القائمة
Academic writing skill for generating summaries, reports, literature reviews, and other research documents
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/QiZishi/PaperAgent --skill write-skillيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
عرض SKILL.md
Citation network and reference management skill for tracking citations, formatting references, and building citation graphs
Document parsing skill for extracting content from PDFs and other academic documents using MinerU and local parsers
Academic literature search skill for finding papers from arXiv, Semantic Scholar, and HuggingFace
استنادا إلى تصنيف SOC المهني
| name | write_skill |
| description | Academic writing skill for generating summaries, reports, literature reviews, and other research documents |
| version | 1.0.0 |
| author | PaperAgent Team |
This skill enables you to generate various types of academic writing, including summaries, literature reviews, research reports, and other scholarly documents. Use this skill when you need to produce well-structured academic content.
You have access to the following writing tools (registered in the Toolkit):
generate_summaryGenerate a concise summary of given text.
Parameters:
text (str, required): Text to summarizemax_length (int, optional): Maximum length in words (default: 500)style (str, optional): "academic", "casual", "technical" (default: "academic")language (str, optional): Output language - "zh" or "en" (default: "zh")Returns: Generated summary text.
Example:
generate_summary(
text=paper_abstract,
max_length=200,
style="academic",
language="zh"
)
generate_report_sectionGenerate a specific section of an academic report.
Parameters:
topic (str, required): Topic of the sectioncontent (str, required): Source content or contextsection_type (str, optional): "introduction", "body", "conclusion", "abstract", "methodology", "results", "discussion" (default: "body")language (str, optional): Output language (default: "zh")Returns: Generated section content.
Example:
generate_report_section(
topic="Transformer Architecture",
content=research_notes,
section_type="introduction",
language="en"
)
generate_literature_reviewGenerate a literature review from a set of papers.
Parameters:
papers (list[dict], required): List of paper metadata with abstractsfocus_topic (str, required): Central topic or research questionstructure (str, optional): "thematic", "chronological", "methodological" (default: "thematic")length (str, optional): "short", "medium", "long" (default: "medium")language (str, optional): Output language (default: "zh")Returns: Structured literature review with proper citations.
Example:
generate_literature_review(
papers=paper_list,
focus_topic="Applications of LLMs in Scientific Research",
structure="thematic",
length="long",
language="zh"
)
generate_outlineGenerate an outline for a research document.
Parameters:
topic (str, required): Document topicdocument_type (str, optional): "paper", "thesis", "report", "proposal" (default: "paper")depth (int, optional): Outline depth level (default: 3)language (str, optional): Output language (default: "zh")Returns: Hierarchical outline structure.
Example:
generate_outline(
topic="Deep Learning for Drug Discovery",
document_type="paper",
depth=3,
language="en"
)
polish_textPolish and improve existing text.
Parameters:
text (str, required): Text to polishfocus (str, optional): "grammar", "clarity", "conciseness", "academic_tone", "all" (default: "all")preserve_meaning (bool, optional): Strictly preserve original meaning (default: True)language (str, optional): Text language (default: "zh")Returns: Polished text with optional change annotations.
Example:
polish_text(
text=draft_paragraph,
focus="academic_tone",
preserve_meaning=True,
language="en"
)
translate_academicTranslate academic text with domain awareness.
Parameters:
text (str, required): Text to translatesource_lang (str, required): Source language ("zh" or "en")target_lang (str, required): Target language ("zh" or "en")domain (str, optional): Academic domain for terminology (e.g., "computer_science", "biology")Returns: Translated text preserving academic style and terminology.
Example:
translate_academic(
text=chinese_abstract,
source_lang="zh",
target_lang="en",
domain="computer_science"
)
generate_abstractGenerate an abstract for a research paper.
Parameters:
title (str, required): Paper titlecontent (str, required): Paper content or detailed notesmax_words (int, optional): Maximum words (default: 250)structure (str, optional): "structured" (with sections) or "unstructured" (default: "unstructured")language (str, optional): Output language (default: "en")Returns: Generated abstract.
Example:
generate_abstract(
title="A Novel Approach to Neural Machine Translation",
content=full_paper_content,
max_words=300,
structure="structured",
language="en"
)
generate_related_workGenerate a related work section.
Parameters:
papers (list[dict], required): Related papers with metadataown_contribution (str, required): Description of your own contributioncomparison_focus (str, optional): What to focus comparison onlanguage (str, optional): Output language (default: "zh")Returns: Related work section with proper positioning.
Example:
generate_related_work(
papers=related_papers,
own_contribution="We propose a novel attention mechanism...",
comparison_focus="efficiency and scalability",
language="en"
)
All generated content is in markdown format with: