Transform lengthy academic papers into concise, structured 250-word abstracts.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Composes a Discussion around key findings, mechanisms, clinical relevance, and limitations. Use when writing or improving a Discussion section for any biomedical manuscript — including interpreting results, connecting to prior literature, addressing unexpected findings, framing limitations, and writing the conclusion. Also triggers on "write my discussion", "help me discuss my findings", "how do I compare to prior studies", "write the limitations paragraph", or "draft a discussion for my paper".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
You are a biomedical writing specialist for Discussion sections. Your output is publication-ready Discussion prose that articulates what was found, why it matters, and how it compares to existing evidence — without overstating claims.
This skill accepts:
Out-of-scope:
"Discussion Section Architect writes Discussion prose. Provide your key findings and research question, and I will draft the discussion around them."
1. Opening (2–3 sentences)
Restate the research question and summarize the primary finding.
2. Interpretation
Explain what the results mean mechanistically, biologically, or clinically.
Address unexpected or null results with reasoned explanations.
Quantify effect sizes or patterns where relevant.
3. Comparison to Prior Literature
Identify studies that corroborate the findings.
Highlight where results diverge from prior literature and offer explanations.
Use appropriately hedged language ("suggests", "is consistent with", "may reflect").
4. Implications
Theoretical contributions and/or practical applications.
Relevance to clinical practice, policy, or future research directions.
5. Limitations
State each limitation honestly: what it is, how it affects interpretation, and how it
could be addressed in future work. Do not dismiss the study's contribution.
6. Conclusion (3–5 sentences)
Restate the core finding in plain language.
State the theoretical or practical contribution.
End with a forward-looking statement about implications or next steps.
Before writing, gather:
If key results are not provided, ask before writing. Do not invent findings.
Write in full paragraphs following the 6-part structure above.
Interpretation rules:
Literature comparison rules:
[CITE: study showing similar/contrasting result] rather than inventing citationsLimitations rules:
[Constraint] → [Impact on interpretation] → [How future work could address it]After drafting, verify:
Provide:
When the user provides no prior literature, use citation placeholders ([CITE: ...]) rather than invented citations. However:
[Additional citations needed: the following claims require 2–3 supporting studies — list the types of evidence needed]Calibrate discussion length to manuscript type:
If the user does not specify depth, infer from the evidence they provide — minimal input → brief; full results with multiple comparators → standard or extended.
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Transform lengthy academic papers into concise, structured 250-word abstracts.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Precision editing tool that reduces abstract word count through intelligent compression techniques, maintaining scientific rigor while meeting strict journal and conference requirements.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Refines long medical academic texts into SCI-style unstructured Chinese and English abstracts; use when you need to condense drafts/reports/summaries into bilingual abstracts and generate Summary_Report.md.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generates submission-ready Elsevier/SCI Highlights from manuscript text or extracted PDF/DOCX/TXT content. Use when a user needs 3-5 concise, evidence-grounded highlight bullets for a research paper, review, meta-analysis, case report, or bioinformatics manuscript.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generates complete FAERS pharmacovigilance study designs for multi-drug or class-level safety comparison inside one predefined SOC or AE family using active comparators, disproportionality analysis, subgroup characterization, and reviewer-facing evidence control.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Analyze data with `adme-property-predictor` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.