domains / families / agent-tooling / optim-agent

optim-agent

authentic

Guide agent-driven parameter optimization for configurable systems with measurable objectives. Use for HPO, inference tuning, simulations, or RL/control experiments.

When to use

• Use when tuning hyperparameters, prompts, inference settings, simulation parameters, quantitative strategies, or RL/control policies.
• Use when the objective can be measured as a scalar score, loss, accuracy, cost, latency, reward, or risk-adjusted metric.
• Use when the user needs a small-budget optimization loop with trial history, comparisons, and stop criteria.

When not to use

• The objective is purely subjective and cannot be scored consistently.
• The user has not provided permission to run experiments or consume compute/API budget.
• The task is a one-shot implementation, debugging, or code review request with no configurable search space.
• This skill does not guarantee a global optimum.
• Results depend on objective quality, noise, search-space design, and experiment reproducibility.
• Use domain review before applying tuned configurations to production, financial, safety-critical, or user-impacting systems.

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