scientific-manuscript-review▌
lyndonkl/claude · updated May 17, 2026
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This skill provides systematic review and editing of scientific manuscripts (research articles, reviews, perspectives) to improve clarity, structure, scientific rigor, and reader comprehension. It applies a multi-pass approach covering structure, scientific logic, language, and formatting to transform drafts into publication-ready documents.
Scientific Manuscript Review
Table of Contents
- Purpose
- When to Use
- Core Principles
- Workflow
- Section-by-Section Review
- Language Guidelines
- Guardrails
- Quick Reference
Purpose
This skill provides systematic review and editing of scientific manuscripts (research articles, reviews, perspectives) to improve clarity, structure, scientific rigor, and reader comprehension. It applies a multi-pass approach covering structure, scientific logic, language, and formatting to transform drafts into publication-ready documents.
When to Use
Use this skill when:
- Drafting manuscripts: Research articles, short communications, review papers, perspectives
- Pre-submission review: Final polish before journal submission
- Revision cycles: Addressing reviewer comments, improving based on feedback
- Collaborative editing: Reviewing co-author drafts, mentoring student writing
- Self-editing: Systematic review of your own writing for blind spots
- Journal transfer: Adapting manuscript for different journal format
Trigger phrases: "manuscript review", "paper draft", "journal article", "research writing", "improve my paper", "reviewer feedback", "submission ready", "scientific writing"
Do NOT use for:
- Grant proposals (use
grant-proposal-assistant) - Recommendation letters (use
academic-letter-architect) - General emails (use
scientific-email-polishing)
Core Principles
Seven foundational beliefs guiding manuscript review:
- Clarity over cleverness: Scientific clarity is more important than stylistic elegance
- Narrative shapes comprehension: Structure and story arc determine reader understanding
- Audience dictates tone: Expert vs. general audience requires different depth and framing
- Format signals credibility: Professional formatting reflects scientific rigor
- Claims require evidence: Strong assertions need strong data and appropriate hedging
- Each section has a job: Introduction sells the problem, Results show the data, Discussion interprets
- Constraints shape structure: Word limits and journal guidelines determine emphasis
Workflow
Copy this checklist and track your progress:
Manuscript Review Progress:
- [ ] Step 1: Identify manuscript type and extract core message
- [ ] Step 2: Structural pass - map and evaluate overall organization
- [ ] Step 3: Introduction review - gap statement, focus, hypothesis
- [ ] Step 4: Results review - question, approach, finding, interpretation
- [ ] Step 5: Discussion review - synthesis, context, limitations
- [ ] Step 6: Scientific clarity check - claims, controls, hedging
- [ ] Step 7: Language polish - terminology, voice, jargon
- [ ] Step 8: Formatting check - journal compliance
Step 1: Identify Manuscript Type and Core Message
Determine document type (research article, review, perspective, short communication). Extract the ONE finding or message readers must remember. Ask: "If readers remember only one thing, what should it be?" See resources/methodology.md for extraction techniques.
Step 2: Structural Pass
Map overall organization against standard IMRaD (Introduction, Methods, Results, Discussion) or review structure. Check logical sequencing - does each section flow into the next? Identify unclear transitions or missing context. See resources/methodology.md for structure evaluation.
Step 3: Introduction Review
Evaluate using the Introduction Arc: Broad context → Narrow focus → Knowledge gap → Hypothesis/Objective. Check that gap statement is explicit and compelling. Verify ending with clear hypothesis or objective. See resources/template.md for template.
Step 4: Results Review
For each figure/table/experiment: Question addressed? → Approach used? → Key finding (with statistics)? → Interpretation (what it means)? Flag data-dump writing that lacks interpretation. Ensure findings build toward core message. See resources/template.md for results structure.
Step 5: Discussion Review
Verify structure: Revisit hypothesis → Interpret findings in field context → Place in broader literature → Acknowledge limitations → Suggest future directions. Check for overclaiming (speculation presented as fact). Ensure clear separation of data interpretation vs. speculation. See resources/methodology.md for discussion framework.
Step 6: Scientific Clarity Check
Run the clarity checklist: Claims supported by data? Quantitative details present (statistics, n values)? Controls adequately described? Interpretations appropriately hedged? Mechanistic explanations where needed? See resources/template.md for full checklist.
Step 7: Language Polish
Ensure terminology consistency throughout. Remove or define jargon on first use. Prefer active voice when it aids clarity. Standardize abbreviations. Check for hedging language ("suggests" vs "proves"). See resources/methodology.md for specific guidance.
Step 8: Formatting Check
Verify compliance with target journal guidelines (word limits, reference format, figure requirements). Check section headings match journal requirements. Ensure abstract follows structured/unstructured requirement. Validate using resources/evaluators/rubric_scientific_manuscript.json. Minimum standard: Average score ≥ 3.5.
Section-by-Section Review
Introduction Structure
Goal: Convince readers the problem matters and your approach is sound
The Funnel Structure:
[Broad context - establish field importance, 1-2 sentences]
↓
[Narrow to specific area - what's been done]
↓
[Knowledge gap - what's missing, why it matters]
↓
[Your hypothesis/objective - what you will address]
Common problems:
- Gap statement buried or implicit (make it explicit: "However, X remains unknown")
- Too broad opening (readers don't need history of the universe)
- No clear hypothesis at end (readers don't know what to expect)
- Overlong literature review (move details to Discussion)
Results Structure
Goal: Present data clearly with interpretation, not just numbers
Per-paragraph/figure structure:
[Question this experiment addresses]
[Approach/method used]
[Key finding - with quantification]
[Brief interpretation - what this means]
Common problems:
- Data dump (listing results without interpretation)
- Missing statistics (p-values, n values, confidence intervals)
- Vague descriptions ("we found differences" vs "we found 3-fold increase")
- Figures not referenced in logical order
- Key findings buried in text (highlight important results)
Discussion Structure
Goal: Interpret findings and place in broader context
Standard flow:
[Restate main finding and hypothesis status]
↓
[Interpret key results in field context]
↓
[Compare to prior literature - agreements/disagreements]
↓
[Mechanistic implications (if applicable)]
↓
[Limitations - honest acknowledgment]
↓
[Future directions - what comes next]
↓
[Concluding statement - big picture significance]
Common problems:
- Overclaiming (data doesn't support conclusions)
- Repeating Results section (discuss, don't recapitulate)
- Missing limitations (reviewers will note them anyway)
- Speculation unmarked (clearly label "we speculate that...")
- No connection to field (discuss in isolation)
Language Guidelines
Active vs. Passive Voice:
- Use active for clarity: "We measured" not "Measurements were made"
- Use passive when agent is obvious or unimportant: "Samples were incubated at 37°C"
- Avoid dangling modifiers: Not "Having analyzed the data, the conclusion was..." but "Having analyzed the data, we concluded..."
Hedging Language:
- Strong data: "demonstrates", "shows", "establishes"
- Moderate confidence: "suggests", "indicates", "supports"
- Speculation: "may", "might", "could potentially"
- Match hedge strength to evidence strength
Jargon Management:
- Define on first use: "polymerase chain reaction (PCR)"
- Avoid unnecessary jargon when plain language works
- Field-standard terms don't need definition (DNA, protein, cell)
- Reader-appropriate: more definition for broad audience journals
Terminology Consistency:
- Pick one term and stick with it (don't alternate between "subjects", "participants", "patients")
- Create terminology table for complex manuscripts
- Check abbreviations defined before use
Guardrails
Critical requirements:
-
Preserve author voice: Edit for clarity, don't rewrite. Never invent claims or change meaning. Mark suggestions clearly when proposing new content.
-
Claims match data: Every conclusion must be supported by presented results. Flag overclaiming immediately. Speculation must be labeled.
-
Quantitative rigor: Statistics required for comparisons. N values for all experiments. Significance thresholds stated. Variability measures included.
-
Logical flow: Each section should flow naturally to the next. Transitions explicit. Conclusions follow from premises.
-
Appropriate hedging: Strong claims need strong evidence. Use hedging language proportional to certainty.
-
Consistent terminology: Same concept = same term throughout. Abbreviations defined before use.
Common pitfalls:
- ❌ Overclaiming: "This proves X" when data only suggests
- ❌ Missing context: Results without interpretation
- ❌ Buried lede: Important finding hidden in paragraph
- ❌ Inconsistent terms: Alternating between synonyms
- ❌ Dense paragraphs: Walls of text without breaks
- ❌ Vague descriptions: "Some increase" instead of "3-fold increase"
Quick Reference
Key resources:
- resources/methodology.md: Detailed review methods, structural assessment, language guidelines
- resources/template.md: Introduction arc, results paragraph, clarity checklist
- resources/evaluators/rubric_scientific_manuscript.json: Quality scoring criteria
Introduction checklist:
- Broad context establishes importance
- Narrows to specific problem
- Gap statement explicit ("However, X remains unknown")
- Ends with clear hypothesis or objective
Results checklist:
- Each experiment has question, approach, finding, interpretation
- Statistics present (p-values, n, confidence intervals)
- Quantitative descriptions (numbers, not "some/many")
- Figures referenced in logical order
- Key findings highlighted
Discussion checklist:
- Opens by revisiting hypothesis
- Interprets (doesn't just repeat) results
- Places in literature context
- Acknowledges limitations
- Suggests future directions
- Speculation clearly labeled
Typical review time:
- Quick review (structure + major issues): 20-30 minutes
- Standard review (full checklist): 45-60 minutes
- Deep revision (rewriting sections): 2-3 hours
Inputs required:
- Manuscript draft (any stage)
- Target journal (if known)
- Specific concerns from author (if any)
Outputs produced:
- Edited manuscript with tracked changes
- Commentary on major structural/logic changes
- Summary of key improvements made
How to use scientific-manuscript-review 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-manuscript-review
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches scientific-manuscript-review from GitHub repository lyndonkl/claude 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-manuscript-review. Access the skill through slash commands (e.g., /scientific-manuscript-review) 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
4.8★★★★★63 reviews- ★★★★★Chaitanya Patil· Dec 24, 2024
scientific-manuscript-review is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Ama Menon· Dec 8, 2024
scientific-manuscript-review is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Anaya Bhatia· Dec 4, 2024
scientific-manuscript-review fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Lucas Perez· Nov 27, 2024
We added scientific-manuscript-review from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Advait Chawla· Nov 27, 2024
Keeps context tight: scientific-manuscript-review is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Nia Rahman· Nov 23, 2024
Registry listing for scientific-manuscript-review matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Min Okafor· Nov 23, 2024
We added scientific-manuscript-review from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Piyush G· Nov 15, 2024
Keeps context tight: scientific-manuscript-review is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Lucas Sanchez· Nov 7, 2024
scientific-manuscript-review fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Advait Gonzalez· Nov 3, 2024
Solid pick for teams standardizing on skills: scientific-manuscript-review is focused, and the summary matches what you get after install.
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