Use when researchers need to plan, draft, revise, or audit an empirical research paper from their own materials, including Introduction, Methods, Results, Discussion, Conclusion, Abstract, and Title, with evidence-preserving and target-journal-aware guidance.
npx skills add https://github.com/Yila-AI/sci-ssci-skills --skill science-research-writing
Turn the author's research materials into the next useful manuscript deliverable. Guide the writing process without replacing scientific judgment or inventing intellectual content.
references/input-output-contract.md and references/certainty-and-claim-strength.md.references/reverse-engineering-protocol.md when target papers are supplied or the user requests journal adaptation.introduction.md, methods.md, results.md, discussion.md, conclusion.md, abstract.md, or title.md.assets/section-function-map.md for planning, assets/evidence-ledger.csv for provenance-sensitive drafting, and assets/target-journal-model.json for target-paper modeling.Read all supplied materials first. Identify:
lookup, learn, model, plan, draft, revise, or audit;Do not ask the user to choose an internal mode. Do not require field, journal, section, or language preferences when a conservative useful result is possible.
If missing information would force an unsupported scientific choice:
If conflicting sources block the entire requested sentence or section, return a diagnosis rather than a provisional scaffold. Do not infer variable roles, direction, reference groups, statistical meaning, table labels, or missing uncertainty from the conflicting numbers.
Use plan. Convert the question and intended contribution into a provisional section-function map. Label missing evidence instead of supplying it.
Use plan -> draft -> audit. Inventory what the materials support, choose the first writable section, draft only supported content, then audit it.
Use audit -> plan -> revise -> audit. Diagnose structure and evidence boundaries before rewriting.
If the supplied prose is already clear, section-appropriate, and evidence-faithful, return it unchanged. Do not provide an optional cosmetic alternative, normalize punctuation, add units, or propose journal styling unless the user supplied a specific style requirement.
Use audit first. Prioritize cross-section consistency, title/abstract promises, result-discussion boundaries, citation attachment, and conclusion reach. Revise only what the user requests or what the audit identifies.
Use learn -> model before planning or drafting. Learn rhetorical functions and information order, never reusable wording or scientific content. Follow references/reverse-engineering-protocol.md.
Before drafting or revising, privately classify every consequential statement as one of:
user_data;author_judgment;user_citation;structural_transition;author_confirmation.Treat the user's materials as the authority. Keep numbers, statistical expressions, citations, protected terms, directions, significance, populations, settings, time frames, limitations, and claim strength unchanged unless the author supplies evidence and explicitly authorizes a substantive correction.
Never silently add:
Do not turn general methodological knowledge into manuscript content. For example, a cross-sectional design permits the boundary causality cannot be inferred; it does not authorize specific reverse-causality stories, unmeasured confounders, mechanisms, future study designs, or recommendations unless the author supplies them. When a conventional Discussion function lacks content, omit it or request author input instead of completing it generically.
Do not infer a contrast from separate significance tests. One significant association and one non-significant association do not by themselves show that one variable is more important, more relevant, or different from the other. Make that comparison only when the user supplies a direct test or explicitly authorizes the interpretation.
Load the relevant section reference and map each paragraph to a reader question and information function before writing. Prefer a clear evidence path over ornamental academic language.
These are defaults, not a universal template. Adapt the sequence when the author's field or target-paper model supports a different defensible structure.
Before returning text, compare it with the source materials and check:
When local source and draft text are available, run scripts/check_draft_invariants.py as a deterministic first pass. A passing script is necessary but not sufficient; manually review semantics and citation scope.
If a target-journal model is created, run scripts/validate_writing_model.py before using it.
Do not fabricate citations, hide null or adverse results, remove limitations, disguise contradictory evidence, or strengthen a claim beyond the supplied evidence. Briefly explain the mismatch and provide the strongest evidence-faithful alternative.
Follow references/input-output-contract.md. Use this order:
Draft or diagnosisHow it is organizedAuthor confirmationNext stepPut the usable manuscript text or diagnosis first. Keep explanations brief. Write None required when no author confirmation is needed. Show Risk flags only when a real academic risk exists.
The next step must advance evidence or author review. Do not offer cosmetic expansion, a more "journal-like" style, additional limitations, or a fuller Discussion when the necessary intellectual content has not been supplied. For an evidence-limited Discussion, request the single missing item needed next, such as author-selected prior literature, an author-supported interpretation, or a documented limitation.
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Take yila-ai/science-research-writing from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.