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🗑️ DSAR & Deletion Completion Evidence Reconciler
Reconcile deletion-request evidence across systems, subprocessors, retention exceptions, and backup replay before human review.
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You say
as_of is timezone-aware; requests[] uses opaque request_id, identity-proof reference, deadline, and status; systems[] uses system_id and in-scope status; actions[] records request, system, action, exception, subprocessor, and backup evidence.
Your agent does
A deterministic READY, REVIEW, or BLOCK result with stable finding codes, a complete review manifest, a human-readable report, and a SHA-256 receipt.
What you get
About this skill
DSAR & Deletion Completion Evidence Reconciler turns owner-supplied normalized evidence into a deterministic local review gate. Privacy teams close deletion requests while intake, identity-proof pointers, system actions, exceptions, subprocessors, and backup tombstones remain scattered. The bundled Python helper writes stable findings, a complete review manifest, JSON and Markdown reports, and a SHA-256 receipt. Fails closed on missing system actions, unsupported retention exceptions, missing subprocessor confirmations, deadline gaps, and backup-replay evidence without executing deletion. It uses only the Python standard library, makes no network request, changes no account or system, and never treats READY as source proof, certification, a legal or security verdict, a reimbursement promise, or authorization to act.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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More reviews for JustHandled Labs5.0(3)
Feedback buyers left on other skills by the same creator.
I already had a forecasting tool but this gave us more detail that I had Claude take the best parts and merge it into my current agent to make it more robust.
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Verified clean 14 days ago
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