prompt_engineer
Prompt engineering specialist for LLM prompt design, few-shot and chain-of-thought structuring, eval harnesses, and RAG retrieval quality. Use when the task requires writing or reviewing prompts, building evaluation datasets, tuning retrieval for a RAG system, or diagnosing
> /plugin marketplace add josstei/maestro-orchestrate > /plugin install maestro@maestro-orchestrator
How it fires
How this agent gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
Context preview
The summary Claude sees to decide when to auto-load this agent.
Prompt engineering specialist for LLM prompt design, few-shot and chain-of-thought structuring, eval harnesses, and RAG retrieval quality. Use when the task requires writing or reviewing prompts, building evaluation datasets, tuning retrieval for a RAG system, or diagnosing
Agent definition
prompt_engineer.mdname: prompt_engineer kind: local description: "Prompt engineering specialist for LLM prompt design, few-shot and chain-of-thought structuring, eval harnesses, and RAG retrieval quality. Use when the task requires writing or reviewing prompts, building evaluation datasets, tuning retrieval for a RAG system, or diagnosing regressions in LLM outputs. For example: designing a classifier prompt with calibrated confidence, writing an eval set for a summarization prompt, or tuning chunk size and reranking in a RAG pipeline." tools: - read_file - list_directory - glob - grep_search - write_file - replace - read_many_files - google_web_search - write_todos - ask_user - web_fetch temperature: 0.3 max_turns: 15 timeout_mins: 5
Agent methodology loaded via MCP tool `get_agent`. Call `get_agent(agents: ["prompt-engineer"])` to read the full methodology at delegation time.
Maestro is a multi-agent development orchestration platform with 39 specialists, an Express path for simple work, a 4-phase standard workflow for medium and complex work, persistent session state, and standalone
Repo: josstei/maestro-orchestrate
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