research-paper-writing

Name: research-paper-writing
Availability: InStock
Author: aissenliu

Orchestrate ML/AI research paper development from experiment design to submission.

Updated Apr 27, 2026

Search Tags:#reproducibility #latex #academic-writing #citation-verification #research-pipeline #experiment-design #paper-writing

Ready to run

npx skills add https://github.com/AissenLiu/EasyHermes --skill research-paper-writing-aissenliu

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: research-paper-writing
Source: https://github.com/AissenLiu/EasyHermes/tree/main/hermes-agent/skills/research/research-paper-writing
Command: npx skills add https://github.com/AissenLiu/EasyHermes --skill research-paper-writing-aissenliu

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Provides a structured, automated workflow to manage the full lifecycle of ML/AI research papers—from experiment design and data collection to drafting, revision, and submission—while enforcing reproducibility and citation integrity.

Core Features & Use Cases

End-to-end pipeline: plan, execute, analyze experiments; draft sections; manage revisions; submit to conferences.
Reproducibility & citations: maintain experiment logs, generate and verify BibTeX entries, and track baselines.
Collaboration-friendly: supports modular components (experiments, references) for team workflows across NeurIPS/ICML/ICLR/ACL/AAAI/COLM.

Quick Start

Initialize a workspace and start collecting results to generate your first paper draft.

Frequently Asked Questions about research-paper-writing

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

FAQPage Schema

How do I manage the end-to-end ML research paper writing pipeline from experiment design to final draft?▼

To manage the end-to-end research paper writing pipeline, you can orchestrate experiment design, data collection, statistical analysis, and iterative drafting within a structured workspace. This enforces reproducibility by maintaining automated experiment logs and tracking baselines throughout the development lifecycle.

Can I automate BibTeX citation verification and reproducibility artifact tracking for conference submissions?▼

Yes, you can automate BibTeX citation verification and reproducibility artifact tracking. The system generates and verifies BibTeX entries against established databases, ensuring citation integrity for ML/AI conference submissions like NeurIPS, ICML, and ICLR.

Does the research paper writing workflow support statistical analysis using scipy and numpy for experiment planning?▼

Yes, the research paper writing workflow supports statistical analysis using scipy and numpy. It applies these dependencies to execute rigorous experiment planning, analyze collected data, and generate visualizations via matplotlib for your reproducibility artifacts.

What is the best way to structure iterative writing and revision for an academic ML paper?▼

The best way to structure iterative writing and revision is to use a modular workflow that separates experiments, drafting, and references. This collaboration-friendly approach supports modular components to manage team workflows across conferences like ACL, AAAI, and COLM.

Do I need semantic scholar and arxiv dependencies to verify references in my research pipeline?▼

Yes, you need semantic scholar and arxiv dependencies to verify references in your research pipeline. These tools are integrated into the citation workflow to automatically fetch, generate, and verify BibTeX entries for accurate baseline tracking.

How does this academic writing pipeline handle reproducibility for machine learning experiments?▼

This academic writing pipeline handles reproducibility for machine learning experiments by enforcing automated monitoring and logging throughout the experiment execution phase. It maintains detailed experiment logs and tracks baselines to generate verifiable reproducibility artifacts.