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optim-agent

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

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Harga belum dikonfirmasi★ 936 Star GitHubDirektori diperbarui · 6 Sep 2026agent-skill

Ringkasan

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

Baca dokumentasi lengkap

Dokumentasi sumber, bukan instruksi untuk situs ini. Periksa izin sebelum menjalankan perintah.

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.

Lihat sumber ↗

Metadata berkas

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.

Lihat teks asli

---
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.

Tinjau sumber

Harga dan biaya penggunaan

Dapatkan skill: Harga belum dikonfirmasi
Jalankan: Persyaratan belum dikonfirmasi. Periksa biaya agen, API, dan layanan di sumbernya.
Lisensi: MIT
Harga belum dikonfirmasi: Harga belum dikonfirmasi. Tautan sumber dan instalasi yang ada tetap tersedia.

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Sumber perlu ditinjau

Sumber berubah atau gagal disinkronkan. Tinjau sumber terbaru sebelum memasang.

Tinjau sebelum memasang: Hindari pemasangan otomatis

Lisensi: 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

Buka audit lengkap

Daftar alat adalah petunjuk metadata, bukan kompatibilitas teruji. Prompt adalah saran.

Mulai dengan tugas kecil

1Baca sumber dan pastikan masukan, keluaran, dependensi, serta izin.
2Minta rencana dari agent. Setujui pengaturan dan biaya sebelum uji terisolasi.
3Periksa hasil dan berkas yang berubah. Laporkan hanya yang dijalankan dan simpan revisi sumber.

Periksa dependensi, kunci API, dan biaya layanan pihak ketiga pada sumber. Repositori publik tidak berarti semua layanan gratis.

Sumber dan catatan penggunaan

Terindeks

Metadata dan tinjauan bersifat saran. Popularitas, penemuan sumber, dan keberhasilan eksekusi adalah fakta berbeda.

Repositori sumber: Optim-Agent/optim-agent
Lisensi: MIT
Versi: 1.0.0
Push GitHub terakhir: 14 Agu 2026
Direktori diperbarui: 6 Sep 2026
Jalur instruksi: SKILL.md @ 39e5f94b5e19

Versi dilaporkan dalam metadata direktori; periksa rilis sumber.

Kualitas

73/100

Kuat

Kepercayaan

63/100

Hanya sandbox

Audit

77/100

Perlu ditinjau

Buka audit lengkap Lihat laporan evaluasi

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: —
Hasil: —

Menyalin bukan memasang. Jumlah instalasi memerlukan laporan berhasil dan bukan jaminan kualitas menyeluruh.

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Buka API pemasangan Format teks LLM Buka Manifest Rencana resolusi otomatis

Detail lainnya

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Sumber listing

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Dapat diklaim

Listing ini diindeks dari sumber publik dan belum ditandai resmi hingga klaim pemelihara disetujui.

Kreator: Optim-Agent
Sumber: Optim-Agent/optim-agent
Diindeks oleh: Indeks komunitas OpenAgentSkill

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Ringkasan

optim-agent

Load the workflow

Workflow

Recovery

Rules

Tinjau sumber

Harga dan biaya penggunaan

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