scientific-writing▌
davila7/claude-code-templates · updated Apr 8, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
Manuscript writing with structured outlines, full-paragraph prose, and publication-ready formatting.
- ›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
Scientific Writing
Overview
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.
When to Use This Skill
This skill should be used when:
- Writing or revising any section of a scientific manuscript (abstract, introduction, methods, results, discussion)
- Structuring a research paper using IMRAD or other standard formats
- Formatting citations and references in specific styles (APA, AMA, Vancouver, Chicago, IEEE)
- Creating, formatting, or improving figures, tables, and data visualizations
- Applying study-specific reporting guidelines (CONSORT for trials, STROBE for observational studies, PRISMA for reviews)
- Drafting abstracts that meet journal requirements (structured or unstructured)
- Preparing manuscripts for submission to specific journals
- Improving writing clarity, conciseness, and precision
- Ensuring proper use of field-specific terminology and nomenclature
- Addressing reviewer comments and revising manuscripts
Visual Enhancement with Scientific Schematics
⚠️ 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:
- Generate at minimum ONE schematic or diagram using scientific-schematics
- Prefer 2-3 figures for comprehensive papers (methods flowchart, results visualization, conceptual diagram)
How to generate figures:
- Use the scientific-schematics skill to generate AI-powered publication-quality diagrams
- Simply describe your desired diagram in natural language
- Nano Banana Pro will automatically generate, review, and refine the schematic
How to generate schematics:
python scripts/generate_schematic.py "your diagram description" -o figures/output.png
The AI will automatically:
- Create publication-quality images with proper formatting
- Review and refine through multiple iterations
- Ensure accessibility (colorblind-friendly, high contrast)
- Save outputs in the figures/ directory
When to add schematics:
- Study design and methodology flowcharts (CONSORT, PRISMA, STROBE)
- Conceptual framework diagrams
- Experimental workflow illustrations
- Data analysis pipeline diagrams
- Biological pathway or mechanism diagrams
- System architecture visualizations
- Any complex concept that benefits from visualization
For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.
Core Capabilities
1. Manuscript Structure and Organization
IMRAD Format: Guide papers through the standard Introduction, Methods, Results, And Discussion structure used across most scientific disciplines. This includes:
- Introduction: Establish research context, identify gaps, state objectives
- Methods: Detail study design, populations, procedures, and analysis approaches
- Results: Present findings objectively without interpretation
- Discussion: Interpret results, acknowledge limitations, propose future directions
For detailed guidance on IMRAD structure, refer to references/imrad_structure.md.
Alternative Structures: Support discipline-specific formats including:
- Review articles (narrative, systematic, scoping)
- Case reports and case series
- Meta-analyses and pooled analyses
- Theoretical/modeling papers
- Methods papers and protocols
2. Section-Specific Writing Guidance
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:
- Establish the research problem's importance
- Review relevant literature systematically
- Identify knowledge gaps or controversies
- State clear research questions or hypotheses
- Explain the study's novelty and significance
Methods Documentation: Ensure reproducibility through:
- Detailed participant/sample descriptions
- Clear procedural documentation
- Statistical methods with justification
- Equipment and materials specifications
- Ethical approval and consent statements
Results Presentation: Present findings with:
- Logical flow from primary to secondary outcomes
- Integration with figures and tables
- Statistical significance with effect sizes
- Objective reporting without interpretation
Discussion Construction: Synthesize findings by:
- Relating results to research questions
- Comparing with existing literature
- Acknowledging limitations honestly
- Proposing mechanistic explanations
- Suggesting practical implications and future research
3. Citation and Reference Management
Apply citation styles correctly across disciplines. For comprehensive style guides, refer to references/citation_styles.md.
Major Citation Styles:
- AMA (American Medical Association): Numbered superscript citations, common in medicine
- Vancouver: Numbered citations in square brackets, biomedical standard
- APA (American Psychological Association): Author-date in-text citations, common in social sciences
- Chicago: Notes-bibliography or author-date, humanities and sciences
- IEEE: Numbered square brackets, engineering and computer science
Best Practices:
- Cite primary sources when possible
- Include recent literature (last 5-10 years for active fields)
- Balance citation distribution across introduction and discussion
- Verify all citations against original sources
- Use reference management software (Zotero, Mendeley, EndNote)
4. Figures and Tables
Create effective data visualizations that enhance comprehension. For detailed best practices, refer to references/figures_tables.md.
When to Use Tables vs. Figures:
- Tables: Precise numerical data, complex datasets, multiple variables requiring exact values
- Figures: Trends, patterns, relationships, comparisons best understood visually
Design Principles:
- Make each table/figure self-explanatory with complete captions
- Use consistent formatting and terminology across all display items
- Label all axes, columns, and rows with units
- Include sample sizes (n) and statistical annotations
- Follow the "one table/figure per 1000 words" guideline
- Avoid duplicating information between text, tables, and figures
Common Figure Types:
- Bar graphs: Comparing discrete categories
- Line graphs: Showing trends over time
- Scatterplots: Displaying correlations
- Box plots: Showing distributions and outliers
- Heatmaps: Visualizing matrices and patterns
5. Reporting Guidelines by Study Type
Ensure completeness and transparency by following established reporting standards. For comprehensive guideline details, refer to references/reporting_guidelines.md.
Key Guidelines:
- CONSORT: Randomized controlled trials
- STROBE: Observational studies (cohort, case-control, cross-sectional)
- PRISMA: Systematic reviews and meta-analyses
- STARD: Diagnostic accuracy studies
- TRIPOD: Prediction model studies
- ARRIVE: Animal research
- CARE: Case reports
- SQUIRE: Quality improvement studies
- SPIRIT: Study protocols for clinical trials
- CHEERS: Economic evaluations
Each guideline provides checklists ensuring all critical methodological elements are reported.
6. Writing Principles and Style
Apply fundamental scientific writing principles. For detailed guidance, refer to references/writing_principles.md.
Clarity:
- Use precise, unambiguous language
- Define technical terms and abbreviations at first use
- Maintain logical flow within and between paragraphs
- Use active voice when appropriate for clarity
Conciseness:
- Eliminate redundant words and phrases
- Favor shorter sentences (15-20 words average)
- Remove unnecessary qualifiers
- Respect word limits strictly
Accuracy:
- Report exact values with appropriate precision
- Use consistent terminology throughout
- Distinguish between observations and interpretations
- Acknowledge uncertainty appropriately
Objectivity:
- Present results without bias
- Avoid overstating findings or implications
- Acknowledge conflicting evidence
- Maintain professional, neutral tone
7. Writing Process: From Outline to Full Paragraphs
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:
- Use the research-lookup skill to gather relevant literature and data
- Create a structured outline with bullet points marking:
- Main arguments or findings to present
- Key studies to cite
- Data points and statistics to include
- Logical flow and organization
- These bullet points serve as scaffolding—they are NOT the final manuscript
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:
- Transform bullet points into complete sentences with subjects, verbs, and objects
- Add transitions between sentences and ideas (however, moreover, in contrast, subsequently)
- Integrate citations naturally within sentences, not as lists
- Expand with context and explanation that bullet points omit
- Ensure logical flow from one sentence to the next within each paragraph
- Vary sentence structure to maintain reader engagement
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:
- ❌ Never leave bullet points in the final manuscript
- ❌ Never submit lists where paragraphs should be
- ❌ Don't use numbered or bulleted lists in Results or Discussion sections (except for specific cases like study hypotheses or inclusion criteria)
- ❌ Don't write sentence fragments or incomplete thoughts
- ✅ Do use occasional lists only in Methods (e.g., inclusion/exclusion criteria, materials lists)
- ✅ Do ensure every section flows as connected prose
- ✅ Do read paragraphs aloud to check for natural flow
When Lists ARE Acceptable (Limited Cases):
Lists may appear in scientific papers only in specific contexts:
- Methods: Inclusion/exclusion criteria, materials and reagents, participant characteristics
- Supplementary Materials: Extended protocols, equipment lists, detailed parameters
- Never in: Abstract, Introduction, Results, Discussion, Conclusions
Integration with Research Lookup:
The research-lookup skill is essential for Stage 1 (creating outlines):
- Search for relevant papers using research-lookup
- Extract key findings, methods, and data
- Organize findings as bullet points in your outline
- Then convert the outline to full paragraphs in Stage 2
This two-stage process ensures you:
- Gather and organize information systematically
- Create logical structure before writing
- Produce polished, publication-ready prose
- Maintain focus on the narrative flow
8. Journal-Specific Formatting
Adapt manuscripts to journal requirements:
- Follow author guidelines for structure, length, and format
- Apply journal-specific citation styles
- Meet figure/table specifications (resolution, file formats, dimensions)
- Include required statements (funding, conflicts of interest, data availability, ethical approval)
- Adhere to word limits for each section
- Format according to template requirements when provided
9. Field-Specific Language and Terminology
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:
- Review terminology used in recent high-impact papers in the target journal
- Note field-specific abbreviations, units, and notation systems
- Identify preferred terms (e.g., "participants" vs. "subjects," "compound" vs. "drug," "specimens" vs. "samples")
- Observe how methods, organisms, or techniques are typically described
Biomedical and Clinical Sciences:
- Use precise anatomical and clinical terminology (e.g., "myocardial infarction" not "heart attack" in formal writing)
- Follow standardized disease nomenclature (ICD, DSM, SNOMED-CT)
- Specify drug names using generic names first, brand names in parentheses if needed
- Use "patients" for clinical studies, "participants" for community-based research
- Follow Human Genome Variation Society (HGVS) nomenclature for genetic variants
- Report lab values with standard units (SI units in most international journals)
Molecular Biology and Genetics:
- Use italics for gene symbols (e.g., TP53), regular font for proteins (e.g., p53)
- Follow species-specific gene nomenclature (uppercase for human: BRCA1; sentence case for mouse: Brca1)
- Specify organism names in full at first mention, then use accepted abbreviations (e.g., Escherichia coli, then E. coli)
- Use standard genetic notation (e.g., +/+, +/-, -/- for genotypes)
- Employ established terminology for molecular techniques (e.g., "quantitative PCR" or "qPCR," not "real-time PCR")
Chemistry and Pharmaceutical Sciences:
- Follow IUPAC nomenclature for chemical compounds
- Use systematic names for novel compounds, common names for well-known substances
- Specify chemical structures using standard notation (e.g., SMILES, InChI for databases)
- Report concentrations with appropriate units (mM, μM, nM, or % w/v, v/v)
- Describe synthesis routes using accepted reaction nomenclature
- Use terms like "bioavailability," "pharmacokinetics," "IC50" consistently with field definitions
Ecology and Environmental Sciences:
- Use binomial nomenclature for species (italicized: Homo sapiens)
- Specify taxonomic authorities at first species mention when relevant
- Employ standardized habitat and ecosystem classifications
- Use consistent terminology for ecological metrics (e.g., "species richness," "Shannon diversity index")
- Describe sampling methods with field-standard terms (e.g., "transect," "quadrat," "mark-recapture")
Physics and Engineering:
- Follow SI units consistently unless field conventions dictate otherwise
- Use standard notation for physical quantities (scalars vs. vectors, tensors)
- Employ established terminology for phenomena (e.g., "quantum entanglement," "laminar flow")
- Specify equipment with model numbers and manufacturers when relevant
- Use mathematical notation consistent with field standards (e.g., ℏ for reduced Planck constant)
Neuroscience:
- Use standardized brain region nomenclature (e.g., refer to atlases like Allen Brain Atlas)
- Specify coordinates for brain regions using established stereotaxic systems
- Follow conventions for neural terminology (e.g., "action potential" not "spike" in formal writing)
- Use "neural activity," "neuronal firing," "brain activation" appropriately based on measurement method
- Describe recording techniques with proper specificity (e.g., "whole-cell patch clamp," "extracellular recording")
Social and Behavioral Sciences:
- Use person-first language when appropriate (e.g., "people with schizophrenia" not "schizophrenics")
- Employ standardized psychological constructs and validated assessment names
- Follow APA guidelines for reducing bias in language
- Specify theoretical frameworks using established terminology
- Use "participants" rather than "subjects" for human research
General Principles:
Match Audience Expertise:
- For specialized journals: Use field-specific terminology freely, define only highly specialized or novel terms
- For broad-impact journals (e.g., Nature, Science): Define more technical terms, provide context for specialized concepts
- For interdisciplinary audiences: Balance precision with accessibility, define terms at first use
Define Technical Terms Strategically:
- Define abbreviations at first use: "messenger RNA (mRNA)"
- Provide brief explanations for specialized techniques when writing for broader audiences
- Avoid over-defining terms well-known to the target audience (signals unfamiliarity with field)
- Create a glossary if numerous specialized terms are unavoidable
Maintain Consistency:
- Use the same term for th
How to use scientific-writing on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add scientific-writing
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches scientific-writing from GitHub repository davila7/claude-code-templates and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate scientific-writing. Access the skill through slash commands (e.g., /scientific-writing) or your agent's skill management interface.
Security & Verification Notice
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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
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
Competitive Analysis
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
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ 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.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★50 reviews- ★★★★★Harper Flores· Dec 20, 2024
scientific-writing reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Sofia Chawla· Dec 16, 2024
scientific-writing has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Ira Ghosh· Dec 16, 2024
Registry listing for scientific-writing matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Harper Singh· Dec 12, 2024
Useful defaults in scientific-writing — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Carlos Gonzalez· Nov 27, 2024
We added scientific-writing from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Yash Thakker· Nov 23, 2024
scientific-writing reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Ira Flores· Nov 7, 2024
Solid pick for teams standardizing on skills: scientific-writing is focused, and the summary matches what you get after install.
- ★★★★★Kofi Harris· Nov 7, 2024
Keeps context tight: scientific-writing is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Soo Abbas· Nov 3, 2024
I recommend scientific-writing for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Kofi Yang· Oct 26, 2024
scientific-writing is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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