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。
Generates PI(E)COS structure (Population, Intervention, Comparator, Outcomes, Study Design) from Meta-analysis or study titles. Use when the user wants to extract these elements from a title.
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。
scripts/validate_skill.py.Python: 3.10+. Repository baseline for current packaged skills.Third-party packages: not explicitly version-pinned in this skill package. Add pinned versions if this skill needs stricter environment control.See ## Usage above for related details.
cd "20260316/scientific-skills/Data Analytics/meta-picos-generator"
python -m py_compile scripts/validate_skill.py
python scripts/validate_skill.py --help
Example run plan:
CONFIG block or documented parameters if the script uses fixed settings.python scripts/validate_skill.py with the validated inputs.scripts/validate_skill.py.Run this minimal command first to verify the supported execution path:
python scripts/validate_skill.py --help
This skill extracts PI(E)COS (Population, Intervention, Comparator, Outcomes, Study Design) elements from a clinical study or meta-analysis title.
When the user provides a study title, follow this two-step process to extract the information.
First, remove any characters related to the type of research from the input title (e.g., "systematic review", "meta-analyses"), which are typically found at the end of the title.
Example:
Based on the cleaned title from Step 1, act as a clinical research expert and extract the following information:
Constraint:
Return the result in the following JSON format (no XML tags):
{
"P": "...",
"I(E)": "...",
"C": "...",
"O": "...",
"S": "...",
"Other": "..."
}
meta_picos_generator_result.md unless the skill documentation defines a better convention.Run this minimal verification path before full execution when possible:
No local script validation step is required for this skill.
Expected output format:
Result file: meta_picos_generator_result.md
Validation summary: PASS/FAIL with brief notes
Assumptions: explicit list if any
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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。