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Optim-Agent

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optim-agent

Use when optimizing configurable system parameters against a measurable scalar objective.

Examiner la sourceVoir sur GitHub
Prix non confirmé★ 936 Stars GitHubRegistre mis à jour · 6 sept. 2026agent-skill

Vue d’ensemble

Use when optimizing configurable system parameters against a measurable scalar objective.

Lire la documentation complète

Documentation source, pas des instructions pour ce site. Vérifiez les permissions avant d’exécuter des commandes.

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

  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:

    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])

  5. 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").

  6. 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.

  7. 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.
Voir la source ↗
Métadonnées du fichier
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.
Voir le texte original
---
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 from code understanding and all
   completed history, then use explicit ask/tell:

   ```python
   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")`.
6. **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.
7. **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.

Examiner la source

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Source à réexaminer

La source a changé ou sa synchronisation a échoué. Vérifiez-la avant installation.

Réviser avant installation: Éviter l’installation automatique

Licence: MIT

  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • Financial research output is not financial advice; require human review before any live investment decision
  • Financial research output is not financial advice; require human review before any live investment decision.
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
Ouvrir l’audit complet

Les outils sont des indications de métadonnées, pas une compatibilité testée. Les prompts sont des suggestions.

Commencer par une petite tâche

  1. 1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
  2. 2Demandez un plan à l’agent. Approuvez la configuration et les coûts avant un test isolé.
  3. 3Vérifiez résultats et fichiers modifiés. Signalez uniquement ce qui a été exécuté et conservez la révision source.

Vérifiez les dépendances, clés API et frais externes dans la source. Un dépôt public ne rend pas tous les services gratuits.

Source et conseils d’utilisation

Répertorié

Métadonnées et examens sont indicatifs. Popularité, découverte et exécution réussie sont des faits distincts.

Dépôt source
Optim-Agent/optim-agent
Licence
MIT
Version
1.0.0
Dernier push GitHub
14 août 2026
Registre mis à jour
6 sept. 2026
Chemin des instructions
SKILL.md @ 39e5f94b5e19

Version déclarée dans le registre ; vérifiez les versions de la source.

Qualité

73/100

Solide

Confiance

63/100

Sandbox uniquement

Audit

77/100

Revue nécessaire

Ouvrir l’audit completVoir le rapport d’évaluation
  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • Financial research output is not financial advice; require human review before any live investment decision
  • Financial research output is not financial advice; require human review before any live investment decision.
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
Verified installs
—
Résultats
—

Copier ne signifie pas installer. Les compteurs nécessitent un rapport de réussite et ne garantissent pas la qualité globale.

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Ouvrir l’API d’installationFormat texte LLMOuvrir ManifestPlan de résolution auto
Plus de détails
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Optim-Agent
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