Optim agent

LLM agents as your hyperparameter optimizer.

Install

npx -y skills add Optim-Agent/optim-agent

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What its author says it does

Copied from the file, not written here

Use when the user wants to optimize configurable system parameters against a measurable scalar objective, especially for model training, inference, quantitative strategies, reinforcement learning, scientific workflows, or other expensive black-box evaluations where reading the project can improve trial selection.

SKILL.md

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optim-agent

Act as the sampler inside any coding-agent session: Claude Code, Codex, OpenCode/OpenClaw, or another agent that can read project files and run shell commands. Read the project to understand parameter meaning and interactions, propose one configuration, run the real evaluator, and record the result through optim-agent's ask/tell API. Let the measured objective, not the agent's intuition, decide what works.

Load the workflow

Use this file as the operating guide for the active coding agent. In Codex, it can be installed directly from GitHub:

$skill-installer install https://github.com/Optim-Agent/optim-agent

In Claude Code, OpenCode/OpenClaw, or another coding-agent environment, place this repository or SKILL.md in the agent-visible workspace and ask the agent to follow the optim-agent workflow. The workflow does not depend on Codex-only APIs; it needs file access, shell access, and Python.

Ensure the Python package is importable. Choose one source; do not install both:

# Stable release from PyPI
python -m pip install optim-agent

# Latest source from GitHub
python -m pip install "optim-agent @ git+https://github.com/Optim-Agent/optim-agent.git"

For a reproducible GitHub install, append @<tag-or-commit> after .git.

Workflow

Understand the system. Read the evaluation entry point and every file that defines the target parameters. Record each parameter's type, legal range, semantics, interactions, and operational constraints.
Define the experiment. Confirm the scalar objective, minimize or maximize, trial budget, evaluation command, runtime/cost limit, and fixed workload or seed. For multiple metrics or hard constraints, agree on one scalar feasibility or penalty rule before running trials.
Establish a baseline. Evaluate the current/default configuration with the same command and environment used for every later trial.

Initialize or resume. Keep artifacts in the repository's ignored .optim-agent-runs/ directory:

if git rev-parse --git-dir >/dev/null 2>&1 && ! git check-ignore -q .optim-agent-runs/; then
  printf '/.optim-agent-runs/\n' >> "$(git rev-parse --git-path info/exclude)"
fi

from pathlib import Path
import optim_agent as oa

run_dir = Path(".optim-agent-runs")
run_dir.mkdir(exist_ok=True)
study = oa.create_study(
    direction="minimize",
    storage=run_dir / "skill-study.json",
    seed=0,
)
print([(t.params, t.value, t.state) for t in study.trials])

Run one informed trial. Choose parameters from code understanding and all completed history, then use explicit ask/tell:
```
params = {"threshold": 0.72, "budget": 80}
trial = study.ask(params)
try:
    value = evaluate_system(**trial.params)
except Exception:
    study.tell(trial, state="failed")
    raise
else:
    study.tell(trial, value)
```
For a deliberately stopped trial, report the latest valid intermediate metric first, then call study.tell(trial, state="pruned").
Select the next point. Avoid accidental repeats, explore broadly before exploiting, respect bounds and constraints, and treat failed regions as evidence. If the evaluator is noisy, repeat promising configurations under the same workload before declaring a winner.
Stop and report. Stop at the approved budget or stopping condition. Report the baseline, best value and parameters, trial count, failed/pruned trials, convergence trend, and exact reproduction command.

Recovery

JSON storage records a trial when study.tell runs. Before launching an expensive external evaluation, save its parameters, command, and output path in a per-trial directory under .optim-agent-runs/. After interruption, inspect that output before rerunning: if a valid result exists, recreate the same point with study.ask(params) and record it; otherwise rerun it deliberately.

Use SQLite storage (skill-study.db) only when the user explicitly wants multiple processes. Sequential trials are the default because each proposal should use the complete prior history.

Rules

Use ask/tell in skill mode; do not delegate proposal selection to AgentSampler when the session agent is meant to read and reason over code.
Keep evaluation inputs and outputs isolated from production configuration.
Never fabricate, infer, or manually improve an objective value.
Record crashes as failed; record intentional early stops as pruned.
Preserve the study and trial artifacts so the result is auditable and resumable.
Do not tune secrets, credentials, or unbounded parameters.