📝
WritingPython

ML Paper Writing

by OpenRaiser

ML Paper 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 ML Paper Writing
Copy ML Paper 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/vendor-ai-research/ml-paper-writing ~/.claude/skills/ml-paper-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/vendor-ai-research/ml-paper-writing/SKILL.md in OpenRaiser/NanoResearch
Installs to
~/.claude/skills/ml-paper-writing
Collection
One of 16 skills cataloged from this repository
Category
Writing1012 skills

What ML Paper Writing does

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.

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

Documentation

README

ML Paper Writing for Top AI & Systems Conferences

Expert-level guidance for writing publication-ready papers targeting NeurIPS, ICML, ICLR, ACL, AAAI, COLM (ML/AI venues) and OSDI, NSDI, ASPLOS, SOSP (Systems venues). This skill combines writing philosophy from top researchers (Nanda, Farquhar, Karpathy, Lipton, Steinhardt) with practical tools: LaTeX templates, citation verification APIs, and conference checklists.

Core Philosophy: Collaborative Writing

Paper writing is collaborative, but Claude should be proactive in delivering drafts.

The typical workflow starts with a research repository containing code, results, and experimental artifacts. Claude's role is to:

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

Frequently asked about ML Paper Writing

  • What else does OpenRaiser publish alongside ML Paper Writing?

    ML Paper 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 ML Paper 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 ML Paper Writing compare to other Writing skills?

    ML Paper Writing ranks #340 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 ML Paper Writing against them. Open each page to compare what they document and how they install.

More from OpenRaiser/NanoResearch

ML Paper 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 Training Recipes

ML Training Recipes provides battle-tested PyTorch training patterns for LLMs, vision, diffusion, medical imaging, and protein/drug discovery. Use it when training or fine-tuning neural networks, debugging loss spikes/OOM, choosing architectures, or optimizing GPU throughput.
AI Engineering
1.4K95