ax-agent-optimizelisted

# AxAgent Optimize Codegen Rules (@ax-llm/ax) Use this skill for `agent.optimize(...)` workflows. Prefer short, modern, copyable patterns. Do not repeat general agent-authoring guidance unless the user needs it. Your job is to help the model choose a good optimization setup for the user's actual goal: - If the user wants better tool use, prefer action-aware tasks and either a deterministic metric or the built-in judge depending on how objective the scoring is. - If the user wants better wording only, responder optimization may be enough. - If the user wants reusable improvements, include artifact save/load. - If the user wants cost or recursion behavior improved, make the eval tasks expose those tradeoffs explicitly. ## Use These Defaults - Use `agent.optimize(...)` only after the agent is already configured and runnable. - Prefer a deterministic custom `metric` when success is easy to score from the prediction and task record. - Prefer the built-in judge path for open-ended assistant tasks: `judgeAI` plus `judgeOptions`. - Only reach for a plain typed `AxGen` evaluator when the user needs LLM-as-judge behavior outside the built-in `agent.optimize(...)` flow. - Default optimize target is `root.actor`; use `target: 'responder'` or explicit program IDs only when the user clearly asks for that. - Use eval-safe tools or in-memory mocks because optimization replays tasks many times. - Prefer precise tool return schemas such as `f.object(...)` over vague `f.json(...)` whenever