xvirobotics/metaskill/generate-report

# Find and read latest metrics
METRICS_FILE=$(ls -t reports/*.json experiments/*.json 2>/dev/null | head -1)
if [ -n "$METRICS_FILE" ]; then
    echo "=== Latest Metrics ==="
    cat "$METRICS_FILE"
fi
 
# Find config used
CONFIG_FILE=$(ls -t configs/*.yaml configs/*.toml 2>/dev/null | head -1)
if [ -n "$CONFIG_FILE" ]; then
    echo "=== Configuration ==="
    cat "$CONFIG_FILE"
fi

python3 -c "
import json
from pathlib import Path
 
# Check if visualization script exists
viz_script = Path('src/evaluation/visualize.py')
if viz_script.exists():
    print('Visualization script found')
else:
    print('No visualization script found -- will generate basic plots')
"

# Experiment Report: [Experiment Name]
 
**Date:** [current date]
**Branch:** [git branch]
**Commit:** [git commit hash]
**Author:** [generated by /generate-report skill]
 
---
 
## Executive Summary
 
[2-3 sentences: what was the experiment, what was the key result, and is it better than baseline?]
 
## Experiment Configuration
 
| Parameter | Value |
|-----------|-------|
| Model architecture | [from config] |
| Learning rate | [from config] |
| Batch size | [from config] |
| Epochs | [from config] |
| Optimizer | [from config] |
| Scheduler | [from config] |
| Random seed | [from config] |
| Dataset version | [from config or DVC] |
 
## Dataset Summary
 
| Split | Samples | Features | Classes |
|-------|---------|----------|---------|
| Train | [count] | [count] | [count or N/A] |
| Validation | [count] | [count] | [count or N/A] |
| Test | [count] | [count] | [count or N/A] |
 
## Results
 
### Final Metrics
 
| Metric | Value |
|--------|-------|
| [metric 1] | [value] |
| [metric 2] | [value] |
| ... | ... |
 
### Comparison with Baseline
 
| Metric | Baseline | Current | Delta | Improvement? |
|--------|----------|---------|-------|-------------|
| [metric 1] | [value] | [value] | [+/- value] | [Yes/No] |
| ... | ... | ... | ... | ... |
 
### Training Curves
 
![Training Loss](figures/training_loss.png)
![Validation Metric](figures/validation_metric.png)
 
### Confusion Matrix
 
![Confusion Matrix](figures/confusion_matrix.png)
 
## Analysis
 
### Key Findings
-[Finding 1: most important result]
-[Finding 2: notable pattern or observation]
-[Finding 3: any concerning behavior]
 
### Error Analysis
-[What types of errors does the model make?]
-[Are errors concentrated in specific classes or data subsets?]
 
### Comparison with Previous Experiments
-[How does this compare to previous runs?]
-[What changed and what impact did it have?]
 
## Recommendations
 
### Next Steps
1.[Actionable recommendation 1]
2.[Actionable recommendation 2]
3.[Actionable recommendation 3]
 
### Potential Improvements
-[Idea for model improvement]
-[Idea for data improvement]
-[Idea for training procedure improvement]
 
## Artifacts
 
| Artifact | Path |
|----------|------|
| Best checkpoint | checkpoints/best_model.pt |
| Metrics JSON | reports/metrics.json |
| Config file | configs/experiment.yaml |
| Training logs | experiments/[run-id]/ |
| Figures | reports/figures/ |
 
---
 
*Report generated automatically by the /generate-report skill.*