ml-experiment
Track ML experiments with proper logging and reproducibility. Use when training models or running experiments.
npx skills add majiayu000/claude-skill-registry --skill ml-experiment --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Reproducibility Checklist - [ ] Random seeds set (Python, NumPy, PyTorch/TensorFlow) - [ ] Data version tracked - [ ] Code version tracked (git commit) - [ ] Environment captured (requirements.txt) - [ ] Hyperparameters logged - [ ] Metrics logged - [ ] Model artifacts saved ## Setting Random Seeds ```python import random import numpy as np def set_seeds(seed: int = 42) -> None: """Set all random seeds for reproducibility.""" random.seed(seed) np.random.seed(seed) # PyTorch try: import torch torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False except ImportError: pass # TensorFlow try: import tensorflow as tf tf.random.set_seed(seed) except ImportError: pass ``` ## Experiment Logging Template ```python from datetime import datetime from pathlib import Path import json class Experiment: def __init__(self, name: str): self.name = name self.timestamp = datetime.now().isoformat() self.params = {} self.metrics = {} self.artifacts = [] def log_params(self, **params): self.params.update(params) def log_metrics(self, **metrics): self.metrics.update(metrics) def log_artifact(self, path: str): self.artifa
- Reproducibility Checklist
- Setting Random Seeds
- Experiment Logging Template
- Hyperparameter Organization
- Model Checkpointing
- Experiment Comparison
- Directory Structure
What does the ml-experiment skill do?
Track ML experiments with proper logging and reproducibility. Use when training models or running experiments.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ml-experiment --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.