Best use case
Academic Writing is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
## Overview
Teams using Academic Writing 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/writing/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Academic Writing Compares
| Feature / Agent | Academic Writing | 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?
## Overview
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
# Academic Writing ## Overview Scientific manuscript writing across all formats, fields, and journals. ## Manuscript Structure (IMRAD) ### Abstract (150-300 words) - Background: 1-2 sentences establishing context - Objective: 1 sentence stating the research question - Methods: 2-3 sentences on approach (design, subjects, analyses) - Results: 2-3 sentences on key findings (with numbers) - Conclusion: 1-2 sentences on significance and implications ### Introduction (funnel structure) 1. Broad context (what is known) 2. Narrowing focus (what is specifically relevant) 3. Knowledge gap (what is NOT known) 4. Study rationale (why THIS study) 5. Objectives/hypothesis (what we aimed to do) ### Methods (reproducibility is key) - Study design and setting - Participants/samples (inclusion/exclusion criteria) - Variables and measurements - Statistical analysis plan (pre-specified) - Ethics approval and consent ### Results (data-driven narrative) - Present results in the order of objectives - Reference every figure and table - Report exact statistics (test, df, p, effect size, CI) - Do NOT interpret results here ### Discussion 1. Summary of main findings (1 paragraph) 2. Comparison with existing literature 3. Possible mechanisms/explanations 4. Strengths of the study 5. Limitations (be honest and specific) 6. Future directions 7. Conclusion (clinical/scientific significance) ## Citation Formatting Support 100+ citation styles via CSL. Common: - APA 7th: (Author, Year) / Author (Year) - Vancouver: [1], [2] — numbered order of appearance - Nature: superscript 1,2 - Cell: (Author et al., Year) ## Writing Tone by Journal Tier - **Nature/Science**: Concise, high-impact. Every sentence earns its place. - **Cell**: Comprehensive, mechanistic depth. Detailed Methods. - **Lancet/NEJM**: Clinical focus, patient implications. Accessible language. - **PNAS/eLife**: Balanced depth. Cross-disciplinary appeal. - **PLoS ONE**: Methodological rigor over novelty. ## Bilingual Support - English: standard academic conventions - Chinese: different paragraph logic, more contextual setup, appropriate deference patterns
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