📝
WritingPython

NanoResearch Writing

by OpenRaiser

NanoResearch Writing is a Writing skill for Claude Code, published by OpenRaiser in NanoResearch.

1.4K stars95 forkson OpenRaiser/NanoResearchAdded 2026/08/19Repository updated 2026/05/26
agent-skillsagentsaiai-agentsai-scientistartificial-intelligenceautonomous-agentsautonomous-researchautoresearchclaude-codeclaude-skillsnanobotopenclaw
Install in seconds
Install NanoResearch Writing
Copy NanoResearch Writing 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-writing ~/.claude/skills/nanoresearch-writing

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-writing/SKILL.md in OpenRaiser/NanoResearch
Installs to
~/.claude/skills/nanoresearch-writing
Collection
One of 16 skills cataloged from this repository
Category
Writing1012 skills

What NanoResearch Writing does

NanoResearch Writing drafts a complete LaTeX research paper from upstream ideation, planning, and experiment outputs. Use it when you need to turn experiment results and a blueprint into a publication-ready PDF with figures, tables, and bibliography.

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

Documentation

README

Writing Skill

Purpose

Take all previous outputs (ideation, planning, experiment results) and produce a complete LaTeX paper draft with figures, tables, and bibliography.

Tools Required

  • generate_latex: Generate and assemble LaTeX source files for each paper section
  • compile_pdf: Compile the LaTeX source into a PDF document
  • generate_figure: Produce publication-quality figures from experiment results

Input

  • ideation_output: Path to papers/ideation_output.json from the ideation skill
  • experiment_blueprint: Path to papers/experiment_blueprint.json from the planning skill
  • experiment_results: Path to experiments/ directory containing code and results from the experiment skill

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

Frequently asked about NanoResearch Writing

  • What else does OpenRaiser publish alongside NanoResearch Writing?

    NanoResearch Writing 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 Writing 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 Writing compare to other Writing skills?

    NanoResearch Writing ranks #341 by stars among the 1012 Writing skills in this catalog. The most-starred ones next to it are Article Writing, Social Media Content Calendar and Knowledge Comic Creator. 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 Writing against them. Open each page to compare what they document and how they install.

More from OpenRaiser/NanoResearch

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

See all 16 skills
📊
2w ago

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.
Data
1.4K95
🔬
2w ago

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.
AI Engineering
1.4K95
💡
2w ago

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.
AI Engineering
1.4K95
🚀
2w ago

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.
AI Engineering
1.4K95
📊
2w ago

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.
AI Engineering
1.4K95
📝
2w ago

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.
Writing
1.4K95