dsar-request-preflight-2d308d — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited dsar-request-preflight-2d308d (Agent Skill) and scored it 96/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 1 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 1 flagged
A bulleted imperative like {match} tells the agent to never reveal, disclose, or mention something to the user. Used adversarially it can instruct the agent to hide its tool calls or lie about what it did — stripping the transparency a user relies on to trust the agent.
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Use this skill when a privacy, legal ops, support ops, security, or startup operations team needs to turn messy privacy-rights intake into a defensible fulfillment plan. It is designed for data subject access requests, deletion/erasure requests, correction requests, portability requests, and similar consumer privacy requests.
The goal is to prevent late responses, unsafe disclosures, unsupported deletions, missed systems, weak identity checks, and vague owner handoffs. This is privacy-operations workflow support, not legal advice.
Use for:
Do not use for:
Ask only for missing inputs that materially change the result:
request_id, received_date, requester, jurisdiction, request_type, identity_status, authorized_agent, agent_proof, scope, match_keys, sensitive_context, notes.system, owner, data_categories, match_keys, export_supported, deletion_supported, retention_lock, legal_hold, processor.If the user has only emails or tickets, first convert them to a small intake table. Do not infer identity or authorization status from tone.
Before analysis:
Read references/dsar-preflight-rules.md before classifying a request as ready.
Use explicit paths:
python3 dsar-request-preflight/scripts/dsar_request_preflight.py \
--requests /absolute/path/requests.csv \
--systems /absolute/path/systems.csv \
--policy /absolute/path/policy.json \
--today 2026-05-30The script accepts CSV or JSON. JSON may be a list or an object containing requests, dsars, systems, inventory, or rows.
Use one primary action per finding:
verify_identity: identity status is missing, pending, or not approved.verify_agent_authority: authorized agent proof is missing or incomplete.accelerate_fulfillment: statutory or internal deadline is due soon.escalate_privacy_owner: response deadline appears overdue.map_system_scope: requested scope does not match inventory.route_owner_tasks: multiple owners must export, delete, or approve exceptions.manual_export_review: access/export request touches systems without export support.deletion_exception_review: deletion request touches legal hold, retention lock, unsupported deletion, or exception context.sensitive_data_review: request mentions child, health, financial, biometric, location, or other sensitive context.Never say that a company is legally compliant or noncompliant. Say which operational checks are complete, blocked, or need owner approval.
Return:
## DSAR Request Decision
[Hold fulfillment pending repair / Review before response / Ready for controlled fulfillment]
## Request Summary
[Requests reviewed, systems reviewed, blocker count, critical/high count]
## Action Summary
[Action counts and top routing needs]
## Request Findings
| Risk | Action | Row | Request | Type | Flag | Evidence | Next step |
|---|---|---:|---|---|---|---|---|
## Owner Routing
[Request-to-owner map]
## Guardrails
[Privacy, authority, legal boundary, no live deletion/export caveat]Use templates/response-readiness-report.md when the user wants a reusable packet for a privacy owner.
Positive example: "Use dsar-request-preflight on this privacy portal export and data inventory before we respond to deletion requests." The skill should check deadlines, identity, authorized-agent proof, deletion support, retention or legal-hold exceptions, system coverage, and owner routing.
Positive startup example: "We have five GDPR/CPRA requests in a spreadsheet and a rough SaaS system list." The skill should turn the rough system list into a scoped owner plan and call out no-inventory gaps rather than claiming completion.
Negative example: "Tell me whether we can deny this DSAR." Do not provide legal advice; classify operational blockers and route exception approval.
Boundary example: "Delete this user's data in all systems." Do not perform live deletion; prepare a fulfillment checklist and require explicit owner authorization.
Smoke-test the bundled fixture:
python3 dsar-request-preflight/scripts/dsar_request_preflight.py \
--requests dsar-request-preflight/scripts/fixtures/requests.csv \
--systems dsar-request-preflight/scripts/fixtures/systems.csv \
--policy dsar-request-preflight/scripts/fixtures/policy.json \
--today 2026-05-30Expected result: exit code 2 with DSAR Request Decision, Hold fulfillment pending repair, response_deadline_overdue, identity_verification_missing, authorized_agent_proof_missing, deletion_blocked_or_exception_needed, access_export_not_supported, sensitive_or_minor_data_context, and missing_received_date.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.