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JamesFincher
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research-paper-writing

Coordinate literature review, experiment design, and verified citations for ML research papers.

Updated May 4, 2026
Search Tags:#citation verification#latex#ml#experiments#submission#research workflow#paper writing
One-click install
npx skills add https://github.com/JamesFincher/gengar --skill research-paper-writing-jamesfincher
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: research-paper-writing
Source: https://github.com/JamesFincher/gengar/tree/main/skills/research/research-paper-writing
Command: npx skills add https://github.com/JamesFincher/gengar --skill research-paper-writing-jamesfincher

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?

Research paper writing for ML/AI is slow, iterative, and error-prone—especially when experiments, claims, baselines, citations, and venue requirements must all stay consistent. This skill helps you produce a coherent, submission-ready draft that is grounded in verified evidence instead of guesswork.

Core Features & Use Cases

  • End-to-end paper pipeline: Iteratively handles literature review, experiment design, execution/monitoring, analysis, drafting, and submission preparation.
  • Claim-to-evidence discipline: Forces an explicit mapping from paper claims to the experiments that support them.
  • Citation hallucination prevention: Provides a workflow to verify sources programmatically and marks anything unverifiable as [CITATION NEEDED].
  • Conference-aware structure: Uses venue conventions for sections, checklists, and pre-submission requirements (e.g., NeurIPS/ICML/ICLR/ACL/AAAI/COLM norms).
  • Experiment logging & traceability: Produces an experiment log that bridges raw results to narrative prose.
  • Iterative refinement loop: Encourages feedback-driven revision (including an autoreason methodology reference) rather than linear drafting.

Quick Start

Use the research-paper-writing skill to turn a repo with partial results into a full iterative draft targeting NeurIPS/ICML/ICLR, and keep citations and claims aligned to verified experiments.

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 an ML research paper with verified citations and avoid hallucinating BibTeX entries?▼

Writing an ML research paper with verified citations requires programmatic source verification instead of memory-based BibTeX entries, marking any unverifiable references as [CITATION NEEDED] to ensure submission-ready academic integrity.

What's the best way to map ML paper claims to specific experiments and results?▼

Mapping ML paper claims to experiments requires an explicit claim-to-evidence discipline framework that enforces structured experiment logs, bridging raw computational results directly to the narrative prose of your publication-ready draft.

Can I format my machine learning paper for NeurIPS, ICML, or ICLR submission automatically?▼

Formatting machine learning papers for NeurIPS, ICML, ICLR, ACL, AAAI, or COLM submission requires applying conference-aware structures that automatically handle venue-specific sections, checklists, and pre-submission requirements.

Does this research workflow handle iterative drafting and reviewer responses for ML papers?▼

ML research workflow iterative drafting handles feedback-driven revision loops for reviewer responses, applying an autoreason methodology to converge on a consistent narrative rather than relying on linear drafting processes.

How do I integrate arxiv and Semantic Scholar literature reviews into my paper writing process?▼

Integrating arxiv and Semantic Scholar literature reviews into paper writing uses programmatic dependencies to coordinate source retrieval, verify citations, and ground empirical and non-empirical claims in verified evidence.

Do I need LaTeX and SciencePlots to generate publication-ready figures for my ML experiments?▼

Generating publication-ready figures for ML experiments uses LaTeX and SciencePlots with matplotlib to produce venue-compliant visualizations that align with structured experiment logs and claim-driven results analysis.

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