -C maestro checkout 4e163a7a8095

Works in

1. 2026-10-19
   ✓SAFE B 89 / 100

   first audit · source 4e163a7a8095

Questions about Enrich

• github.com/sharpdeveye/maestro/tree/main/source/skills/enrich — the audited source at commit 4e163a7a8095 and every finding aboveObserved · 19 Oct 2026

Spotted something wrong? Report a correction · Methodology