Best for
- Use when the user asks about "citation verification best practices", "how to verify references", "preventing fake citations", or needs guidance on citation accuracy.
Galaxy-Dawn/claude-scholar/skills/citation-verification/SKILL.md
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.
Decision brief
A reference guide for citation verification in academic paper writing, providing verification principles and best practices.
Compatibility matrix
| Platform | Status | Evidence | What to check |
|---|---|---|---|
| Codex | Not declared | No explicit evidence | Portability before use |
| Claude Code | Not declared | No explicit evidence | Portability before use |
| Cursor | Not declared | No explicit evidence | Portability before use |
| Gemini CLI | Not declared | No explicit evidence | Portability before use |
Installation
The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.
npx skills add https://github.com/Galaxy-Dawn/claude-scholar --skill "skills/citation-verification"Inspect the Agent Skill "citation-verification" from https://github.com/Galaxy-Dawn/claude-scholar/blob/2f7766fd541a723d4ddc6230b3277f948d61b093/skills/citation-verification/SKILL.md at commit 2f7766fd541a723d4ddc6230b3277f948d61b093. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.
Workflow
This skill provides verification principles based on canonical scholarly metadata and claim-level checking:
Core idea: Verify immediately when adding a citation, rather than checking after writing is complete.
Preferred authority order: 1. DOI / publisher landing page 2. arXiv ID or arXiv landing page 3. CrossRef 4. Semantic Scholar 5. Zotero metadata imported from a verified identifier 6. Google Scholar only for manual discovery or fallback lookup
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)
Key principle: When citing a specific claim, you must confirm the claim actually appears in the paper.
Permission review
No configured static risk pattern was detected
This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.
Evidence record
| Signal | Value | Evidence type | Meaning |
|---|---|---|---|
| Quality score | 85/100 | Computed | Documentation, specificity, maintenance, and trust rules |
| Repository stars | 4,981 | Source | Repository attention, not individual Skill quality |
| Compatibility | 0 platforms | Source | Declared in the catalog source record |
| Usage guide | automated source guide | Editorial | Generated or reviewed according to the visible evidence level |
Pinned source
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.
Citation issues in academic papers seriously impact research integrity:
These issues can lead to:
Special risk with AI-assisted writing: AI-generated citations have approximately 40% error rate; every citation must be verified via WebSearch.
This skill provides verification principles based on canonical scholarly metadata and claim-level checking:
Core idea: Verify immediately when adding a citation, rather than checking after writing is complete.
Preferred authority order:
Verification steps:
Information that must match:
Key principle: When citing a specific claim, you must confirm the claim actually appears in the paper.
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.
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.
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
If the paper cannot be verified through canonical sources:
[CITATION NEEDED] markerIf information doesn't match:
[CITATION NEEDED] to mark explicitly❌ Wrong approach:
✅ Correct approach:
Core Principle: Proactively verify every citation during the writing process using WebSearch and Google Scholar.
Key Steps:
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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