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Get Started Free →[COMMUNITY] Generate a Privacy Impact Assessment (PIA) for Australian Government entities under Privacy Act 1988 s33D, assessing compliance with all 13 Australian Privacy Principles (APPs).
.claude/skills/thomasmoreai-arckit-au-pia/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 32% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 41% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 102% | 0% |
| case-12 | ✗→✓ | ▲ Improved | -1% | 0% |
| case-20 | ✗→✓ | ▲ Improved | 9% | 0% |
> ⚠️ Community-contributed command — not part of the officially-maintained ArcKit baseline. Output should be reviewed by a qualified Privacy Officer, DPO, or legal counsel before reliance. Citations to the Privacy Act 1988 and OAIC guidance may lag current amendments — verify against the source. The Privacy Act 1988 reform (Tranche 2) is under development — monitor for changes.
You are an enterprise architect generating a Privacy Impact Assessment (PIA) for an Australian Government entity or regulated-sector organisation under the Privacy Act 1988 (Cth).
text$ARGUMENTS
Australian Government agencies covered by the Privacy Act 1988 must conduct PIAs for projects that involve new or changed handling of personal information. Section 33D requires agencies to conduct a PIA for all high-privacy-risk activities. The OAIC (Office of the Australian Information Commissioner) publishes the Guide to undertaking privacy impact assessments, which defines the methodology.
Authoritative anchors:
Key Privacy Act 1988 Reform Context:
projects/000-global/ARC-000-PRIN-*.md (architecture principles, if present).arckit/templates/_partials/RENDERING.md.arckit/templates-custom/au-pia-template.md (user override).arckit/templates-custom/au-pia-template.md.arckit/templates/au-pia-template.mdscripts/bash/create-project.sh --json <project-name> if the project does not yet exist; otherwise locate it.scripts/bash/generate-document-id.sh <PROJECT_ID> AUPIA --filename for the artefact filename.<!-- DOC-CONTROL-HEADER --> marker per RENDERING.md. Use the Australian classification scheme (UNOFFICIAL / OFFICIAL / OFFICIAL:Sensitive / PROTECTED / SECRET) — replace the standard UK line in the header.For each APP, document:
$arckit-au-ai-assurance if applicable..arckit/references/citation-instructions.md. The Privacy Act 1988 and OAIC PIA Guide MUST appear in the Document Register.projects/<project-id>/<filename>.After completing this command, consider running:
$arckit-au-dss -- PIA findings feed DSS Criterion 7 (Protect users' privacy).$arckit-au-e8-posture -- APP 11 (security of personal information) informs E8 target maturity level.$arckit-risk -- Privacy risks surface in the project risk register.| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 21,878 | 39,460 | +80% | 1 | 1 | 0% | 3,948 | 7,970 | +102% | 0 | 0 | — |
case-02 | fail→fail | 12,990 | 5,421 | -58% | 1 | 1 | 0% | 2,468 | 2,038 | -17% | 0 | 0 | — |
case-03 | fail→fail | 21,139 | 4,569 | -78% | 1 | 1 | 0% | 4,271 | 1,975 | -54% | 0 | 0 | — |
case-04 | fail→pass | 9,298 | 1,781 | -81% | 1 | 1 | 0% | 1,544 | 2,037 | +32% | 0 | 0 | — |
case-05 | fail→pass | 7,208 | 2,152 | -70% | 1 | 1 | 0% | 1,496 | 2,112 | +41% | 0 | 0 | — |
case-06 | pass→pass | 7,025 | 2,549 | -64% | 1 | 1 | 0% | 1,204 | 2,048 | +70% | 0 | 0 | — |
case-07 | pass→pass | 7,858 | 12,501 | +59% | 1 | 1 | 0% | 1,472 | 3,877 | +163% | 0 | 0 | — |
case-08 | fail→pass | 11,094 | 12,434 | +12% | 1 | 1 | 0% | 1,888 | 3,819 | +102% | 0 | 0 | — |
case-09 | pass→pass | 14,869 | 14,345 | -4% | 1 | 1 | 0% | 2,502 | 4,009 | +60% | 0 | 0 | — |
case-10 | pass→pass | 6,393 | 7,197 | +13% | 1 | 1 | 0% | 1,151 | 2,849 | +148% | 0 | 0 | — |
case-11 | pass→pass | 4,571 | 3,263 | -29% | 1 | 1 | 0% | 746 | 2,193 | +194% | 0 | 0 | — |
case-12 | fail→pass | 11,816 | 3,028 | -74% | 1 | 1 | 0% | 2,114 | 2,103 | -1% | 0 | 0 | — |
case-13 | fail→fail | 14,616 | 10,566 | -28% | 1 | 1 | 0% | 2,193 | 3,463 | +58% | 0 | 0 | — |
case-14 | fail→fail | 14,105 | 6,319 | -55% | 1 | 1 | 0% | 2,402 | 2,883 | +20% | 0 | 0 | — |
case-15 | pass→pass | 14,692 | 14,230 | -3% | 1 | 1 | 0% | 2,483 | 4,160 | +68% | 0 | 0 | — |
case-16 | pass→pass | 23,937 | 20,359 | -15% | 1 | 1 | 0% | 2,583 | 4,532 | +75% | 0 | 0 | — |
case-17 | fail→fail | 4,706 | 2,089 | -56% | 1 | 1 | 0% | 847 | 2,080 | +146% | 0 | 0 | — |
case-18 | pass→pass | 6,798 | 6,609 | -3% | 1 | 1 | 0% | 1,076 | 3,028 | +181% | 0 | 0 | — |
case-19 | pass→pass | 10,377 | 19,252 | +86% | 1 | 1 | 0% | 1,877 | 4,407 | +135% | 0 | 0 | — |
case-20 | fail→pass | 12,421 | 3,002 | -76% | 1 | 1 | 0% | 2,024 | 2,197 | +9% | 0 | 0 | — |
case-21 | fail→pass | 23,491 | 6,785 | -71% | 1 | 1 | 0% | 4,237 | 2,790 | -34% | 0 | 0 | — |
case-22 | pass→fail | 17,340 | 37,618 | +117% | 1 | 1 | 0% | 3,213 | 2,205 | -31% | 0 | 0 | — |
case-23 | pass→fail | 40,568 | 13,020 | -68% | 1 | 1 | 0% | 4,843 | 2,562 | -47% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted, and 19 counted toward the lift figure. The other 4 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +17 percentage points is the difference between those two pass rates over the 19 comparable cases. 3 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.