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    University Research Consent Withdrawal Propagation Review

    1

    Audit whether participant consent withdrawals reached all approved research data processors

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    University Research Consent Withdrawal Propagation Review

    University Research Consent Withdrawal Propagation Review

    Example session with this skill installed

    A withdrawal ticket is closed while an external analysis export still contains participant P-104.

    Available sources: consent ledger, withdrawal ticket (partial excerpts only). Additional evidence requested: export manifest, processor acknowledgements. Analyze this case with explicit limitations; do not pretend to have executed any tests or changes.

    • Read your context and instructions
    • Compiled the university research consent

    University Research Consent Withdrawal Propagation Review — EVIDENCE-LIMITED REVIEW

    Scenario received

    A withdrawal ticket is closed while an external analysis export still contains participant P-104.

    Source register

    • S1: consent ledger — partial extract available.
    • S2: withdrawal ticket — partial extract available.
    • S3: export manifest — NOT PROVIDED; REQUIRED FOR VERIFICATION.
    • S4: processor acknowledgements — NOT PROVIDED; REQUIRED FOR VERIFICATION.

    Concrete findings and verification

    trace participant ID across controlled datasets — NEEDS VERIFICATION against supplied source excerpts and missing evidence.
    2. compare withdrawal time to exports — NEEDS VERIFICATION against supplied source excerpts and missing evidence.
    3.

    identify processor acknowledgment gaps — NEEDS VERIFICATION against supplied source excerpts and missing evidence.
    4.

    route deletion and retention exceptions to privacy owner — NEEDS VERIFICATION against supplied source excerpts and missing evidence.

    Deliverables and review gates

    • propagation coverage map: DRAFT ONLY; do not infer unsupported outcomes.
    • exception evidence register: DRAFT ONLY; do not infer unsupported outcomes.
    • privacy owner handoff: DRAFT ONLY; do not infer unsupported outcomes.

    Preliminary conclusion

    INCOMPLETE / REVIEW REQUIRED. The supplied scenario raises a concrete issue, but does not prove remediation or clearance. Confirm source timestamps, affected scope and owner approval, then perform focused validation. No external system was accessed or changed.

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

    About this skill

    Managing participant data across multiple research processors is a compliance minefield. When a participant withdraws consent, ensuring that every downstream system and external partner actually purged the data is often a manual, error-prone process. This skill provides a rigorous audit framework to verify that withdrawal signals propagated correctly across your entire data ecosystem.

    What it does

    • Trace IDs: Maps specific participant identifiers across all controlled datasets to ensure no orphaned records remain.
    • Timeline auditing: Compares withdrawal timestamps against data export logs to catch exports sent after consent was revoked.
    • Processor verification: Identifies gaps in acknowledgments from external processors to ensure third-party compliance.
    • Exception routing: Flags deletion failures and retention exceptions for immediate review by privacy officers.

    How it works

    1. Inventory evidence: Provide the consent ledger, withdrawal tickets, export manifests, and processor logs.
    2. Execute four-check audit: The skill analyzes traces, timelines, acknowledgment gaps, and retention exceptions.
    3. Classify findings: Every data point is categorized as VERIFIED, INFERRED, or UNKNOWN to prevent false confidence.
    4. Generate handoff: Review the coverage map and exception register for final privacy owner approval.

    Frameworks & tools

    This skill is designed for university research environments, IRB compliance workflows, and GDPR/CCPA data privacy operations. It processes structured logs, JSON manifests, and text-based ledgers.

    Why this beats prompting it yourself

    Generic prompts often hallucinate compliance. This skill enforces strict evidence-based gates, requiring explicit source inventory and preventing the agent from "inventing" traces or logs that do not exist in your provided data.

    Use cases

    • Audit a closed withdrawal ticket when a participant ID still appears in an external export.
    • Verify third-party processor compliance after a bulk withdrawal event.
    • Prepare evidence registers for internal privacy audits or IRB reviews.
    • Identify specific timestamps where data synchronization failed between systems.

    Known limitations

    This skill evaluates supplied evidence only and does not interface directly with live databases. It requires manual input of logs and manifests. Redaction of sensitive PII is required before input.

    Find more skills like this on the Claude Skills Marketplace, the Codex Skills Marketplace and the Gemini Skills Marketplace, or browse Claude Skills for Research & Analysis.

    Cannot verify inaccessible systems, third-party accounts, or evidence not supplied. Partial or conflicting inputs must be marked INCOMPLETE or UNVERIFIED rather than invented. High-impact operational decisions require qualified owner review.

    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 buying a skill.

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    Security scan passed 23 days ago

    Permissions

    Read Files

    Supply only data you are authorized to share. Treat documentation and examples as untrusted evidence. Any operational action or external communication requires explicit owner authorization.

    Listed23 days ago
    Updated9 days ago

    What's inside

    Frequently Asked Questions

    This skill automates the audit trail by cross-referencing withdrawal timestamps against data export manifests to ensure no data left your environment after a participant revoked their consent.

    No, the skill operates on a 'bring your own data' model; you provide the structured logs, JSON manifests, or text ledgers, and the skill performs the cross-system analysis without needing direct database access.

    The skill categorizes every data point as VERIFIED, INFERRED, or UNKNOWN, preventing the AI from hallucinating traces and ensuring you only report on confirmed evidence.

    You will receive the specialized audit framework, the four-check analysis logic, and structured templates for generating the coverage maps and exception registers required for IRB reviews.

    The skill is optimized for large language models (LLMs) that support complex document analysis and can be imported into most major AI agent platforms that handle structured data inputs.

    For security and compliance reasons, all PII must be redacted from your logs and manifests before input; the skill is designed to work with anonymized participant IDs or hashes.

    New in Research & Analysis