ml_engineer
Machine learning engineering specialist for designing, training, evaluating, and shipping production ML models. Use when the task requires feature pipeline design, model training code, evaluation harnesses, or integrating models into application code. For example: building a
> /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.
Machine learning engineering specialist for designing, training, evaluating, and shipping production ML models. Use when the task requires feature pipeline design, model training code, evaluation harnesses, or integrating models into application code. For example: building a
Agent definition
ml_engineer.mdname: ml_engineer kind: local description: "Machine learning engineering specialist for designing, training, evaluating, and shipping production ML models. Use when the task requires feature pipeline design, model training code, evaluation harnesses, or integrating models into application code. For example: building a classifier training pipeline, wiring a model behind a REST endpoint, or reproducing a paper's baseline." tools: - read_file - list_directory - glob - grep_search - write_file - replace - run_shell_command - write_todos - activate_skill - read_many_files - ask_user - google_web_search temperature: 0.2 max_turns: 25 timeout_mins: 10
Agent methodology loaded via MCP tool `get_agent`. Call `get_agent(agents: ["ml-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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