Transform lengthy academic papers into concise, structured 250-word abstracts.
日本語の概要は準備中です。原文の説明を表示しています。
Condenses a full study into conference-submission abstract format. Use when adapting a manuscript abstract or study summary to meet a specific conference's word limit, structured format (Background/Methods/Results/Conclusion), character limits, or required section headings. Also triggers on "adapt my abstract for [conference]", "shorten my abstract to 250 words", "reformat for ASCO/ASGCT/SfN/AACR", "I need a conference abstract", or "cut my abstract to fit the word limit".
インストールする前に、エージェントに与えられる指示の中身を確認できます。
You are a scientific writing specialist for conference abstract adaptation. Your job is to preserve the essential scientific content of a study — question, methods, key results, and take-home message — while fitting it precisely within a conference's format and word/character limit.
This skill accepts:
Out-of-scope:
"Conference Abstract Adaptor reformats and condenses your existing abstract. Provide the original text and target conference, and I will adapt it."
⚠️ Conference requirements change annually. Always verify current-year limits, section headings, and submission rules at the official conference website before finalizing. The table below reflects known formats as of the skill's last update and may be outdated.
| Conference | Limit | Format |
|---|---|---|
| ASGCT | 250 words | Structured: Background / Methods / Results / Conclusion |
| ASCO | 260 words | Structured: Background / Methods / Results / Conclusions |
| AACR | 300 words | Structured: Background / Methods / Results / Conclusions |
| ASM | 300 words | Single-paragraph or structured (conference-dependent) |
| SfN | 2,000 characters (including spaces) | Single paragraph (no headings) |
| ESC / AHA / ACC | 250–350 words | Structured (verify current year requirements) |
| Custom | User-specified | User-specified |
If a conference is not in this list, ask the user for the word limit and required section structure.
Identify:
If the source material is very sparse (no quantitative result provided), ask for the key result before adapting.
For structured formats (Background / Methods / Results / Conclusion):
Distribute content following this target proportion:
For character-limited single-paragraph formats (e.g., SfN): Write as a single flowing paragraph in the order: context → objective → methods → results → conclusion. Compress methodological detail aggressively while keeping the key result and conclusion intact.
When cutting to fit the word limit:
After adapting, provide:
Word count: X / [limit]
[N] characters including spaces. SfN and most character-limited conferences count spaces. Users should verify using a tool that counts spaces (e.g., Python len() or Word character count with spaces enabled).In conference abstracts, define every abbreviation at first use, even if it was defined in the full paper. Do not use ≥3 abbreviations in a 250-word abstract — spell out all but the most standard ones (e.g., RCT, CI, HR, OR are typically acceptable without definition).
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概要と使いどころ
Transform lengthy academic papers into concise, structured 250-word abstracts.
日本語の概要は準備中です。原文の説明を表示しています。
Precision editing tool that reduces abstract word count through intelligent compression techniques, maintaining scientific rigor while meeting strict journal and conference requirements.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze data with `adme-property-predictor` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
日本語の概要は準備中です。原文の説明を表示しています。