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Optim Agent

Use when the user wants to optimize configurable system parameters against a measurable scalar objective, especially for model training, inference, quantitat... Skill: Optim Agent Owner: optim-agent Summary: Use when the user wants to optimize configurable system parameters against a measurable scalar objective, especially for model training, inference, quantitat... Tags: latest:0.1.0 Version history: v0.1.0 | 2026-07-24T10:29:51.279Z | auto - Initial release of optim-agent skill for optimizing configurable system parameters against a measurable scalar objective. - Provides

OpenClaw
View on ClawHubHomepage

Rank

62

Safety

84

Downloads

3.0k

Updated

Oct 9, 2026

Version

0.1.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 3K downloads reported by the source. Last updated 10/9/2026.

Avoid when

  • Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor
Clawhubvendor · observed Oct 9, 2026
Protocol compatibility
OpenClawcompatibility · observed Oct 9, 2026
Adoption signal
3K downloadsadoption · observed Oct 9, 2026
Latest release
0.1.0release · observed Jul 24, 2026
Handshake status
UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17d81bx0d0wzzwqq29rwtranh8ag1x0:optim-agent
  1. Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
  2. Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-optim-agent-optim-agent/snapshot"

Documentation

CLAWHUB

40,338 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

plugins/optim-agent/SKILL.md

---
name: optim-agent
description: Use when optimizing configurable system parameters against a measurable scalar objective.
---

# optim-agent

Read and follow the canonical optim-agent workflow in `../../SKILL.md`.
Resolve that path from this file's directory before beginning the optimization.

plugins/optim-agent/skills/optim-agent/SKILL.md

---
name: optim-agent
description: Use when optimizing configurable system parameters against a measurable scalar objective.
---

# optim-agent

Read and follow the canonical optim-agent workflow in `../../../../SKILL.md`.
Resolve that path from this file's directory before beginning the optimization.

SKILL.md

---
name: optim-agent
description: 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.
---

# 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:

```text
$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:

```bash
# 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

1. **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.
2. **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.
3. **Establish a baseline.** Evaluate the current/default configuration with the
   same command and environment used for every later trial.
4. **Initialize or resume.** Keep artifacts in the repository's ignored
   `.optim-agent-runs/` directory:

   ```bash
   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
   ```

   ```python
   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])
   ```

5. **Run one informed trial.** Choose parameters fr

benchmarks/README.md

# Benchmark contract

The committed benchmark artifacts support the claims in the README, docs, and
paper. They are evidence, not decorative assets. Tables and figures must be
generated from the JSON runs under `docs/assets/`.

## Suites

`manifest.json` records the stable suite identifiers, trial budgets, seeds,
result globs, and context policy. Individual result files remain authoritative
for backend, model, effort, search-space version, objective values, and sampled
parameters.

The hard-function comparison uses **no supplied task context**: generic
parameter names, bounds, and observed trial history only. A model may still
recognize a standard function from its bounds or values, so these runs measure
small-budget optimization rather than semantic-context benefit. Classification
runs provide the explicit with-context versus no-context comparison.
The RL-control benchmark is CPU-only and uses Gymnasium Acrobot-v1 and
LunarLander-v3 with a discretized Q-learning controller. It runs Random, TPE,
GPT-5.5 with context, and GPT-5.5 without context for 20 trials across seeds
`0..4`. The GPT-5.5 arms use high modeling effort and the last 5 trials of
history. The winning contextual arm disables explicit reasoning and qualitative
notes. It is strongest on both environment means in the committed run.
The credit-default benchmark is CPU-only and uses UCI dataset 350, Default of
Credit Card Clients (CC BY 4.0, DOI `10.24432/C55S3H`). The official archive
SHA-256, workbook schema, 60/20/20 stratified split, split seed, search space,
and 20-trial budget are pinned. All five optimizer seeds see the same train and
validation data; the held-out test split is evaluated only for each run's
validation-selected configuration.

Random, TPE, GP-BO, selected contextual GPT-5.5, and matched GPT-5.5/no-context
artifacts must all be present. Agent runs are fail-closed. The primary
trajectory metric is validation incumbent log loss; held-out test log loss is a
secondary generalization check. This is not a production credit-decision system
and must not be interpreted as one. The test split is reported after selection,
not used to select.

## Provenance requirements

Every agent result intended for publication must identify:

- suite and function or dataset;
- backend and exact model identifier;
- agent effort and context policy;
- trial budget, seed, and search-space version;
- ordered parameter proposals and objective values; and
- creation time or source commit when the runner provides it.

Baselines must use the same space, objective, budget, and seeds. A rotating
hosted model alias must be labeled as such; never silently present it as a
pinned model.

## Publication gate

Do not update prose or plots from a partial seed set. Before publishing:

```bash
pip install -e ".[examples,ml,dev]"
pytest
python scripts/verify_classification_cumulative_error.py
python examples/hard_functions.py selfcheck
python examples/hard_functions.py plot
pip install -e ".[rl,examples]"
pytho

README.md

<p align="center">
  <picture>
    <source media="(prefers-color-scheme: dark)" srcset="docs/assets/optim-agent-logo-dark.svg">
    <img alt="optim-agent" src="docs/assets/optim-agent-logo-light.svg" width="500">
  </picture>
</p>

<h1 align="center">optim-agent</h1>

<p align="center">
  <strong>Agentic system optimization with coding agents.</strong><br>
  Automate the iterative parameter-tuning work of an algorithm engineer.
</p>

<p align="center">
  <a href="https://github.com/Optim-Agent/optim-agent/stargazers"><img alt="GitHub stars" src="https://img.shields.io/github/stars/Optim-Agent/optim-agent?style=square"></a>
  <a href="https://pypi.org/project/optim-agent/"><img alt="PyPI" src="https://img.shields.io/pypi/v/optim-agent"></a>
  <a href="https://pypi.org/project/optim-agent/"><img alt="Python versions" src="https://img.shields.io/pypi/pyversions/optim-agent"></a>
  <a href="LICENSE"><img alt="License: MIT" src="https://img.shields.io/pypi/l/optim-agent"></a>
  <a href="https://optim-agent.github.io/optim-agent/"><img alt="Docs" src="https://img.shields.io/badge/docs-online-blue"></a>
  <a href="https://code.claude.com/docs/en/skills"><img alt="Claude Skill" src="https://img.shields.io/badge/Claude-Skill-D97757?logo=claude&logoColor=white"></a>
  <a href="https://developers.openai.com/codex/skills"><img alt="Codex Skill" src="https://img.shields.io/badge/Codex-Skill-blue?logo=openai&logoColor=white"></a>
</p>

<p align="center">
  <strong>English</strong> |
  <a href="docs/i18n/README_ZH.md">简体中文</a> |
  <a href="docs/i18n/README_JA.md">日本語</a> |
  <a href="docs/i18n/README_KO.md">한국어</a> |
  <a href="docs/i18n/README_FR.md">Français</a> |
  <a href="docs/i18n/README_DE.md">Deutsch</a> |
  <a href="docs/i18n/README_ES.md">Español</a> |
  <a href="docs/i18n/README_PT.md">Português</a> |
  <a href="docs/i18n/README_RU.md">Русский</a>
</p>

optim-agent lets Claude Code / Codex / OpenCode tune real system parameters by
reading your code, proposing trials, and recording measured objective results.
Use it when your system exposes configurable parameters and a measurable objective.
It combines what each parameter *means* with what the trial history *shows*,
then proposes the next configuration to evaluate. Objective evaluations remain
authoritative: optim-agent proposes values, validates them against the declared
space, records outcomes, and falls back to safe sampling when an agent reply is
invalid.

<p align="center">
  <img alt="optim-agent tuning loop" src="docs/assets/optim-agent-overview.png" width="900">
</p>

| Models | Systems | Research |
|---|---|---|
| Training, architecture, and RL experiments | Inference, latency, cost, control, and decision rules | Quant signals, simulations, and scientific workflows |

## Why optim-agent

- **Semantic proposals** - coding agents reason over parameter meanings, study
  context, and observed outcomes instead of treating every dimension as an
  anonymous coordinate.
- **Small-budget leverage** -

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Machine-readable data

The same record, as JSON, for agents and crawlers.

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Record generated Oct 9, 2026.

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