NanoResearch Experiment Scaffolder

What it does

This is the experiment stage of the NanoResearch pipeline. It reads papers/experiment_blueprint.json (produced by the planning skill), extracts the datasets, baselines, metrics, and ablation groups, and generates a complete Python project scaffold.

Generated output:

• data/ — data loading and preprocessing modules
• models/ — architecture stubs for the proposed method and each baseline
• training/ — training loop with logging, checkpointing, early stopping
• evaluation/ — metric computation and result aggregation
• configs/ — YAML configs per experiment and ablation variant
• run.py, requirements.txt — entry point and dependency list

No external tools or API keys required; it works purely through LLM code generation.

Who it's for

• Grad students and researchers tired of rebuilding experiment code for every paper
• ML engineers who want baselines and ablations in one consistent structure
• Users already running NanoResearch's idea → plan → experiment workflow

Example uses

1. Paper reproduction: a blueprint listing 2 datasets and 3 baselines yields matching model stubs, data loaders, and a shared training loop in one pass.
2. Ablation setup: each ablation group (drop a loss term, change attention heads) gets its own YAML so you can run python run.py --config configs/ablation_a.yaml immediately.
3. Team template: every new project opens with the same directory conventions, making code review and handoff far easier.

Install the skill I found on Claude Skill Mart.
Copy the skills/nanoresearch-experiment folder from the GitHub repo OpenRaiser/NanoResearch into my ~/.claude/skills/nanoresearch-experiment/.
When it's done, tell me in one line what this skill can do.