nanoresearch-planning
Produce an experiment blueprint from a research hypothesis
npx skills add OpenRaiser/NanoResearch --skill nanoresearch-planning --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.
# Planning Skill ## Purpose Take the selected hypothesis from ideation and produce a detailed experiment blueprint specifying datasets, baselines, evaluation metrics, and ablation groups. ## Tools Required None. This skill operates entirely through LLM reasoning over the ideation output. ## Input - `ideation_output`: Path to `papers/ideation_output.json` produced by the ideation skill ## Process 1. Parse the selected hypothesis and supporting literature from the ideation output 2. Identify candidate datasets that are publicly available and appropriate for validating the hypothesis 3. Select 2-4 baseline methods from the surveyed literature for comparison 4. Define primary and secondary evaluation metrics aligned with the hypothesis 5. Design ablation groups that isolate each novel component of the proposed approach 6. Estimate computational requirements and timeline for each experiment 7. Compile everything into a structured experiment blueprint ## Output Produces `papers/experiment_blueprint.json` containing: - Selected hypothesis (carried forward) - Dataset specifications (name, source, splits, preprocessing steps) - Baseline methods with references - Evaluation metrics and succe
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What does the nanoresearch-planning skill do?
Produce an experiment blueprint from a research hypothesis
How do I install it?
Run `npx skills add OpenRaiser/NanoResearch --skill nanoresearch-planning --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.