Citation verifier

Skill papersflow-ai/papersflow-skills/skills/citation-verifier

Verify, normalize, and enrich a single citation or paper identifier. Use when the user pastes a DOI, URL, arXiv ID, PubMed ID, citation string, or paper title and wants it checked.From its SKILL.md

Install

npx -y skills add papersflow-ai/papersflow-skills --skill citation-verifier

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

5 stars5 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

2.2 KB, 501 tokens by cl100k_base, as published. Nobody here has run it

Citation Verifier

Use this skill when the user wants to quickly verify or normalize a single paper reference rather than run a full literature search.

Workflow

Accept whatever the user provides: DOI, URL, arXiv ID, PubMed ID, citation string, or a partial title.
Call verify_citation with the raw input.
If verification succeeds, call fetch with the resolved identifier to get the full paper record.
Present the normalized result.

Output Style

Return a clean, structured card with:

Title — full paper title
Authors — first three authors, et al. if more
Year — publication year
Venue — journal or conference
DOI — canonical DOI if available
Identifiers — any additional IDs (arXiv, PubMed, Semantic Scholar)
Status — whether the citation was verified successfully or had issues

If verification fails or is ambiguous, say so clearly and suggest what the user can try (e.g., a more complete title, a different identifier).

Tool Guidance

Use `verify_citation`

Always call this first. It handles:

DOI strings (with or without resolver prefix)
arXiv IDs (e.g., 2301.12345, arXiv:2301.12345)
PubMed IDs
full or partial URLs from publisher sites, Semantic Scholar, Google Scholar
free-text citation strings (e.g., "Smith et al. 2020, Neural networks for...")

Use `fetch`

Call after successful verification to hydrate the full paper record with abstract, citation count, and venue details.

Do NOT use

search_literature — this skill is for a known reference, not topic discovery
get_citation_graph — the user wants verification, not graph exploration

Examples

User pastes: 10.1038/s41586-021-03819-2
User pastes: https://arxiv.org/abs/2301.12345
User says: "Check this cite: Vaswani et al., Attention Is All You Need, NeurIPS 2017"
User says: "Is this DOI valid? 10.1234/fake.doi.000"
User pastes a Semantic Scholar or Google Scholar URL

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most web research skills give in 501 tokens

Counted across 292 of the 300 authors here whose files we hold, read 2026-09-06

Use web_search_exa for current information and broad discoveryin 22 of 292, across 8 files
Cite every claim with a sourcein 21 of 292, across 18 files
Configure the Exa MCP server with an API keyin 18 of 292, across 5 files
Use get_code_context_exa for code examples and API docsin 16 of 292, across 6 files
Verify exact tool names before depending on themin 13 of 292, across 4 files
Narrow results with site:, quoted phrase, and intitle: operatorsin 13 of 292, across 4 files
Adjust tokensNum lower for snippets, higher for full contextin 13 of 292, across 4 files
Break the topic into 3-5 research sub-questionsin 13 of 292
Confirm current Exa docs and exposed tool surface before usein 11 of 292, across 2 files
Get user confirmation after Phase 1in 10 of 292, across 9 files
Prefer primary sources when availablein 10 of 292
Verify extracted metadata against original sourcesin 9 of 292, across 5 files

Said here and by no other author read

Call verify_citation first with the raw input
Accept any input format the user provides
Call fetch after successful verification with the resolved identifier
Present the normalized result
Return a clean structured card
State clearly when verification fails or is ambiguous

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.