citation-verification
This skill provides reference guidance for citation verification in academic writing. Use when the user asks about "citation verification best practices", "how to verify references", "preventing fake citations", or needs guidance on citation accuracy. This skill supports ml-paper-writing by providing detailed verification principles and common error patterns.
DeepseekModel
官方收录技能
质量 优秀 · 90
v1.0.0
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https://deepseekmodel.com/api/download.php?id=galaxy-dawn-claude-scholar-skills-citation-verification-skill-md&format=skill
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标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
name citation-verification description This skill provides reference guidance for citation verification in academic writing. Use when the user asks about "citation verification best practices", "how to verify references", "preventing fake citations", or needs guidance on citation accuracy. This skill supports ml-paper-writing by providing detailed verification principles and common error patterns. tags ["Research","Academic","Citation","Reference"] version 0.1.0 Citation Verification Reference Guide A reference guide for citation verification in academic paper writing, providing verification principles and best practices. Core Principle : Proactively verify every citation during the writing process using programmatic or canonical scholarly sources first: arXiv, DOI/CrossRef, Semantic Scholar, publisher landing pages, and Zotero metadata. Google Scholar is useful for manual discovery, but it is not the canonical verification authority. Core Problems Citation issues in academic papers seriously impact research integrity: Fake citations - Citing non-existent papers (common issue with AI-generated citations) Incorrect information - Mismatched authors, titles, years, etc. Inconsistent formatting - Mixed citation formats Missing citations - Referenced but uncited work These issues can lead to: Paper rejection or retraction Damage to academic reputation Reviewers questioning research rigor Special risk with AI-assisted writing : AI-generated citations have approximately 40% error rate; every citation must be verified via WebSearch. Verification Principles This skill provides verification principles based on canonical scholarly metadata and claim-level checking: 1. Proactive Verification (Verify During Writing) Core idea : Verify immediately when adding a citation, rather than checking after writing is complete. Search for the paper via WebSearch each time a citation is needed Confirm the paper exists on Google Scholar Add to bibliography only after verification passes 2. Canonical Metadata Verification Preferred authority order: DOI / publisher landing page arXiv ID or arXiv landing page CrossRef Semantic Scholar Zotero metadata imported from a verified identifier Google Scholar only for manual discovery or fallback lookup Verification steps : Find a DOI, arXiv ID, publisher URL, or verified Zotero item. Confirm title, first author, year, venue, and identifier. Fetch BibTeX from CrossRef, arXiv, publisher metadata, Zotero, or another programmatic source when possible. If only Google Scholar can find the item, mark it as manual verification and do not treat the BibTeX as final until metadata is checked elsewhere. 3. Information Matching Verification Information that must match : Title (minor differences allowed, e.g., capitalization) Authors (at least the first author must match) Year (±1 year difference allowed, considering preprints) Publication venue (conference/journal name) 4. Claim Verification Key principle : When citing a specific claim, you must confirm the claim actually appears in the paper. Use WebSearch to access the paper PDF Search for relevant keywords Confirm the accuracy of the claim Record the section/page where the claim appears Verification Workflow Integration into Writing Process Need a citation during writing ↓ Find DOI / arXiv ID / publisher page / verified Zotero item ↓ Verify metadata with CrossRef / arXiv / Semantic Scholar / publisher / Zotero ↓ Confirm paper details ↓ Get BibTeX ↓ (If citing a specific claim) Verify the claim ↓ Add to bibliography Key point : Verification is part of the writing process, not a separate post-processing step. Usage Guide Using with ml-paper-writing The verification principles of this skill are integrated into the Citation Workflow of the ml-paper-writing skill. Auto-trigger : Citation verification is automatically executed when writing papers with the ml-paper-writing skill. Manual reference : Refer to this skill when you need detailed verification principles. Verification Step Example Scenario : Need to cite the Transformer paper Step 1: WebSearch lookup Query: "Attention is All You Need Vaswani 2017" Result: Found multiple sources for the paper Step 2: Google Scholar verification Query: "site:scholar.google.com Attention is All You Need Vaswani" Result: ✅ Paper exists, 50,000+ citations, NeurIPS 2017 Step 3: Confirm details - Title: "Attention is All You Need" - Authors: Vaswani, Ashish; Shazeer, Noam; Parmar, Niki; ... - Year: 2017 - Venue: NeurIPS (NIPS) Step 4: Get BibTeX - Click "Cite" on Google Scholar - Select BibTeX format - Copy BibTeX entry Step 5: Add to bibliography - Paste into .bib file - Use \cite{vaswani2017attention} in the paper Handling Verification Failures If the paper cannot be verified through canonical sources : Check spelling - Is the title or author name correct? Try different queries - Use different keyword combinations Find alternative sources - Try arXiv, DOI, CrossRef, Semantic Scholar, publisher pages, or Zotero Mark as pending - Use [CITATION NEEDED] marker Notify the user - Clearly state the citation cannot be verified If information doesn't match : Confirm the source - Did you find the correct paper? Check versions - Preprint vs. published version Update information - Use the most accurate version Record discrepancies - Note the reason for differences Best Practices Preventing Fake Citations Never generate citations from memory - AI-generated citations have 40% error rate Use WebSearch to find - Verify every citation through WebSearch Confirm on Google Scholar - Verify paper existence on Google Scholar Verify promptly - Verify when adding citations, don't wait until finished Handling Verification Failures Don't guess - If you can't find the paper, don't fabricate information Mark clearly - Use [CITATION NEEDED] to mark explicitly Notify the user - Clearly state which citations cannot be verified Provide reasons - Explain why verification failed (not found, info mismatch, etc.) Improving Verification Accuracy Complete queries - Include title, author, year Check citation count - Citation count on Google Scholar is a credibility indicator Confirm venue - Verify conference/journal name is correct Verify claims - When citing specific claims, confirm they exist in the paper Common Pitfalls ❌ Wrong approach : Generating BibTeX from memory Skipping Google Scholar verification Assuming a paper exists Not marking unverifiable citations ✅ Correct approach : Search every citation with WebSearch Confirm on Google Scholar Copy BibTeX from Google Scholar Clearly mark unverifiable citations Summary Core Principle : Proactively verify every citation during the writing process using WebSearch and Google Scholar. Key Steps : WebSearch to find the paper Google Scholar to verify existence Confirm details Get BibTeX Verify claims (if needed) Add to bibliography Failure handling : When verification fails, mark as [CITATION NEEDED] and clearly notify the user. Integration : The principles of this skill are integrated into the ml-paper-writing skill for automatic verification.
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下载的 .skill 包内含以下字段。
| 字段 | 说明 |
|---|---|
| format | 格式标识(skill/v1) |
| skill_id | 技能唯一 ID |
| name | 技能名称 |
| version | 版本号 |
| description | 技能描述 |
| category | 所属分类(数组) |
| trigger_words | 触发词列表 |
| tags | 标签列表 |
| source | 来源标识 |
| source_url | 来源链接(本页地址) |
| exported_at | 导出时间(每次下载生成) |
| system_prompt | 系统提示词正文 |
| model_config | 模型参数:provider / model / temperature / max_tokens / top_p |
| examples | 示例 |
| install_guide | 各平台导入说明(Coze / Dify / Claude / 自定义框架) |