academic-humanizer
Use when researchers ask to remove generic, templated, or AI-like patterns from Chinese or English academic prose, make an…
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 -y skills add yila-ai/awesome-research-skills --skill science-research-writing --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/science-research-writingContext preview
The summary Claude sees to decide when to auto-load this skill.
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.
name: science-research-writing description: 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. license: Apache-2.0
Turn the author's research materials into the next useful manuscript deliverable. Guide the writing process without replacing scientific judgment or inventing intellectual content.
Read all supplied materials first. Identify:
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:
1. deliver every safe and useful part first; 2. identify the exact gap or conflict; 3. ask one highest-impact question; 4. wait before drafting only the blocked content.
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:
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.
The research workflow every AI agent should have. A growing open-source Skill stack for the full research lifecycle—from discovering and understanding papers to writing, reviewing, polishing, and presenting research.
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