Individual skill
ml-research-lab
Machine-learning research loop for dataset curation, fine-tuning, evaluation, inference deployment, experiment tracking, and model explainability. Use when working on ML experiments, training data, model benchmarks, RunPod/GPU runs, classifier quality, vLLM/GGUF serving, SHAP-style model explanations, or research-to-code iterations. Do not use for a simple code edit that has no ML dataset, metric, model, or experiment artifact.
skills/ai-ml/ml-research-lab/SKILL.md
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- Last checked
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- Repository last updated
- Nov 7, 2026
- License
- MIT
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- ai-agents Repository topic
- claude Repository topic
- claude-code Repository topic
- llm Repository topic
- machine-learning Repository topic
- prompt-engineering Repository topic
- skills Repository topic
- mcp Repository topic
- agent-harness Repository topic
- harness Repository topic
- data Derived category
- devops Derived category
- research Derived category
- education Derived category
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# ML Research Lab
Use this skill as the compact router for ML work. It is derived from an audit of
`synthetic-sciences/openscience` at commit `531467c`, but does not require running
OpenScience or loading its full 250+ skill set.
## Operating Loop
1. Freeze the question as a measurable hypothesis.
2. Identify dataset provenance, labels, splits, leakage risks, and regeneration cost.
3. Pick the smallest baseline that can disprove the idea.
4. Define metrics before training. For release claims, require train/val/test split,
no test-set model selection, and multi-seed proof when cost permits.
5. Run or wire experiment tracking before long jobs start.