academic-paper-skills

🥈Silver

Automates academic paper planning and writing for philosophy and interdisciplinary research. Uses Claude Code to create structured workflows with quality checkpoints. Benefits researchers and academics by streamlining manuscript preparation.

2330Updated Today

Intermediate30min to implementautomation

Saves ~60 min per use

Quick InstallView Source

git clone https://github.com/lishix520/academic-paper-skills.git

Works with:

Claude

Overview

About This Skill

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/lishix520/academic-paper-skills

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

I'm writing an academic paper on [TOPIC] in [INDUSTRY]. Create a structured outline with sections for introduction, literature review, methodology, results, and conclusion. Include 3-5 key points for each section and suggest 5 relevant academic sources. Use Claude Code to create a workflow with quality checkpoints at each stage.

Example Output

# Academic Paper Outline: The Impact of AI on Modern Education

## Introduction
- Brief overview of AI's growing role in education
- Importance of understanding AI's impact on learning outcomes
- Research objectives and questions
- Scope and limitations of the study

## Literature Review
- Key studies on AI in education (e.g., Holmes et al., 2020)
- Gaps in current research on AI's impact on critical thinking
- Theoretical frameworks for analyzing AI's educational role
- Comparative analysis of AI applications in K-12 vs. higher education

## Methodology
- Mixed-methods approach: quantitative surveys and qualitative interviews
- Participant selection criteria (e.g., educators, students, administrators)
- Data collection instruments and procedures
- Ethical considerations and IRB approval process

## Results
- Quantitative findings on AI adoption rates and perceived effectiveness
- Qualitative themes from educator interviews about AI integration challenges
- Comparative analysis of AI's impact on different educational levels
- Unexpected findings and their implications

## Conclusion
- Summary of key findings
- Implications for educational policy and practice
- Limitations and suggestions for future research
- Call to action for stakeholders

### Recommended Sources
1. Holmes, W., Bialik, M., & Fiennes, L. (2020). *AI in Education: A Systematic Review*. Journal of Educational Technology, 15(3), 45-67.
2. ...