Manuscript writing with structured outlines, full-paragraph prose, and publication-ready formatting.
Works with
Two-stage writing process: create section outlines with research-lookup, then convert to flowing paragraphs (never submit bullet points in final manuscripts)
Supports IMRAD structure, alternative formats (reviews, case reports, meta-analyses), and discipline-specific terminology across biomedical, molecular, chemistry, ecology, physics, and social sciences
Handles citations in APA, AM
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 davila7/claude-code-templates 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
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
1
total installs
1
this week
24.2K
GitHub stars
0
upvotes
Run in your terminal
1
installs
1
this week
24.2K
stars
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 at least 1-2 AI-generated figures using the scientific-schematics skill.
This is not optional. Scientific papers without visual elements are incomplete. Before finalizing any document:
How to generate figures:
How to generate schematics:
python scripts/generate_schematic.py "your diagram description" -o figures/output.png
The AI will automatically:
When to add schematics:
For detailed guidance on creating schematics, refer to the scientific-schematics 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:
Integration with Research Lookup:
The research-lookup skill is essential for Stage 1 (creating outlines):
This two-stage process ensures you:
Adapt 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:
Physics and Engineering:
Neuroscience:
Social and Behavioral Sciences:
General Principles:
Match Audience Expertise:
Define Technical Terms Strategically:
Maintain Consistency:
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
davila7/claude-code-templates
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
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.
Registry listing for scientific-writing matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in scientific-writing — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added scientific-writing from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
scientific-writing reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: scientific-writing is focused, and the summary matches what you get after install.
Keeps context tight: scientific-writing is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend scientific-writing for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
scientific-writing is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
showing 1-10 of 50