skills/scientific-manuscript-review/SKILL.md
Guides systematic multi-pass review and editing of scientific manuscripts (research articles, reviews, perspectives) to improve clarity, structure, scientific rigor, and reader comprehension. Use when reviewing or editing research manuscripts, journal articles, or perspectives, when user mentions manuscript, paper draft, article, research writing, journal submission, reviewer feedback, or needs to improve scientific writing.
npx skillsauth add lyndonkl/claude scientific-manuscript-reviewInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Related skills: Grant proposals → grant-proposal-assistant | Recommendation letters → academic-letter-architect | Emails → scientific-email-polishing
Seven foundational beliefs guiding manuscript review:
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.
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:
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:
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:
Active vs. Passive Voice:
Hedging Language:
Jargon Management:
Terminology Consistency:
Key requirements:
Preserve author voice: Edit for clarity, not voice. Avoid inventing claims or changing 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:
Key resources:
Introduction checklist:
Results checklist:
Discussion checklist:
Typical review time:
Inputs required:
Outputs produced:
testing
Cluster a conference's event records into a small set of coarse themes with finer sub-clusters, an explicit outlier bucket, and soft (multi-membership) affinities — using the hybrid embed-then-label pipeline (embed abstracts, reduce, density-cluster, then LLM-label the clusters) when embedding libraries are available, and an LLM-reasoned hierarchical fallback when they are not. Embeddings do the grouping; the LLM only names the groups. Conference-agnostic. Use when turning structured event records into a navigable theme map for preference elicitation and scheduling, when you need 6-8 reasonable themes rather than 20 muddy ones, or when overlapping talks must belong to more than one theme. Trigger keywords - theme clustering, cluster talks, embed then label, soft membership, outlier talks, conference themes, topic map.
development
Build a personal conference schedule as a constraint-optimization problem — hard constraints (no time overlap, room-to-room travel time, capacity/registration, the attendee's own must-attends and blackouts) plus a user-owned weighted objective trading interest against breadth, pacing (maximize contiguous free time), and serendipity. Surfaces unbreakable conflicts (two high-value overlapping talks the model cannot rank) as decisions for the human rather than silently picking, and reports what each choice traded away. Conference-agnostic. Use to turn a preference profile plus a theme map into a day-by-day plan, to resolve overlapping sessions, or to balance a packed vs paced schedule. Trigger keywords - schedule optimization, conference schedule, constraint optimization, overlapping talks, contiguous free time, conflict surfacing, packed vs paced.
development
Parse a heterogeneous conference program (markdown, HTML, PDF-derived text, or JSON) into normalized event records with per-field confidence scores and independent classification axes (topic, depth, format, prerequisites, recorded, capacity). Detects the program's format before extracting, treats every inferred field as uncertain (present vs inferred vs missing), and flags thin or missing abstracts so downstream enrichment can target them. Conference-agnostic. Use when ingesting a conference or event schedule into a structured store, normalizing a talk/session list, or extracting per-session metadata with calibrated confidence. Trigger keywords - program ingestion, parse schedule, session extraction, event records, conference program, talk metadata, per-field confidence.
development
Build a personalized preference profile from a small number of well-chosen, cluster-grounded questions instead of a long survey. Represents the person's interests as an uncertainty region over the theme map, picks the single highest-information-gain choice-based question (contrasting real talks from different clusters), balances exploiting known interests against exploring uncertain ones, deliberately injects outlier probes to fight selection bias, and stops as soon as the schedule would be stable. Also elicits the user-owned objective weights and hard constraints. Interactive — runs where it can actually ask the person. Conference-agnostic. Use to turn a theme map into a preference profile, to decide what to ask a conference attendee, or to elicit scheduling priorities. Trigger keywords - preference elicitation, ask few questions, information gain, choice-based questions, selection bias probe, objective weights, attendee preferences.