Claude Scholar is a semi-automated research assistant for academic research and software development, supporting literature review, coding, experiments, reporting, writing, and project knowledge management. Computer science and AI researchers use it across the research workflow with several coding-agent platforms; the catalogue contains its skills, commands, agents, hooks, plugin, and instruction.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Galaxy-Dawn/claude-scholar --skill ml-paper-writinggit clone --depth 1 https://github.com/Galaxy-Dawn/claude-scholarWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/galaxy-dawn/claude-scholar/ml-paper-writing)<a href="https://agentmods.dev/skills/galaxy-dawn/claude-scholar/ml-paper-writing"><img src="https://agentmods.dev/badge/skills/galaxy-dawn/claude-scholar/ml-paper-writing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/galaxy-dawn/claude-scholar/ml-paper-writing"><img src="https://agentmods.dev/badge/skills/galaxy-dawn/claude-scholar/ml-paper-writing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00072 | $0.10333 |
| Opus 5 | $0.00036 | $0.05167 |
| Sonnet 5 | $0.00014 | $0.02067 |
| Haiku 4.5 | $0.00007 | $0.01033 |
Grade A, and why
ml-paper-writing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
89% identical to ml-paper-writing — 359 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 1,123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ML Paper Writing for Top AI Conferences
Expert-level guidance for writing publication-ready papers targeting NeurIPS, ICML, ICLR, ACL, AAAI, and COLM. This skill combines writing philosophy from top researchers (Nanda, Farquhar, Karpathy, Lipton, Steinhardt) with practical tools: LaTeX templates, citation verification APIs, and conference checklists.
Default operating order
Use this skill in the following order unless the task is unusually narrow:
- lock the operating mode from
references/OPERATING-MODES.md, - understand the repo or draft context,
- use
references/citation-workflow.mdas the canonical citation authority, - load venue- or template-specific references only after the main writing path is clear.
Google Scholar may still help with manual discovery, but it is not the canonical verification authority in this skill. Default verification should use programmatic sources such as Semantic Scholar, CrossRef, and arXiv.
Claim ledger gate
Before a project plan, experiment note, or literature summary becomes manuscript prose:
- identify the Claim Candidate or Evidence Record that supports the sentence,
- preserve allowed wording and forbidden stronger wording,
- keep project plans as hypotheses unless experiment artifacts or verified papers support them,
- do not turn related-work motivation into evidence for the paper's own result,
- mark unsupported claims as
[CLAIM NEEDS EVIDENCE]instead of polishing them.
If the repo context is clear enough for a first draft, still apply this gate before stating contributions, results, related-work contrasts, or rebuttal-facing claims.
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:
- Understand the project by exploring the repo, results, and existing documentation
- Deliver a complete first draft when confident about the contribution
- Search literature using web search and APIs to find relevant citations
- Refine through feedback cycles when the scientist provides input
- Ask for clarification only when genuinely uncertain about key decisions
What ships with it
60 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/checklists.md 11 KB
- references/citation-workflow.md 15 KB
- references/knowledge/design-simplification-papers-kaiming-he.md 21 KB
- references/knowledge/kaiming_he_injection_record.json 6.0 KB
- references/knowledge/paper-miner-writing-memory.md 1.1 KB
- references/knowledge/README.md 1.5 KB
- references/knowledge/rethinking-papers-kaiming-he.md 33 KB
- references/knowledge/review-response.md 12 KB
- references/knowledge/structure.md 9.8 KB
- references/knowledge/submission-guides.md 8.7 KB
- references/knowledge/theory-driven-papers-kaiming-he.md 19 KB
- references/knowledge/writing-techniques.md 19 KB
- references/literature-research/arxiv-search-guide.md 5.5 KB
- references/literature-research/paper-quality-criteria.md 7.0 KB
- references/OPERATING-MODES.md 921 B
- references/reviewer-guidelines.md 10 KB
- references/sources.md 7.1 KB
- references/writing-guide.md 16 KB
- templates/aaai2026/aaai2026-unified-supp.tex 4.4 KB
- templates/aaai2026/aaai2026-unified-template.tex 62 KB
- templates/aaai2026/aaai2026.bib 4.7 KB
- templates/aaai2026/aaai2026.bst 29 KB
- templates/aaai2026/aaai2026.sty 12 KB
- templates/aaai2026/README.md 18 KB
- templates/acl/acl_latex.tex 14 KB
- templates/acl/acl_lualatex.tex 3.0 KB
- templates/acl/acl_natbib.bst 44 KB
- templates/acl/acl.sty 11 KB
- templates/acl/anthology.bib.txt 1.1 KB
- templates/acl/custom.bib 2.0 KB
- templates/acl/formatting.md 18 KB
- templates/acl/README.md 2.1 KB
- templates/colm2025/colm2025_conference.bib 496 B
- templates/colm2025/colm2025_conference.bst 26 KB
- templates/colm2025/colm2025_conference.pdf 120 KB
- templates/colm2025/colm2025_conference.sty 7.5 KB
- templates/colm2025/colm2025_conference.tex 13 KB
- templates/colm2025/fancyhdr.sty 20 KB
- templates/colm2025/math_commands.tex 12 KB
- templates/colm2025/natbib.sty 44 KB
- templates/colm2025/README.md 51 B
- templates/iclr2026/fancyhdr.sty 20 KB
- templates/iclr2026/iclr2026_conference.bib 629 B
- templates/iclr2026/iclr2026_conference.bst 26 KB
- templates/iclr2026/iclr2026_conference.pdf 196 KB
- templates/iclr2026/iclr2026_conference.sty 8.8 KB
- templates/iclr2026/iclr2026_conference.tex 17 KB
- templates/iclr2026/math_commands.tex 12 KB
- templates/iclr2026/natbib.sty 44 KB
- templates/icml2026/algorithm.sty 2.2 KB
- templates/icml2026/algorithmic.sty 7.2 KB
- templates/icml2026/example_paper.bib 2.0 KB
- templates/icml2026/example_paper.pdf 189 KB
- templates/icml2026/example_paper.tex 29 KB
- templates/icml2026/fancyhdr.sty 31 KB
- templates/icml2026/icml_numpapers.pdf 2.8 KB
- templates/icml2026/icml2026.bst 27 KB
- templates/icml2026/icml2026.sty 27 KB
- templates/neurips2025/extra_pkgs.tex 2.8 KB
- templates/neurips2025/main.tex 574 B
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 1,123 lines · 72 tokens per session scan A 6f6ed76de172
ml-paper-writing is a skill published in the GitHub repository Galaxy-Dawn/claude-scholar (5,419 stars, last pushed 15d ago), licensed MIT. It adds 72 tokens to every session and 10,333 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to ml-paper-writing, differing in 359 lines, and is treated as a copy.
Other skills, from other repositories
ml-paper-writing
Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verification workflows.
ml-paper-writing
Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. For systems venues (OSDI, NSDI, ASPLOS, SOSP), use systems-paper-writing instead.
ml-paper-writing
Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM.
ml-paper-writing
Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verification workflows.
ml-paper-writing
Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, conducting literature reviews, finding related work, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, citation verification workflows, and paper…
ml-paper-writing
Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. For systems venues (OSDI, NSDI, ASPLOS, SOSP), use systems-paper-writing instead.