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Use when the agent needs access to information beyond its training data — knowledge sources, RAG pipelines, or grounding data.
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Baseado na classificação ocupacional SOC
Use when the workflow is too slow, too expensive, or both and needs latency, cost, or token usage optimization.
Use when porting a workflow to a different AI provider, deployment environment, model tier, or organizational context.
Use when any Maestro command is invoked — provides foundational workflow design principles across prompt engineering, context management, tool orchestration, agent architecture, feedback loops, knowledge systems, and guardrails.
Use when the workflow works but needs to handle more complex cases or produce higher-quality output through better tools, context, prompts, or models.
Use when workflow components are inconsistent, naming conventions vary, or a new team member's work needs alignment to project standards.
Capture a session summary — what was done, what decisions were made, and what to do next.
| 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 |
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.
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 |
For document-based knowledge (consult the knowledge-systems reference in the agent-workflow skill):
For database-backed knowledge:
For live information:
After enrichment, run /evaluate to test retrieval quality, or /iterate to set up continuous monitoring of knowledge freshness.
NEVER:
