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Log ML experiments with hyperparameters, metrics, and plots; human interprets results and plans next experiments
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| 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
