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AI EngineeringPython

NanoResearch Planning

NanoResearch Planning is an AI Engineering skill for Claude Code, published by OpenRaiser in NanoResearch.

1.4K stars95 forkson OpenRaiser/NanoResearchAdded 2026/08/19Repository updated 2026/05/26

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agent-skillsagentsaiai-agentsai-scientistartificial-intelligenceautonomous-agentsautonomous-researchautoresearchclaude-codeclaude-skillsnanobotopenclaw

Install in seconds

Install NanoResearch Planning

Copy NanoResearch Planning into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

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

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/OpenRaiser/NanoResearch.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: skills/nanoresearch-planning/SKILL.md in OpenRaiser/NanoResearch
Installs to: ~/.claude/skills/nanoresearch-planning
Collection: One of 16 skills cataloged from this repository
Category: AI Engineering — 2451 skills

What NanoResearch Planning does

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

NanoResearch Planning is cataloged under AI Engineering on DirSkills. NanoResearch Planning comes from a repository tagged agent-skills, agents, ai, ai-agents and ai-scientist.

Documentation

README

Excerpt

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

This is the opening of the README. Read the full README on GitHub.

Frequently asked about NanoResearch Planning

What else does OpenRaiser publish alongside NanoResearch Planning?
NanoResearch Planning is one of 16 skills that DirSkills catalogs from OpenRaiser/NanoResearch, the repository it ships in. Its siblings there include Academic Plotting, Autoresearch and Creative Thinking for Research. Each one is a separate skill with its own page in this directory, installs the same way NanoResearch Planning does, and is maintained by OpenRaiser in that same repository. The rest of the collection is listed on the OpenRaiser/NanoResearch page.
How does NanoResearch Planning compare to other AI Engineering skills?
NanoResearch Planning ranks #1079 by stars among the 2451 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of NanoResearch Planning against them. Open each page to compare what they document and how they install.

More from OpenRaiser/NanoResearch

NanoResearch Planning is one of 16 skills cataloged on DirSkills from OpenRaiser/NanoResearch.

See all 16 skills →

Academic Plotting

Academic Plotting generates publication-quality figures for ML papers from research context or experimental data. It creates architecture diagrams via Gemini and data-driven charts via matplotlib/seaborn, auto-selecting chart types and highlighting key results.

Autoresearch

Autoresearch orchestrates end-to-end autonomous AI research projects using a two-loop architecture for rapid experiments and periodic synthesis, routing to domain-specific skills and producing papers; use when starting a research project or managing multi-hypothesis experiments.

Creative Thinking for Research

Creative Thinking for Research applies eight cognitive science frameworks to generate novel research directions in computer science and AI. Use it when seeking genuinely novel directions via combinatorial creativity, analogical reasoning, and constraint manipulation.

Hugging Face Accelerate

Hugging Face Accelerate simplifies distributed training for PyTorch models by adding only four lines of code. Use it to run the same script on single or multiple GPUs, with mixed precision, DeepSpeed, or FSDP.

LLM Evaluation Harness

LLM Evaluation Harness evaluates LLMs across 60+ academic benchmarks using standardized prompts and metrics. Use when benchmarking model quality, comparing models, or tracking training progress.

ML Paper Writing

ML Paper Writing drafts publication-ready ML/AI/systems papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM, OSDI, NSDI, ASPLOS, SOSP. Use it to turn research repos into structured papers, find and verify citations, and apply conference LaTeX templates.

More AI Engineering skills

NanoResearch Planning is one of 2451 AI Engineering skills cataloged on DirSkills, ranked #1079 by stars among them.

Browse all AI Engineering skills →

Architecture Decision Records

Architecture Decision Records captures architectural decisions made during Claude Code sessions as structured ADRs, recording context, alternatives, and rationale. Use it when significant technical choices are made so future developers understand why the codebase is shaped the way it is.

AI-First Engineering

AI-First Engineering defines process, review, and architecture standards for teams where AI agents produce most implementation output. Use it to set ownership rules, quality gates, and evaluation criteria for agent-generated code.

Agentic OS

Agentic OS codifies a persistent multi-agent operating system architecture for Claude Code. Use it when building a multi-agent workflow with file-based memory, slash commands, and scheduled automation that survives session restarts.