ml-experiment
Design and run machine learning experiments with proper evaluation using jupyter_execute, including training, benchmarking, and ablation studies
Best use case
ml-experiment is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Design and run machine learning experiments with proper evaluation using jupyter_execute, including training, benchmarking, and ablation studies
Teams using ml-experiment should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/prismer-ml-experiment/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ml-experiment Compares
| Feature / Agent | ml-experiment | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Design and run machine learning experiments with proper evaluation using jupyter_execute, including training, benchmarking, and ablation studies
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# ML Experiment Skill ## Description Design, implement, and evaluate machine learning experiments with reproducible workflows, proper baselines, and statistical analysis. ## Tools Used - `jupyter_execute` - Execute ML code in Python (auto-switches to Jupyter) - `jupyter_notebook` - Manage experiment notebooks - `update_notebook` - Set up experiment cells - `update_latex` - Write experiment results to papers - `latex_compile` - Compile CS conference papers (auto-switches to LaTeX) - `arxiv_to_prompt` - Read related work from arXiv papers - `update_notes` - Write experiment logs and analysis summaries ## Capabilities ### Experiment Design - Proper train/validation/test splits - Cross-validation and bootstrap confidence intervals - Ablation study design - Hyperparameter search (grid, random, Bayesian) ### Implementation - PyTorch and TensorFlow model building - Data loading and augmentation pipelines - Training loops with logging and checkpointing - Distributed training setup ### Evaluation - Standard metrics per task (accuracy, F1, BLEU, mAP, etc.) - Statistical significance testing (paired t-test, bootstrap) - Comparison with baselines - Error analysis and visualization ## Usage Patterns ### Run an Experiment When user says: "Train a model for [task]" 1. Clarify dataset, metrics, and baselines 2. Implement data loading and preprocessing 3. Build model architecture 4. Train with proper logging 5. Evaluate and compare to baselines 6. Report results with confidence intervals ### Reproduce a Paper When user says: "Reproduce [paper title/arXiv ID]" 1. Fetch paper using arxiv_to_prompt 2. Extract key method details 3. Implement core algorithm 4. Run experiments matching paper setup 5. Compare results to reported numbers
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