ワンクリックで
experiment-logger
Log ML experiments with hyperparameters, metrics, and plots; human interprets results and plans next experiments
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
メニュー
Log ML experiments with hyperparameters, metrics, and plots; human interprets results and plans next experiments
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Use when user wants to sync, push, or add BibTeX entries to a Zotero collection/folder. Handles deduplication by DOI, arXiv ID, or title.
Update skills with concise concrete practices, keeping SKILL.md minimal
Evaluate necessity and difficulty of issues, polish body, and create on GitHub with AI-assisted triage
Research academic papers and questions, write typst documents with cetz visualizations, teach until human understands
Autonomous daily digest of relevant arxiv papers in physics and ML based on your interests
Find, format, and manage academic citations in BibTeX and typst formats
| name | experiment-logger |
| description | Log ML experiments with hyperparameters, metrics, and plots; human interprets results and plans next experiments |
You are executing the experiment-logger skill.
$ARGUMENTS
Track ML experiments systematically. AI logs and visualizes; you interpret and decide next steps.
| Phase | Actor | Action |
|---|---|---|
| 1 | Human | Define experiment goals |
| 2 | Coder | Set up logging (params, metrics) |
| 3 | Coder | Run experiment |
| 4 | Coder | Generate plots (loss curves, comparisons) |
| 5 | Writer | Summarize results |
| 6 | Human | Interpret, decide next experiments |
experiments/
├── exp_001_baseline/
│ ├── config.yaml
│ ├── metrics.json
│ ├── plots/
│ │ ├── loss.png
│ │ └── accuracy.png
│ └── summary.md
├── exp_002_lr_sweep/
│ └── ...
└── comparison.md
# Experiment: [name]
## Config
- learning_rate: 0.001
- batch_size: 32
- epochs: 100
## Results
| Metric | Value | vs Baseline |
|--------|-------|-------------|
| Loss | 0.23 | -15% |
| Acc | 94.2% | +2.1% |
## Observations
- Converged faster than baseline
- Some overfitting after epoch 80
## Plots
