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Get Started Free →/cs:compliance-readiness <program> — Multi-framework compliance officer 6-question forcing interrogation of any compliance program. Use before starting a new framework, planning the annual audit calendar, or preparing for certification stage 1.
.claude/skills/alirezarezvani-compliance-readiness/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 6% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 52% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 67% | 0% |
| case-17 | ✗→✓ | ▲ Improved | 36% | 0% |
| case-19 | ✗→✓ | ▲ Improved | 78% | 0% |
Command: /cs:compliance-readiness <program>
The multi-framework compliance officer pressure-tests any compliance program. Six questions before any new-framework commitment, audit cycle planning, or certification readiness sign-off.
No framework selector run, no defensible scope.
framework_selector.py with company profileSingle evidence -> N controls = the cornerstone of multi-framework efficiency.
cross_framework_mapper.py with enabled frameworksJoint ownership without accountability is the most common cause of stale evidence.
evidence_pool_generator.py for the artefact inventorySurveillance audits stacking in the same week is a smell.
No mock audit, no readiness signal.
audit_simulator.py with framework + scopeEach framework wants its own management review; an integrated review (per Annex SL) saves 5x exec time.
bash# 1. Framework selection python ../../skills/compliance-os/scripts/framework_selector.py profile.json # 2. Cross-framework overlap python ../../skills/compliance-os/scripts/cross_framework_mapper.py program.json # 3. Evidence pool consolidation python ../../skills/compliance-os/scripts/evidence_pool_generator.py program.json # 4. Mock audit (per framework) python ../../skills/compliance-os/scripts/audit_simulator.py scope.json
markdown# Compliance Readiness: <program> **Date:** YYYY-MM-DD ## The Decision Being Made [framework-set | audit-calendar | certification-readiness | evidence-consolidation] ## Framework Set - Applicable: <list> - Binding (regulations): <count> - Certifiable: <count> - Missing dependencies: <list> ## Cross-Framework Overlap - Total merged controls in scope: N - High-leverage artefacts (≥ 5 mappings): M - Top reuse opportunities: <top 5 artefacts> ## Evidence Pool - Artefacts in catalog: N - High-leverage count: M - Stale evidence rate: X% - Unowned artefacts: K ## Audit Calendar - Frameworks scheduled this year: <list> - Auditor independence respected: Y/N - Conflicts: <list> ## Mock Audit Results (per framework) - <framework>: total findings N, critical X%, observation Y%, healthy distribution: Y/N ## Verdict 🟢 READY | 🟡 STAGE-2-CANDIDATE | 🔴 NOT-READY ## Top 3 Actions [3 concrete next steps with owners + dates]
/cs:aims-audit — for ISO 42001-specific forcing questions/cs:ai-act-readiness — for EU AI Act-specific forcing questions/cs:ciso-review — for cybersecurity strategy/cs:caio-review — for executive AI strategy/cs:gc-review — for novel-case legal review/cs:decide — to log the verdict/cs:freeze 30 — on certification commitments (multi-year financial impact)cs-compliance-officercompliance-osra-qm-team/skills/iso42001-specialist/, ra-qm-team/skills/eu-ai-act-specialist/, ra-qm-team/skills/information-security-manager-iso27001/, ra-qm-team/skills/soc2-compliance/, ra-qm-team/skills/gdpr-dsgvo-expert/Version: 1.0.0
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-02 | fail→pass | 30,240 | 26,583 | -12% | 1 | 1 | 0% | 5,576 | 5,912 | +6% | 0 | 0 | — |
case-01 | fail→fail | 26,104 | 27,338 | +5% | 1 | 1 | 0% | 6,247 | 6,558 | +5% | 0 | 0 | — |
case-09 | pass→pass | 11,946 | 12,132 | +2% | 1 | 1 | 0% | 2,078 | 3,596 | +73% | 0 | 0 | — |
case-03 | fail→fail | 33,748 | 27,963 | -17% | 1 | 1 | 0% | 6,227 | 6,003 | -4% | 0 | 0 | — |
case-04 | pass→pass | 18,572 | 23,202 | +25% | 1 | 1 | 0% | 3,456 | 5,606 | +62% | 0 | 0 | — |
case-05 | pass→pass | 23,416 | 32,061 | +37% | 1 | 1 | 0% | 4,054 | 6,139 | +51% | 0 | 0 | — |
case-06 | pass→pass | 28,890 | 17,643 | -39% | 1 | 1 | 0% | 3,148 | 4,368 | +39% | 0 | 0 | — |
case-07 | pass→pass | 24,270 | 28,862 | +19% | 1 | 1 | 0% | 4,276 | 5,720 | +34% | 0 | 0 | — |
case-08 | pass→pass | 10,188 | 13,136 | +29% | 1 | 1 | 0% | 1,805 | 3,804 | +111% | 0 | 0 | — |
case-10 | fail→pass | 14,481 | 14,726 | +2% | 1 | 1 | 0% | 2,583 | 3,927 | +52% | 0 | 0 | — |
case-11 | fail→pass | 19,877 | 21,546 | +8% | 1 | 1 | 0% | 3,036 | 5,071 | +67% | 0 | 0 | — |
case-12 | pass→pass | 13,192 | 11,197 | -15% | 1 | 1 | 0% | 2,223 | 3,337 | +50% | 0 | 0 | — |
case-13 | fail→fail | 20,273 | 19,845 | -2% | 1 | 1 | 0% | 3,201 | 5,262 | +64% | 0 | 0 | — |
case-14 | pass→pass | 18,298 | 37,956 | +107% | 1 | 1 | 0% | 3,217 | 5,797 | +80% | 0 | 0 | — |
case-15 | pass→pass | 12,324 | 18,345 | +49% | 1 | 1 | 0% | 2,131 | 4,118 | +93% | 0 | 0 | — |
case-16 | pass→pass | 15,959 | 14,369 | -10% | 1 | 1 | 0% | 2,766 | 3,799 | +37% | 0 | 0 | — |
case-17 | fail→pass | 14,327 | 10,948 | -24% | 1 | 1 | 0% | 2,380 | 3,241 | +36% | 0 | 0 | — |
case-18 | pass→pass | 12,513 | 10,175 | -19% | 1 | 1 | 0% | 2,105 | 2,799 | +33% | 0 | 0 | — |
case-19 | fail→pass | 10,360 | 9,291 | -10% | 1 | 1 | 0% | 1,752 | 3,122 | +78% | 0 | 0 | — |
case-20 | pass→pass | 15,584 | 18,884 | +21% | 1 | 1 | 0% | 2,927 | 4,396 | +50% | 0 | 0 | — |
case-21 | pass→fail | 14,286 | 11,194 | -22% | 1 | 1 | 0% | 2,654 | 3,398 | +28% | 0 | 0 | — |
case-22 | pass→pass | 15,795 | 15,817 | +0% | 1 | 1 | 0% | 2,311 | 4,221 | +83% | 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. 22 cases were attempted. The headline lift of +18 percentage points is the difference between those two pass rates over the 22 comparable cases. 1 case got worse with the skill loaded, and it is 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.