Agent skill · data analytics · sickn33
optim-agent
Guide agent-driven parameter optimization for configurable systems with measurable objectives. Use for HPO, inference tuning, simulations, or RL/control experiments.
Why this skill is useful
Provides domain-specific knowledge for hyperparameter optimization and structured experimentation that the AI wouldn't reliably generate on its own.
What it needs
About 2k tokens when loaded. Last updated 2026-08-07. 44,571 stars on the source repository.
What this skill does
Optim Agent Overview Use this skill to optimize configurable systems against a measurable scalar objective. It helps an agent turn vague tuning requests into bounded experiments with a defined search space, budget, baseline, and evidence-backed recommendation. When to Use This Skill 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. Do not use this skill when 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. Instructions 1. Define the optimization target in one sentence: maximize or minimize one scalar metric. 2. List the tunable parameters, valid ranges, types, defaults, and any forbidden combinations. 3. Establish at least one baseline before proposing agent-guided trials. 4. Set the budget up front: number of trials, time, compute, money, or dataset subsample. 5. Run or request trials one at a time unless the user explicitly approves parallel execution. 6. Record every trial with parameters, metric value, notes, and failure status. 7. Compare the best result against the baseline and a simple search strategy when possible. 8. Stop when the budget is exhausted, the improvement plateaus, or the next trial cannot be justified from evidence. 9. Report the recommended configuration, measured gain, tradeoffs, and any validation still needed before production use. Examples Example 1: Hyperparameter optimization Tune learning rate, regularization, and tree depth for a credit-default model. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills sickn33/optim-agent