Best fit
Privacy teams close deletion requests while intake, identity-proof pointers, system actions, exceptions, subprocessors, and backup tombstones remain scattered.
// Security & Compliance
DSAR & Deletion Completion Evidence Reconciler
Reconcile deletion-request evidence across systems, subprocessors, retention exceptions, and backup replay before human review.
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Package requirements: SKILL.md-compatible agents that can run a local Python helper. Python 3.10 or newer on Windows, macOS, or Linux using only the standard library. One documented UTF-8 JSON evidence packet; no API key or provider account.
You get: findings.csv with stable codes and evidence sources review-manifest.csv with every reviewed record
Licence and purchase details
Review the package licence at the destination.
Review the offer and access method at your chosen marketplace. Agensi provides connected-agent access, not a download. Buying a skill does not include an AI-agent subscription or setup service.
Agensi access: This product’s bundled-script workflow through Agensi has not been verified here. Tests of other products do not establish this package’s compatibility. Local setup examples apply to separately obtained downloadable packages.
What problem does DSAR & Deletion Completion Evidence Reconciler solve?
Privacy teams close deletion requests while intake, identity-proof pointers, system actions, exceptions, subprocessors, and backup tombstones remain scattered.
Use it to
- Check every in-scope system before closing a deletion request
- Review retention exceptions and subprocessor confirmations
- Preserve backup replay tombstone evidence for privacy review
What it returns
- findings.csv with stable codes and evidence sources
- review-manifest.csv with every reviewed record
- result.json with READY, REVIEW, or BLOCK gate and SHA-256 receipt
- report.md with a human-review handoff
A representative input and result
input `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.
result A deterministic READY, REVIEW, or BLOCK result with stable finding codes, a complete review manifest, a human-readable report, and a SHA-256 receipt.
Access and approval boundaries
- Terminal permission to run the bundled local Python reconciler.
- Read access to one user-selected normalized UTF-8 JSON packet.
- Write access only to one user-selected local output directory.
- No browser, network, credential, account, environment-variable, messaging, payment, deletion, access-change, filing, or publication permission.
Known limitations
- The owner and qualified counsel must decide legal scope, identity sufficiency, deadlines, and retention duties.
- The package does not ingest raw identity data, connect to systems, or delete anything.
- READY is evidence completeness only, not proof of legal compliance or deletion.
// choose with context
Is DSAR & Deletion Completion Evidence Reconciler the right skill?
It returns
findings.csv with stable codes and evidence sources. review-manifest.csv with every reviewed record.
Do not use it as
The owner and qualified counsel must decide legal scope, identity sufficiency, deadlines, and retention duties.
Compatibility, access, version, and licence
- Reads or accesses
- Declared local scope: one owner-selected local JSON input, one owner-selected local output directory.
- Compatibility
- SKILL.md-compatible agents that can run a local Python helper. Python 3.10 or newer on Windows, macOS, or Linux using only the standard library. One documented UTF-8 JSON evidence packet; no API key or provider account.
- Version
- 1.0.0
- Licence
- Review the package licence and seller terms at the linked destination.
Questions
Does it connect to a live account or provider?
No. It reads one normalized local JSON packet and makes no network request.
Does it make the final decision or external change?
No. It produces evidence findings; the responsible owner makes every decision and action outside the package.
Is the result deterministic?
Yes. Stable finding codes, sorted records, and a SHA-256 receipt make repeated review inspectable.
What happens with malformed input?
Unreadable JSON, missing arrays, duplicate core identifiers, invalid timestamps, and invalid core numbers fail closed.
Does READY prove the underlying evidence is true?
No. READY means no automated finding appeared in the supplied normalized packet; source truth still requires human review.
DSAR & Deletion Completion Evidence Reconciler keeps proof and approval boundaries visible.
The listing includes the tested package, realistic samples, declared permissions, and known limitations.
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