experiment-tracker
Manages ML experiment tracking with MLflow, Weights & Biases, or SpecWeave's built-in tracking. Activates for "track experiments", "MLflow", "wandb", "experiment logging", "compare experiments", "hyperparameter tracking". Automatically configures tracking tools to log to SpecWeave increment folders, ensuring all experiments are documented and reproducible. Integrates with SpecWeave's living docs for persistent experiment knowledge.
npx skills add majiayu000/claude-skill-registry --skill experiment-tracker-anton-abyzov-specweave --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.
# Experiment Tracker ## Overview Transforms chaotic ML experimentation into organized, reproducible research. Every experiment is logged, versioned, and tied to a SpecWeave increment, ensuring team knowledge is preserved and experiments are reproducible. ## Problem This Solves **Without structured tracking**: - ❌ "Which hyperparameters did we use for model v2?" - ❌ "Why did we choose XGBoost over LightGBM?" - ❌ "Can't reproduce results from 3 months ago" - ❌ "Team member left, all knowledge in their notebooks" **With experiment tracking**: - ✅ All experiments logged with params, metrics, artifacts - ✅ Decisions documented ("XGBoost: 5% better precision, chose it") - ✅ Reproducible (environment, data version, code hash) - ✅ Team knowledge in living docs, not individual notebooks ## How It Works ### Auto-Configuration When you create an ML increment, the skill detects tracking tools: ```python # No configuration needed - automatically detects and configures from specweave import track_experiment # Automatically logs to: # .specweave/increments/0042.../experiments/exp-001/ with track_experiment("baseline-model") as exp: model.fit(X_train, y_train) exp.log_metric("accuracy", accuracy)
- Overview
- Problem This Solves
- How It Works
- Auto-Configuration
- Tracking Backends
- Experiment Comparison
- Living Docs Integration
- When to Use This Skill
- Key Features
- 1. Automatic Logging
- 2. Hyperparameter Tracking
- 3. Cross-Validation Tracking
- 4. Artifact Management
- 5. Experiment Metadata
Experiments automatically tied to increment All experiments logged to: .specweave/increments/0042.../experiments/ Sync experiment findings to docs Create issue for model retraining Links to previous experiments in increment List all experiments in increment Compare experiments Load experiment details Export experiment data
What does the experiment-tracker skill do?
Manages ML experiment tracking with MLflow, Weights & Biases, or SpecWeave's built-in tracking. Activates for "track experiments", "MLflow", "wandb", "experiment logging", "compare experiments", "hyperparameter tracking". Automatically configures tracking tools to log to SpecWeave increment folders, ensuring all experiments are documented and reproducible. Integrates with SpecWeave's living docs for persistent experiment knowledge.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill experiment-tracker-anton-abyzov-specweave --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.