review-writing

name review-writing description null Review Writing — 学术综述逐节写作方法论 Use this skill when the user asks to write a literature review, review article, or 综述 based on an outline. Trigger keywords: "写综述", "write review", "综述写作", "按大纲写", "逐节写", "review section", "写第N节". This skill orchestrates the ENTIRE review writing process from outline to finished manuscript. This skill calls academic-literature-search skill for all search and citation operations. Read that skill first if not already loaded. Tool routing : PubMed operations → MCP tools ( pubmed_search_articles , pubmed_fetch_contents , pubmed_article_connections ). arXiv search, GB/T 7714 formatting, citation processing → Python code. See academic-literature-search for the complete routing table and code templates. Architecture: Why Section-by-Section A full review (12,000–15,000 words, 100–130 references) CANNOT be written in one pass due to context window limits. The correct approach: Outline → [Phase 0: Validate & Revise outline] → [Phase 1..N: Per-section pipeline] → [Final: Assemble full review] Each section is an independent unit of work: Search → Filter → Group → Write → Cite → Save to file ↓ section_N.md (persisted immediately) Final assembly reads all section files → cross-section dedup → unified numbering Phase 0: Outline Validation & Revision (大纲验证与修订) DO NOT skip this phase. No outline is perfect before reading the literature. Step 0.1: Read the outline Read the user's outline file. Parse each section's title, sub-topics, and any pre-identified references. Step 0.2: Scout search (侦察检索) For each section, run ONE quick search using the section title/topic as query: MCP pubmed_search_articles (maxResults=10, fetchBriefSummaries=5) For CS/AI-heavy sections: also Python search_arxiv() (max_results=5) For sections with known seed papers: MCP pubmed_article_connections (similar, maxRelatedResults=5) Step 0.3: Evaluate and suggest revisions Based on scout results, produce a validation report: ## 大纲验证报告 ### §1 [section title] - 检索命中: PubMed X篇, arXiv Y篇 - 代表性论文: - "Paper Title A" (Journal, Year) — [关系:直接相关] - "Paper Title B" (Journal, Year) — [关系:方法论参考] - "Paper Title C" (Journal, Year) — [关系:最新进展] - 评估: ✅ 文献充足 / ⚠️ 偏少建议扩展 / 🔴 极少建议合并或调整 - 建议: [specific suggestion if any] ### §2 ... ### 整体建议 - 建议新增: [topic] — 检索发现大量文献但大纲未覆盖 - 建议合并: §X 和 §Y 文献高度重叠 - 建议拆分: §Z 文献过于丰富，建议拆为两节 Step 0.4: User confirms revised outline Wait for user to confirm or further adjust. Only proceed to Phase 1 after outline is finalized. Pre-Writing: Thesis Reference Ingestion (正文引用复用) If the user's thesis body already has references (like the 论文正文——第一二部分合并.md ), BEFORE starting Phase 1: Extract all references from the thesis body (PMIDs, DOIs, author-year citations) Fetch their full metadata via MCP pubmed_fetch_contents Store as a seed reference pool — when the same paper appears in review search results, reuse this metadata exactly (ensures consistency between thesis body and review) When writing review sections, if a thesis-body reference is relevant, cite it directly from the seed pool without re-searching Phase 1–N: Per-Section Writing (逐节写作) For each section, execute the full pipeline below. One section per conversation turn. Step 1: Deep Search (深度检索) Generate 3-5 targeted search queries based on the section's sub-topics. Then: For biomedical-heavy sections (§1, §2, §5, §7): MCP pubmed_search_articles (maxResults=15, dateRange minDate="2020", fetchBriefSummaries=10) MCP pubmed_search_articles (queryTerm="X AND biorxiv[journal]", maxResults=5) — 预印本 For CS/AI-heavy sections (§3, §4, §6): MCP pubmed_search_articles (maxResults=10) Python search_arxiv(query, max_results=10) — ML/AI 会议论文和预印本 For all sections: 3. Deduplicate across all queries: Python deduplicate() 4. For foundational papers in the outline: MCP pubmed_fetch_contents (pmids=[...]) 5. For expanding from seed papers: MCP pubmed_article_connections (similar / citedin / references) 6. Check seed reference pool for any already-known relevant papers Target candidates per section: Biomedical sections: 15–25 papers CS/AI sections: 20–30 papers (wider net because PubMed coverage is sparser) Step 2: Filter & Score (筛选评估) Present search results to the LLM with this instruction: From the following N search results, select the most relevant papers for writing a review section about [section topic]. SELECTION TARGETS: - Biomedical sections: select 10-15 papers - CS/AI sections: select 15-20 papers SELECTION CRITERIA (in priority order): 1. RELEVANCE to the specific section topic 2. IMPACT: prefer high-impact journals and highly-cited works 3. RECENCY: prefer 2022-2026, but include seminal older papers 4. DIVERSITY: cover different sub-aspects, not just the most popular finding 5. BALANCE: include both supporting evidence and contrasting viewpoints For each selected paper, assign ONE role: - FOUNDATIONAL: establishes the field/concept - EVIDENCE: provides key experimental/computational evidence - METHOD: introduces an important method/tool - COMPARISON: enables comparison between approaches - GAP: identifies limitations or open problems - MILESTONE: landmark paper (e.g., AlphaFold, GPT-4) Output format: [search_index] [ROLE] — one-sentence reason for inclusion Step 3: Evidence Grouping (证据分组) Organize selected papers into narrative groups that will drive paragraph structure: Group A: "Historical development / Milestones" Group B: "Current mainstream approaches / Consensus" Group C: "Emerging advances / Recent breakthroughs" Group D: "Methodological comparisons" Group E: "Limitations, controversies, and open problems" Not every section needs all groups. Choose the groups that fit the section's content. Step 4: Write Section (撰写本节) Write a review section about [section topic] using the provided literature. WRITING RULES: 1. ACADEMIC CHINESE PROSE (学术中文). Scientific terms keep English abbreviations on first mention: e.g., 检索增强生成（Retrieval-Augmented Generation, RAG）. Subsequent uses can use abbreviation directly. 2. NARRATIVE, NOT LIST. Write flowing paragraphs with logical transitions. ❌ "A研究了X[1]。B研究了Y[2]。C研究了Z[3]。" ✅ "多项研究从不同角度探讨了这一问题。A等[1]首先通过...揭示了...； 在此基础上，B等[2]进一步...；然而，C等[3]的研究指出..." 3. CITE BY INDEX. Reference papers using [N] where N is the paper's index in the provided source list. Every factual claim MUST have at least one citation. 4. CRITICAL ANALYSIS. Don't just summarize — compare, contrast, evaluate. Point out methodological differences, conflicting findings, remaining gaps. ❌ "取得了重要进展"、"具有广阔前景" ✅ "将检索精度从 70.1% 提升至 80.7%"、"覆盖了 338 个数据库" 5. SECTION STRUCTURE: a. 开门点题（1-2句）：本节综述什么主题，为什么重要 b. 发展脉络（2-3段）：按时间或逻辑组织 c. 现状分析（1-2段）：主流方法/共识/争议 d. 批判性评价（1段）：现有工作的局限和不足 e. 收束引出（1-2句）：指向下一节或研究空白 6. SECTION TRANSITION: The FIRST sentence of this section must logically connect to the LAST sentence of the previous section. The LAST sentence must set up the next section's topic. [Agent: verify this after writing.] 7. LENGTH: 1,500-2,500 Chinese characters per section. 8. NO FABRICATION. Only cite papers from the provided source list. If a fact lacks source support, write "据报道" without citation rather than fabricating one. NEVER invent PMIDs, DOIs, or author names. 9. JOURNAL NAMES: Use FULL journal names (Nature Medicine, not Nat Med). Keep this consistent across all sections. SOURCE LIST: [paste filtered papers with index, title, authors, year, abstract] Step 5: Post-Write Checks (写后检查) After the LLM writes the section, perform these checks: 5a. Citation integration (code): process_citations() from academic-literature-search skill Expand multi-citations, remove phantoms, record actually-cited papers 5b. Section transition check (LLM): Read the last 2 sentences of the PREVIOUS section file Read the first 2 sentences of the current section Verify logical connection. If disconnected, suggest revision. 5c. Citation density check (code): # Count paragraphs and citations paragraphs = [p for p in section_text.split( "\n\n" ) if p.strip()] for i, p in enumerate (paragraphs): cite_count = len (re.findall( r"\[\d+\]" , p)) if cite_count == 0 and len (p) > 100 : print ( f"WARNING: Paragraph {i+ 1 } has no citations ( { len (p)} chars)" ) 5d. Format reference list (code): format_gbt7714() for each cited paper Verify journal names are full names, not abbreviations Step 6: Save to File (保存) output_dir/ section_1_[short_name].md section_2_[short_name].md ... section_N_[short_name].md _metadata.json Each section file: # [Section Number] [Section Title] [Section text with [N] citations] --- ## 本节参考文献（临时编号） [1] Author, et al. Title[J]. Journal, Year, Vol(Issue): Pages. DOI: xxx. [2] ... --- <!-- metadata section _index: 1 cited_ papers: [ {"local _index": 1, "pmid": "12345678", "doi": "10.1234/xxx", "title": "...", "source": "pubmed"}, {"local_ index": 2, "pmid": "", "doi": "", "arxiv _id": "2210.03629", "title": "...", "source": "arxiv"}, ... ] search_ queries: ["query1", "query2", ...] candidate _count: 25 cited_ count: 14 --> _metadata.json tracks cross-section state: { "outline_file" : "/path/to/综述大纲.md" , "output_dir" : "/path/to/综述输出/" , "sections_completed" : [ 1 , 2 , 3 ] , "sections_total" : 8 , "all_cited_papers" : [ { "pmid" : "12345678" , "doi" : "..." , "title" : "..." , "first_cited_in_section" : 1 } , ... ] , "total_unique_references" : 45 , "seed_reference_pool" : [ ... ] , "last_updated" : "2026-02-27T20:30:00" } Final Phase: Assembly (全文组装) Step F1: Read all section files Step F2: Cross-section deduplication Same paper cited in §2 and §5 → ONE reference number. Match by DOI > PMID > normalized title. Step F3: Unified sequential numbering MUST be done by code , scanning sections in order: import re, json def assemble_review ( section_files, output_path ): """Assemble all sections into final review with unified GB/T 7714 numbering.""" global_refs = [] paper_to_global = {} global_num = 1 full_text_parts = [] for sf in section_files: with open (sf) as f: content = f.read() # Split text from metadata text_part = content.split( "---\n## 本节参考文献" )[ 0 ] # Load cited papers from metadata comment meta_match = re.search( r'<!-- metadata\n(.*?)\n-->' , content, re.DOTALL) local_papers = [] if meta_match: meta_text = meta_match.group( 1 ) cp_match = re.search( r'cited_papers: (\[.*?\])' , meta_text, re.DOTALL) if cp_match: local_papers = json.loads(cp_match.group( 1 )) if not local_papers: full_text_parts.append(text_part) continue paper_by_local = {p[ "local_index" ]: p for p in local_papers} def remap ( m ): nonlocal global_num local_idx = int (m.group( 1 )) paper = paper_by_local.get(local_idx) if not paper: return "" key = (paper.get( "doi" ) or paper.get( "pmid" ) or paper.get( "title" , "" ).lower()) if key not in paper_to_global: paper_to_global[key] = global_num global_refs.append(paper) global_num += 1 return f"[ {paper_to_global[key]} ]" remapped = re.sub( r"\[(\d+)\]" , remap, text_part) full_text_parts.append(remapped) # Build final GB/T 7714 reference list from academic_literature_search import format_gbt7714 # conceptual import ref_lines = [format_gbt7714(p, i) for i, p in enumerate (global_refs, 1 )] full_review = "\n\n" .join(full_text_parts) full_review += "\n\n---\n\n# 参考文献\n\n" + "\n" .join(ref_lines) with open (output_path, "w" ) as f: f.write(full_review) return len (global_refs) Step F4: Quality Check (质量自检) Run automated checks and produce a report: 质量自检报告 ───────────────────────────────── 总节数: N 总唯一参考文献: M 每节平均引用: M/N = X.X (目标: 12-18) ───────────────────────────────── 引用覆盖率: Y% 段落有至少1个引用 最长无引用段: Z 字符 (目标: <500) ───────────────────────────────── 来源分布: PubMed 期刊论文 [J]: X篇 (XX%) 预印本 [Z/OL]: Y篇 (YY%) 会议论文 [C]: Z篇 (ZZ%) ───────────────────────────────── 年份分布: 2024-2026: X篇 (XX%) 2021-2023: Y篇 (YY%) 2020及以前: Z篇 (ZZ%) ───────────────────────────────── 跨节引用复用: X篇被多节引用 无引用的节: [列表, 应为空] ───────────────────────────────── 节间衔接: §1→§2: ✅ / ⚠️ [具体问题] §2→§3: ✅ / ⚠️