---
name: NanoResearch Planning
slug: nanoresearch-planning
category: AI Engineering
description: NanoResearch Planning produces an experiment blueprint from a selected research hypothesis, specifying datasets, baselines, evaluation metrics, and ablation groups.
github: "https://github.com/OpenRaiser/NanoResearch/tree/main/skills/nanoresearch-planning"
language: Python
stars: 1357
forks: 95
install: "npx degit https://github.com/OpenRaiser/NanoResearch/tree/main/skills/nanoresearch-planning ~/.claude/skills/nanoresearch-planning"
installs_to: ~/.claude/skills/nanoresearch-planning
source_path: skills/nanoresearch-planning/SKILL.md
collection_size: 16
category_size: 2451
collection_url: "https://dirskills.com/collections/OpenRaiser/NanoResearch"
added: 2026-08-19T07:28:00.561Z
last_synced: 2026-08-19T07:28:00.561Z
canonical_url: "https://dirskills.com/skills/nanoresearch-planning"
---

# NanoResearch Planning

NanoResearch Planning produces an experiment blueprint from a selected research hypothesis, specifying datasets, baselines, evaluation metrics, and ablation groups.

**Install:**

```bash
npx degit https://github.com/OpenRaiser/NanoResearch/tree/main/skills/nanoresearch-planning ~/.claude/skills/nanoresearch-planning
```

## README

# 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 success criteria
- Ablation study design (groups, variables, expected outcomes)
- Resource estimates and experiment schedule