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    Citation Guard AI Reference Verification & Retraction Check

    1

    Audit bibliographies against Crossref and PubMed to verify DOIs, flag retractions, and identify metadata mismatches.

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    Citation Guard AI Reference Verification & Retraction Check

    Citation Guard AI Reference Verification & Retraction Check

    Example session with this skill installed

    Audit these citations for retractions and metadata accuracy:

    1. 10.1038/s41586-021-03616-x
    2. Smith et al., 2023, 'Neural networks in clinical diagnosis', Journal of Tech.
    • Read your context and instructions
    • Compiled the citation guard ai

    I have audited the 2 citations provided against Crossref and PubMed. One record is a verified match, while the other requires manual review due to missing database records.

    Citation Audit Report

    Citation IDOriginal citationPrimary statusFlagsFields checkedEvidence sourceEvidence URLRecommended actionConfidence
    110.1038/s41586-021-03616-xVERIFIED_MATCHNoneDOI, Title, Authors, YearCrossrefhttps://doi.org/10.1038/s41586-021-03616-xNoneHigh
    2Smith et al., 2023, 'Neural networks...'NOT_FOUNDMissing DOI/PMIDTitle, Author, YearCrossref, PubMedN/ASearch for DOI or PMID manuallyLow

    Executive Summary

    • Verified Match: 1
    • Not Found: 1
    • Retractions/Corrections: 0

    Audit Notes

    • Citation 1 (DOI: 10.1038/s41586-021-03616-x) matches "Structure and function of a chimeric antimony-binding protein" published in Nature (2021). Metadata is consistent.
    • Citation 2 yielded no exact matches in Crossref or PubMed for the title "Neural networks in clinical diagnosis" by Smith in 2023. This may be due to the paper being too recent for indexing or a generic title match failure.

    Next steps

    • Provide a DOI for the Smith et al. (2023) paper to re-run the audit.
    • Confirm the journal name for Citation 2, as "Journal of Tech" may be an abbreviation not recognized by the search API.
    • Download the verified metadata for Citation 1 to update your bibliography manager.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Academic integrity relies on verifiable sources, yet manual citation auditing is slow and prone to oversight. Citation Guard automates the verification of bibliographies by cross-referencing user-supplied data against live Crossref and PubMed records to identify discrepancies, retractions, and missing metadata.

    What it does

    • Source verification checks DOIs and PMIDs against authoritative scholarly databases to confirm record existence.
    • Metadata auditing compares titles, authors, and publication venues to identify field-level mismatches or formatting errors.
    • Retraction flagging detects official retraction or correction signals directly from Crossref and PubMed metadata.
    • Evidence logging generates a transparent trail including retrieval dates, query strings, and source URLs for every check.
    • Priority reporting categorizes citations by status, from verified matches to high-priority mismatches requiring human review.

    How it works

    1. Input the citation list or bibliography and specify a preferred citation style if formatting is required.
    2. Normalize the data by extracting DOIs, PMIDs, and titles while preserving the original source text.
    3. Query official Crossref and PubMed API endpoints using identifiers or title-based search logic.
    4. Audit the retrieved metadata against the input fields to assign a primary status like VERIFIED_MATCH or RETRACTION_SIGNAL.
    5. Review the generated audit table to address flags, correct metadata, or investigate retracted works.

    Frameworks & tools

    This skill utilizes the Crossref REST API and NCBI E-utilities (PubMed) for real-time bibliographic data retrieval.

    Why this beats prompting it yourself

    Generic LLMs often hallucinate DOIs or claim retracted papers are valid because their training data is static. This skill forces live lookups against official registries, ensuring that verification is based on current evidence rather than probabilistic text generation.

    Use cases

    • Audit a manuscript bibliography before journal submission to ensure all DOIs are functional.
    • Screen a literature review for retracted or corrected studies to maintain research quality.
    • Clean up inconsistent metadata in a shared reference library or Zotero export.
    • Verify the existence of obscure citations that appear potentially fabricated or incorrect.

    Known limitations

    Does not prove a paper is fabricated if not found. Requires live API access to Crossref and PubMed. Limited to scholarly works indexed in these two databases.

    Requires live API access. Does not verify papers not indexed in Crossref or PubMed. Cannot confirm if a paper is 'unretracted', only if a retraction signal exists.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Accounts
    CrossrefDoi
    Software
    Python

    Permissions

    Terminal / Shell
    Read Files
    Network Access

    Allowed Hosts

    api.crossref.org
    doi.org
    Listed24 days ago

    What's inside

    Frequently Asked Questions

    Unlike standard AI models that rely on static training data, Citation Guard performs real-time API lookups against Crossref and PubMed to catch recent retractions and ensure DOIs are currently active and accurate.

    The skill checks any academic work indexed in the Crossref or PubMed databases; while coverage is vast, it may not include very old, niche, or non-indexed legal and humanities sources.

    You will receive the Citation Guard skill file compatible with major AI agent platforms, along with documentation on how to integrate the necessary API connections for Crossref and PubMed.

    The skill outputs a structured audit report that categorizes every citation by status, highlighting specific discrepancies in titles or authors and explicitly flagging any retracted works.

    Updates are released periodically to optimize API query logic and ensure compatibility with the latest scholarly database schemas, and these are typically made available to all existing owners.

    If a citation isn't found, the skill flags it for manual review rather than assuming it is false, as the work might simply be missing from the specific databases indexed.

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