paper-writing-assistant
Assist in drafting research papers and meeting notes, enforcing academic rigor and formatting.
Best use case
paper-writing-assistant is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Assist in drafting research papers and meeting notes, enforcing academic rigor and formatting.
Teams using paper-writing-assistant should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/paper-writing-assistant/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How paper-writing-assistant Compares
| Feature / Agent | paper-writing-assistant | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Assist in drafting research papers and meeting notes, enforcing academic rigor and formatting.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Paper Writing Assistant Skill
## Value Proposition
Transforms raw experimental data and informal notes into high-quality academic text suitable for paper submissions or professor meetings.
## When to Use
- **Drafting Sections**: When the user asks to write "Abstract", "Methodology", or "Results".
- **Meeting Prep**: When creating summaries for advisor meetings (e.g., "Prepare for the Friday meeting").
- **Reformatting**: When converting code comments or rough notes into LaTeX or polished Markdown.
## Instructions
1. **Structure**: Adhere to the user's preferred structure:
- **Background**: Context of the problem.
- **Analysis**: Method of investigation.
- **Findings**: Data-backed results.
- **Conclusion**: Summary and next steps.
2. **Data First**: Always prioritize quantitative data (metrics, tables) over qualitative descriptions.
3. **Comparison**: When comparing models, use tables.
- Compare parameters, training data size, inference speed, and accuracy.
4. **Citations**: Use standard citation markers (e.g., `[Author, Year]`) or BibTeX keys if provided.
## Best Practices
- **Visuals**: Suggest placeholder figures where data is dense (e.g., `[Figure 1: Success Rate vs Training Steps]`).
- **Tone**: Maintain a formal, academic tone. Avoid slang or overly casual language.
- **LaTeX Support**: Be ready to generate LaTeX snippets for tables and equations.Related Skills
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