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CHENHUI-X
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research-paper-writing

Guides end-to-end ML research paper production from experiment design through conference submission.

Updated Jul 3, 2026
Search Tags:#machine learning#citations#statistical analysis#latex#research paper#experiments#conference submission
One-click install
npx skills add https://github.com/CHENHUI-X/toolbox --skill research-paper-writing-chenhui-x
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: research-paper-writing
Source: https://github.com/CHENHUI-X/toolbox/tree/main/custom-skills/research/research-paper-writing
Command: npx skills add https://github.com/CHENHUI-X/toolbox --skill research-paper-writing-chenhui-x

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires semanticscholar, arxiv, habanero, requests, scipy, numpy, matplotlib, SciencePlots, and includes references (resource) and assets (resource) components.

What problem does it solve? Writing a publication-ready ML/AI paper involves coordinating literature review, experiment design, statistical analysis, LaTeX drafting, citation verification, and venue-specific formatting—a process where missed steps (hallucinated citations, missing checklists, weak baselines) cause desk rejections. ## Core Features & Use Cases - Full Research Lifecycle: Eight iterative phases covering project setup, literature review, experiment design, execution monitoring, statistical analysis, drafting, self-review, and submission for NeurIPS, ICML, ICLR, ACL, AAAI, and COLM. - Verified Citation Workflow: Programmatic BibTeX retrieval via Semantic Scholar, CrossRef, and arXiv APIs with a mandatory 5-step verification process to prevent hallucinated references. - Venue Templates & Checklists: Official LaTeX templates for six conferences plus pre-submission checklists covering page limits, reproducibility statements, and ethics requirements. - Use Case: A researcher with experimental results asks the agent to draft an ICML submission—the skill produces a grounded draft using the experiment log, verified citations, the ICML 2026 template, and a simulated reviewer pass before submission. ## Quick Start Ask the agent to help write a research paper from your existing codebase and results, specifying your target venue such as NeurIPS or ICML.

Frequently Asked Questions about research-paper-writing

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I write a machine learning paper for NeurIPS or ICML?▼

Follow an iterative pipeline: define a one-sentence contribution, map every experiment to a claim, run baselines with statistical significance tests, then draft using the venue's official LaTeX template. Finish with a simulated self-review and the venue's mandatory checklist before submitting.

How do I prevent hallucinated citations in AI-generated papers?▼

Never generate BibTeX from memory—fetch it programmatically via DOI content negotiation from CrossRef. Verify each paper exists in at least two sources (Semantic Scholar plus arXiv or CrossRef), and mark anything unverifiable as a placeholder citation.

What LaTeX templates are included for conference submissions?▼

Official templates are provided for ICML 2026, ICLR 2026, NeurIPS 2025, ACL, AAAI 2026, and COLM 2025, each with the correct style file, bibliography style, and example document. Page limits range from 7 pages (AAAI) to 9 pages (ICLR, NeurIPS, COLM).

Does this workflow support human evaluation studies?▼

Yes, it covers human evaluation design including annotator selection, pairwise versus Likert scales, inter-annotator agreement metrics like Krippendorff's alpha, crowdsourcing platforms such as Prolific, and the reporting details reviewers at ACL and EMNLP expect.

What statistical tests should ML papers report?▼

Report error bars with the method specified, 95% confidence intervals via bootstrapping, McNemar's test for paired method comparisons, and effect sizes such as Cohen's h. Always state sample sizes and number of independent runs.

When should I use iterative refinement versus single-pass drafting?▼

Use autoreason-style iterative refinement for mid-tier models on constrained tasks, where the generation-evaluation gap is largest. For frontier models on unconstrained tasks, single-pass or critique-and-revise performs better since synthesis drift prevents convergence.

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