### Scientific Writing
Works with
name: "scientific-writing"
description: "Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research..."
allowed-tools: "Read Write Edit Bash"
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionscientific-writingExecute the skills CLI command in your project's root directory to begin installation:
Fetches scientific-writing from K-Dense-AI/scientific-agent-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate scientific-writing. Access via /scientific-writing in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
1
total installs
1
this week
0
upvotes
Run in your terminal
1
installs
1
this week
—
stars
| name | scientific-writing |
| description | Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA/AMA/Vancouver), figures/tables, reporting guidelines (CONSORT/STROBE/PRISMA), for research papers and journal submissions. |
| allowed-tools | Read Write Edit Bash |
| license | MIT license |
| metadata | version: "1.0" skill-author: K-Dense Inc. |
This is the core skill for the deep research and writing tool—combining AI-driven deep research with well-formatted written outputs. Every document produced is backed by comprehensive literature search and verified citations through the research-lookup skill.
Scientific writing is a process for communicating research with precision and clarity. Write manuscripts using IMRAD structure, citations (APA/AMA/Vancouver), figures/tables, and reporting guidelines (CONSORT/STROBE/PRISMA). Apply this skill for research papers and journal submissions.
Critical Principle: Always write in full paragraphs with flowing prose. Never submit bullet points in the final manuscript. Use a two-stage process: first create section outlines with key points using research-lookup, then convert those outlines into complete paragraphs.
This skill should be used when:
⚠️ MANDATORY: Every scientific paper MUST include a graphical abstract plus 1-2 additional AI-generated figures using the scientific-schematics skill.
This is not optional. Scientific papers without visual elements are incomplete. Before finalizing any document:
Every scientific writeup MUST include a graphical abstract. This is a visual summary of your paper that:
Generate the graphical abstract FIRST:
python scripts/generate_schematic.py "Graphical abstract for [paper title]: [brief description showing workflow from input → methods → key findings → conclusions]" -o figures/graphical_abstract.png
Graphical Abstract Requirements:
[HH:MM:SS] GENERATED: Graphical abstract for paper summary⚠️ CRITICAL: Use BOTH scientific-schematics AND generate-image EXTENSIVELY throughout all documents.
Every document should be richly illustrated. Generate figures liberally - when in doubt, add a visual.
MINIMUM Figure Requirements:
| Document Type | Minimum | Recommended |
|---|---|---|
| Research Papers | 5 | 6-8 |
| Literature Reviews | 4 | 5-7 |
| Market Research | 20 | 25-30 |
| Presentations | 1/slide | 1-2/slide |
| Posters | 6 | 8-10 |
| Grants | 4 | 5-7 |
| Clinical Reports | 3 | 4-6 |
Use scientific-schematics EXTENSIVELY for technical diagrams:
python scripts/generate_schematic.py "your diagram description" -o figures/output.png
Use generate-image EXTENSIVELY for visual content:
python scripts/generate_image.py "your image description" -o figures/output.png
The AI will automatically:
When in Doubt, Generate a Figure:
For detailed guidance, refer to the scientific-schematics and generate-image skill documentation.
IMRAD Format: Guide papers through the standard Introduction, Methods, Results, And Discussion structure used across most scientific disciplines. This includes:
For detailed guidance on IMRAD structure, refer to references/imrad_structure.md.
Alternative Structures: Support discipline-specific formats including:
Abstract Composition: Craft concise, standalone summaries (100-250 words) that capture the paper's purpose, methods, results, and conclusions. Support both structured abstracts (with labeled sections) and unstructured single-paragraph formats.
Introduction Development: Build compelling introductions that:
Methods Documentation: Ensure reproducibility through:
Results Presentation: Present findings with:
Discussion Construction: Synthesize findings by:
Apply citation styles correctly across disciplines. For comprehensive style guides, refer to references/citation_styles.md.
Major Citation Styles:
Best Practices:
Create effective data visualizations that enhance comprehension. For detailed best practices, refer to references/figures_tables.md.
When to Use Tables vs. Figures:
Design Principles:
Common Figure Types:
Ensure completeness and transparency by following established reporting standards. For comprehensive guideline details, refer to references/reporting_guidelines.md.
Key Guidelines:
Each guideline provides checklists ensuring all critical methodological elements are reported.
Apply fundamental scientific writing principles. For detailed guidance, refer to references/writing_principles.md.
Clarity:
Conciseness:
Accuracy:
Objectivity:
CRITICAL: Always write in full paragraphs, never submit bullet points in scientific papers.
Scientific papers must be written in complete, flowing prose. Use this two-stage approach for effective writing:
Stage 1: Create Section Outlines with Key Points
When starting a new section:
Example outline (Introduction section):
- Background: AI in drug discovery gaining traction
* Cite recent reviews (Smith 2023, Jones 2024)
* Traditional methods are slow and expensive
- Gap: Limited application to rare diseases
* Only 2 prior studies (Lee 2022, Chen 2023)
* Small datasets remain a challenge
- Our approach: Transfer learning from common diseases
* Novel architecture combining X and Y
- Study objectives: Validate on 3 rare disease datasets
Stage 2: Convert Key Points to Full Paragraphs
Once the outline is complete, expand each bullet point into proper prose:
Example conversion to prose:
Artificial intelligence approaches have gained significant traction in drug discovery
pipelines over the past decade (Smith, 2023; Jones, 2024). While these computational
methods show promise for accelerating the identification of therapeutic candidates,
traditional experimental approaches remain slow and resource-intensive, often requiring
years of laboratory work and substantial financial investment. However, the application
of AI to rare diseases has been limited, with only two prior studies demonstrating
proof-of-concept results (Lee, 2022; Chen, 2023). The primary obstacle has been the
scarcity of training data for conditions affecting small patient populations.
To address this challenge, we developed a transfer learning approach that leverages
knowledge from well-characterized common diseases to predict therapeutic targets for
rare conditions. Our novel neural architecture combines convolutional layers for
molecular feature extraction with attention mechanisms for protein-ligand interaction
modeling. The objective of this study was to validate our approach across three
independent rare disease datasets, assessing both predictive accuracy and biological
interpretability of the results.
Key Differences Between Outlines and Final Text:
| Outline (Planning Stage) | Final Manuscript |
|---|---|
| Bullet points and fragments | Complete sentences and paragraphs |
| Telegraphic notes | Full explanations with context |
| List of citations | Citations integrated into prose |
| Abbreviated ideas | Developed arguments with transitions |
| For your eyes only | For publication and peer review |
Common Mistakes to Avoid:
When Lists ARE Acceptable (Limited Cases):
Lists may appear in scientific papers only in specific contexts:
Abstract Format Rule:
Integration with Research Lookup:
The research-lookup skill is essential for Stage 1 (creating outlines):
This two-stage process ensures you:
For research reports, technical reports, white papers, and other professional documents that are NOT journal manuscripts, use the scientific_report.sty LaTeX style package for a polished, professional appearance.
When to Use Professional Report Formatting:
When NOT to Use (Use Venue-Specific Formatting Instead):
venue-templates skillvenue-templates skillThe scientific_report.sty Style Package Provides:
| Feature | Description |
|---|---|
| Typography | Helvetica font family for modern, professional appearance |
| Color Scheme | Professional blues, greens, and accent colors |
| Box Environments | Colored boxes for key findings, methods, recommendations, limitations |
| Tables | Alternating row colors, professional headers |
| Figures | Consistent caption formatting |
| Scientific Commands | Shortcuts for p-values, effect sizes, confidence intervals |
Box Environments for Content Organization:
% Key findings (blue) - for major discoveries
\begin{keyfindings}[Title]
Content with key findings and statistics.
\end{keyfindings}
% Methodology (green) - for methods highlights
\begin{methodology}[Study Design]
Description of methods and procedures.
\end{methodology}
% Recommendations (purple) - for action items
\begin{recommendations}[Clinical Implications]
\begin{enumerate}
\item Specific recommendation 1
\item Specific recommendation 2
\end{enumerate}
\end{recommendations}
% Limitations (orange) - for caveats and cautions
\begin{limitations}[Study Limitations]
Description of limitations and their implications.
\end{limitations}
Professional Table Formatting:
\begin{table}[htbp]
\centering
\caption{Results Summary}
\begin{tabular}{@{}lccc@{}}
\toprule
\textbf{Variable} & \textbf{Treatment} & \textbf{Control} & \textbf{p} \\
\midrule
Outcome 1 & \meansd{42.5}{8.3} & \meansd{35.2}{7.9} & <.001\sigthree \\
\rowcolor{tablealt} Outcome 2 & \meansd{3.8}{1.2} & \meansd{3.1}{1.1} & .012\sigone \\
Outcome 3 & \meansd{18.2}{4.5} & \meansd{17.8}{4.2} & .58\signs \\
\bottomrule
\end{tabular}
{\small \siglegend}
\end{table}
Scientific Notation Commands:
| Command | Output | Purpose |
|---|---|---|
\pvalue{0.023} | p = 0.023 | P-values |
\psig{< 0.001} | p = < 0.001 | Significant p-values (bold) |
\CI{0.45}{0.72} | 95% CI [0.45, 0.72] | Confidence intervals |
\effectsize{d}{0.75} | d = 0.75 | Effect sizes |
\samplesize{250} | n = 250 | Sample sizes |
\meansd{42.5}{8.3} | 42.5 ± 8.3 | Mean with SD |
\sigone, \sigtwo, \sigthree | *, **, *** | Significance stars |
Getting Started:
\documentclass[11pt,letterpaper]{report}
\usepackage{scientific_report}
\begin{document}
\makereporttitle
{Report Title}
{Subtitle}
{Author Name}
{Institution}
{Date}
% Your content with professional formatting
\end{document}
Compilation: Use XeLaTeX or LuaLaTeX for proper Helvetica font rendering:
xelatex report.tex
For complete documentation, refer to:
assets/scientific_report.sty: The style packageassets/scientific_report_template.tex: Complete template exampleassets/REPORT_FORMATTING_GUIDE.md: Quick reference guidereferences/professional_report_formatting.md: Comprehensive formatting guideAdapt manuscripts to journal requirements:
Adapt language, terminology, and conventions to match the specific scientific discipline. Each field has established vocabulary, preferred phrasings, and domain-specific conventions that signal expertise and ensure clarity for the target audience.
Identify Field-Specific Linguistic Conventions:
Biomedical and Clinical Sciences:
Molecular Biology and Genetics:
Chemistry and Pharmaceutical Sciences:
Ecology and Environmental Sciences:
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
K-Dense-AI/scientific-agent-skills
K-Dense-AI/scientific-agent-skills
K-Dense-AI/scientific-agent-skills
K-Dense-AI/scientific-agent-skills
google-deepmind/science-skills
BuilderIO/skills
Solid pick for teams standardizing on skills: scientific-writing is focused, and the summary matches what you get after install.
scientific-writing reduced setup friction for our internal harness; good balance of opinion and flexibility.
scientific-writing has been reliable in day-to-day use. Documentation quality is above average for community skills.
We added scientific-writing from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend scientific-writing for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
scientific-writing fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
scientific-writing fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Useful defaults in scientific-writing — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Keeps context tight: scientific-writing is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added scientific-writing from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
showing 1-10 of 29