Skip to content
Automation
Skill

/nanoresearch-planning

Produce an experiment blueprint from a research hypothesis

From plugin
nanoresearch
1.3k16 skills9 commands

Claude Code

$ npx -y skills add OpenRaiser/NanoResearch --skill nanoresearch-planning --agent claude-code

SKILL.md

nanoresearch-planning.SKILL.md
name: nanoresearch-planning
description: Produce an experiment blueprint from a research hypothesis
version: 0.1.0

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
Read more
@openraiser
Ships withnanoresearch

端到端自主 AI 科研引擎 — 从研究想法到完整论文,全程自动化 快速开始 · 效果展示 · 流水线 · Claude Code · 飞书机器人 🔬 NanoResearch 真正运行计算实验——它不仅生成代码,还能将代码提交到 GPU 集群执行训练,收集真实实验结果,生成论文配图,最终输出一篇有实验数据支撑的完整 LaTeX 论文。论文中的每一个数据、表格、图表都来自实际运行的实验结果,而非 LLM 编造。

Stars on GitHub

Momentum

stars in total
1.3k
Star history of OpenRaiser/NanoResearch on GitHub, a chart by star-history.com

Other skills on nanoresearch.

academic-plotting

academic-plotting

Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system…

accelerate

accelerate

Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for…

More skills from other plugins.

agent-builder

agent-builder

Load immediately after an Agent intent, before the first agent_builder_* call. Governs how you create, configure, test, and…

n8n11d209k
@n8n-io@n8n-ioInstall Skill
browser-use

browser-use

Direct browser control via CDP for web interaction: automation, scraping, testing, screenshots, and site/app work. Also covers…

@browser-use@browser-useInstall Skill
agent-browser

agent-browser

Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling…

@vercel-labs-2@vercel-labs-2Install Skill
add-anydoc

add-anydoc

Add local office-document-to-Markdown conversion to NanoClaw agent containers with the pinned Firecrawl AnyDoc CLI. Use when…

@nanocoai@nanocoaiInstall Skill

Questions about nanoresearch-planning.

Which coding agents can use nanoresearch-planning?
nanoresearch-planning installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.