npx claudepluginhub andikarachman/data-science-plugin --plugin dsThis skill uses the workspace's default tool permissions.
Standard format for logging ML experiments. Every experiment gets a plan and a result document.
Maintains persistent ML experiment journals in Markdown files, logging hypotheses, changes, results, metrics, and learnings across sessions.
Manages ML experiment metadata via YAML registry: register experiments, record benchmarks, compare runs, track status using exp CLI.
Sets up ML experiment tracking with MLflow or Weights & Biases: installs packages, initializes tools, and provides logging code for parameters, metrics, and artifacts.
Share bugs, ideas, or general feedback.
Standard format for logging ML experiments. Every experiment gets a plan and a result document.
## Experiment: [Short descriptive name]
**Date:** YYYY-MM-DD
**Author:** [Name]
**Status:** [planned | running | complete | abandoned]
### Hypothesis
[What you expect to happen and why]
### Configuration
- **Model:** [algorithm and key settings]
- **Features:** [feature set identifier or description]
- **Data:** [dataset version, date range, row count]
- **Data hash:** [SHA-256 of the dataset file or query fingerprint]
- **Split:** [strategy, ratios, seed]
- **Hyperparameters:** [key hyperparameters]
### Environment
- **Python:** [version]
- **Key libraries:** [e.g., sklearn 1.4.0, xgboost 2.1.0, pandas 2.2.0]
- **Hardware:** [CPU/GPU, memory]
- **Git commit:** [SHA of the code at experiment time]
- **Compute cost:** [wall time, GPU hours if applicable]
### Results
| Metric | Train | Validation | Test | Baseline |
|---|---|---|---|---|
| [primary] | | | | |
| [secondary] | | | | |
### Key Observations
- [What worked, what didn't, surprises]
### Decision
[Ship | Iterate | Abandon] -- [Reasoning]
### Next Steps
- [What to try next based on findings]
### Series
- **Parent experiment:** [link to prior experiment this builds on, if any]
- **Child experiments:** [links to follow-up experiments, if any]
Every experiment log must include:
| Field | Why |
|---|---|
| Hypothesis | Forces clear thinking about what you expect |
| Data hash | Reproducibility -- exact data version |
| Split + seed | Reproducibility -- exact train/test split |
| Environment | Reproducibility -- library versions matter |
| Git commit | Reproducibility -- exact code version |
| Baseline comparison | Context -- improvement means nothing without a reference |
| Decision | Accountability -- forces a clear outcome |
| Series linkage | Continuity -- prevents orphaned experiment chains |
Experiment files follow this pattern:
docs/ds/experiments/YYYY-MM-DD-<experiment-name>-plan.md
docs/ds/experiments/YYYY-MM-DD-<experiment-name>-result.md
Use lowercase, hyphens, and descriptive names: 2026-02-24-churn-xgb-recency-features-plan.md
import sys
import pkg_resources
import subprocess
def capture_environment():
"""Capture current environment for experiment logging."""
env = {
"python": sys.version.split()[0],
"libraries": {},
"git_commit": subprocess.getoutput("git rev-parse HEAD").strip(),
}
for pkg in ['pandas', 'numpy', 'sklearn', 'xgboost', 'lightgbm', 'torch']:
try:
env["libraries"][pkg] = pkg_resources.get_distribution(pkg).version
except pkg_resources.DistributionNotFound:
pass
return env
import hashlib
def hash_file(path, algorithm='sha256'):
"""Compute hash of a data file for version tracking."""
h = hashlib.new(algorithm)
with open(path, 'rb') as f:
for chunk in iter(lambda: f.read(8192), b''):
h.update(chunk)
return h.hexdigest()