nanoresearch-experiment
Generate a Python code skeleton from an experiment blueprint
npx skills add OpenRaiser/NanoResearch --skill nanoresearch-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.
# Experiment Skill ## Purpose Take the experiment blueprint and produce a runnable Python code skeleton that implements the proposed method, baselines, training loops, evaluation harness, and ablation configurations. ## Tools Required None. This skill operates entirely through LLM code generation based on the experiment blueprint. ## Input - `experiment_blueprint`: Path to `papers/experiment_blueprint.json` produced by the planning skill ## Process 1. Parse the experiment blueprint for datasets, baselines, metrics, and ablation groups 2. Generate the project directory structure (data loaders, models, training, evaluation, configs) 3. Produce data loading and preprocessing code for each specified dataset 4. Implement model architecture stubs for the proposed method and each baseline 5. Generate training loop with logging, checkpointing, and early stopping 6. Implement the evaluation harness computing all specified metrics 7. Create configuration files for each ablation group 8. Add a main entry point that accepts a config and runs the full train-evaluate pipeline ## Output Produces `experiments/` directory containing: - `data/`: Data loading and preprocessing modules - `models/`: Mo
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- Tools Required
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What does the nanoresearch-experiment skill do?
Generate a Python code skeleton from an experiment blueprint
How do I install it?
Run `npx skills add OpenRaiser/NanoResearch --skill nanoresearch-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 OpenRaiser/NanoResearch, a repository with 1,480 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.