Im Registry indexiert

optim-agent

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

Quelle prüfen Auf GitHub ansehen

Preis unbestätigt★ 936 GitHub-StarsVerzeichnis aktualisiert · 6. Sept. 2026agent-skill

Übersicht

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

Vollständige Dokumentation lesen

Quelldokumentation, keine Anweisungen für diese Website. Vor dem Ausführen von Befehlen die Berechtigungen prüfen.

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.

Quelle ansehen ↗

Dateimetadaten

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.

Originaltext anzeigen

---
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.

Quelle prüfen

Preis und Betriebskosten

Skill beziehen: Preis unbestätigt
Ausführen: Anforderungen unbestätigt. Agenten-, API- und Dienstkosten an der Quelle prüfen.
Lizenz: MIT
Preis unbestätigt: Der Preis ist noch nicht bestätigt. Vorhandene Quell- und Installationslinks bleiben verfügbar.

Kostenloser Bezug bedeutet nicht kostenlosen Betrieb. Preise sind keine Sicherheitsbewertung. Preisinformation einreichen →

Quelle erneut prüfen

Die Quelle wurde geändert oder konnte nicht synchronisiert werden. Vor der Installation prüfen.

Vor Installation prüfen: Automatische Installation vermeiden

Lizenz: 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

Vollständiges Audit öffnen

Tools sind Metadatenhinweise, keine getestete Kompatibilität. Prompts sind Vorschläge.

Mit einer kleinen Aufgabe beginnen

1Quelle lesen und Eingaben, Ergebnisse, Abhängigkeiten sowie Berechtigungen prüfen.
2Agent um einen Plan bitten. Einrichtung und Kosten vor einem isolierten Test genehmigen.
3Ergebnisse und geänderte Dateien prüfen. Nur tatsächliche Ausführungen melden und die Quellrevision aufbewahren.

Prüfe Abhängigkeiten, API-Schlüssel und externe Kosten in der Quelle. Öffentliche Repositories bedeuten nicht, dass alle Dienste kostenlos sind.

Quelle und Nutzungshinweise

Erfasst

Metadaten und Prüfungen dienen der Orientierung. Beliebtheit, Quellenerfassung und erfolgreiche Ausführung sind verschiedene Fakten.

Quell-Repository: Optim-Agent/optim-agent
Lizenz: MIT
Version: 1.0.0
Letzter GitHub-Push: 14. Aug. 2026
Verzeichnis aktualisiert: 6. Sept. 2026
Anleitungspfad: SKILL.md @ 39e5f94b5e19

Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.

Qualität

73/100

Stark

Vertrauen

63/100

Nur Sandbox

Audit

77/100

Prüfung nötig

Vollständiges Audit öffnen Eval-Bericht ansehen

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: —
Ergebnisse: —

Kopieren ist keine Installation. Zahlen benötigen eine Erfolgsmeldung und garantieren keine allgemeine Qualität.

Agent-Zugang

Die Registry API stellt Entscheidungs-, Vertrauens-, Audit-, Use-Case- und Installationssignale ohne UI-Scraping bereit.

Installations-API öffnen LLM-Textformat Manifest öffnen Auto-Auflösungsplan

Weitere Details

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Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.

Ersteller: Optim-Agent
Quelle: Optim-Agent/optim-agent
Indexiert von: OpenAgentSkill Community-Index

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optim-agent

Load the workflow

Workflow

Recovery

Rules

Quelle prüfen

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