research-paper-writing

Design, execute, analyze, and submit AI conference research papers.

Updated Jun 4, 2026

Search Tags:#collaboration #experiment design #deep learning #research paper #AI conference

Ready to run

npx skills add https://github.com/kngender5/hermes --skill research-paper-writing-kngender5

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: research-paper-writing
Source: https://github.com/kngender5/hermes/tree/main/skills/research/research-paper-writing
Command: npx skills add https://github.com/kngender5/hermes --skill research-paper-writing-kngender5

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill solves the challenge of producing high-quality research papers for top AI conferences, offering an end-to-end workflow from experiment design to submission.

Core Features & Use Cases

End-to-End Pipeline: Covers experiment design, execution, analysis, writing, review, and submission.
Iterative Process: Supports iterative improvement through continuous feedback loops.
Collaboration Tools: Integrates with version control and collaboration platforms for multi-author workflows.

Quick Start

Use the research-paper-writing skill to initiate the research paper writing process for the paper on 'Deep Learning for Image Recognition'.

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 an end-to-end AI conference paper submission workflow?▼

AI conference paper writing requires an end-to-end pipeline from experiment design to submission. This involves iterative improvement through structured feedback loops and version control integration to ensure reproducibility and high-quality results for leading AI conferences.

Does this research paper pipeline support multi-author collaboration with version control?▼

Yes, multi-author collaboration is supported through integration with version control systems like Git. This enables multi-author workflows, tracks iterative changes, and maintains reproducibility across distributed research teams during the paper writing process.

How do I design and execute deep learning experiments for reproducible research?▼

Designing deep learning experiments for reproducible research requires structured experiment design, execution, and analysis. Integrating Python automation with NumPy, SciPy, and Matplotlib ensures reproducible results across iterative feedback loops during the research process.

Can I automate literature review using arXiv and Semantic Scholar for AI research papers?▼

Yes, literature review can be automated by integrating arXiv and Semantic Scholar dependencies. These platforms provide access to academic papers and metadata, streamlining the reference gathering and analysis phase of AI research paper writing.

What Python dependencies are required for automated research paper writing and analysis?▼

Automated research paper writing requires Python dependencies including Semantic Scholar, arXiv, Habanero, Requests, SciPy, NumPy, Matplotlib, and SciencePlots. These facilitate literature access, data analysis, visualization, and version control integration for the complete pipeline.

What are the limitations of automating AI conference paper submissions?▼

Automating AI conference paper submissions requires Python proficiency and cannot replace human scientific judgment. Limitations include the need for manual oversight of iterative feedback loops, interpretation of complex deep learning results, and manual handling of conference-specific formatting requirements.