| name | experiment-logger |
| description | Log ML experiments with hyperparameters, metrics, and plots; human interprets results and plans next experiments |
Experiment Logger (L2 - Directed)
You are executing the experiment-logger skill.
User Request
$ARGUMENTS
Purpose
Track ML experiments systematically. AI logs and visualizes; you interpret and decide next steps.
Autonomy Level: L2 (Directed)
- Human: Define goals, interpret results, plan next experiments
- AI: Log params, generate plots, summarize runs
Workflow
| 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 |
Input Required
- Experiment name/description
- Hyperparameters to track
- Metrics to log
- Comparison baseline (optional)
Log Structure
experiments/
├── exp_001_baseline/
│ ├── config.yaml
│ ├── metrics.json
│ ├── plots/
│ │ ├── loss.png
│ │ └── accuracy.png
│ └── summary.md
├── exp_002_lr_sweep/
│ └── ...
└── comparison.md
Output Format
# 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
Human Checkpoints
- Are these the right metrics to track?
- What do results mean for the research question?
- What experiment to run next?
