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Get Started Free →GCP security configuration scanning and hardening using Security Command Center, Forseti, and ScoutSuite
.claude/skills/a5c-ai-gcp-security-scanner/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 152% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 30% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 47% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 71% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 110% | 0% |
Automated Google Cloud Platform security configuration scanning and hardening to identify misconfigurations, compliance violations, and security risks across GCP projects and organizations.
| Category | Services | |----------|----------| | Identity | IAM, Cloud Identity, Workforce Identity | | Compute | Compute Engine, GKE, Cloud Run, Functions | | Storage | Cloud Storage, Persistent Disks | | Database | Cloud SQL, Spanner, BigQuery, Firestore | | Network | VPC, Firewall, Cloud Armor, Cloud CDN | | Security | Security Command Center, Cloud KMS, BeyondCorp | | Monitoring | Cloud Logging, Cloud Monitoring, Cloud Audit Logs |
json{ "type": "object", "properties": { "scanType": { "type": "string", "enum": ["full", "cis", "pci", "hipaa", "iso27001", "custom"], "description": "Type of security scan" }, "projects": { "type": "array", "items": { "type": "string" }, "description": "GCP project IDs to scan" }, "organization": { "type": "string", "description": "GCP organization ID for org-wide scanning" }, "services": { "type": "array", "items": { "type": "string" }, "description": "Specific services to scan" }, "severityThreshold": { "type": "string", "enum": ["critical", "high", "medium", "low"] }, "complianceFrameworks": { "type": "array", "items": { "type": "string", "enum": ["CIS", "PCI-DSS", "HIPAA", "ISO27001", "SOC2", "NIST"] } }, "includeSCC": { "type": "boolean", "description": "Include Security Command Center findings" } }, "required": ["scanType"] }
json{ "type": "object", "properties": { "scanId": { "type": "string" }, "scanTimestamp": { "type": "string", "format": "date-time" }, "projectsScanned": { "type": "array" }, "organizationId": { "type": "string" }, "summary": { "type": "object", "properties": { "totalChecks": { "type": "integer" }, "passed": { "type": "integer" }, "failed": { "type": "integer" }, "warnings": { "type": "integer" } } }, "findingsBySeverity": { "type": "object", "properties": { "critical": { "type": "integer" }, "high": { "type": "integer" }, "medium": { "type": "integer" }, "low": { "type": "integer" } } }, "findings": { "type": "array", "items": { "type": "object", "properties": { "checkId": { "type": "string" }, "severity": { "type": "string" }, "service": { "type": "string" }, "project": { "type": "string" }, "resourceName": { "type": "string" }, "description": { "type": "string" }, "remediation": { "type": "string" }, "complianceMapping": { "type": "array" } } } }, "sccFindings": { "type": "array" }, "organizationPolicyStatus": { "type": "object" }, "recommendations": { "type": "array", "items": { "type": "string" } } } }
javascriptskill: { name: 'gcp-security-scanner', context: { scanType: 'cis', projects: ['my-project-id'], complianceFrameworks: ['CIS', 'SOC2'], includeSCC: true, severityThreshold: 'medium' } }
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 36,217 | 16,793 | -54% | 1 | 1 | 0% | 7,826 | 2,626 | -66% | 0 | 0 | — |
case-02 | fail→fail | 21,863 | 11,370 | -48% | 1 | 1 | 0% | 1,128 | 2,642 | +134% | 0 | 0 | — |
case-03 | fail→fail | 23,123 | 19,586 | -15% | 1 | 1 | 0% | 3,910 | 3,498 | -11% | 0 | 0 | — |
case-04 | fail→pass | 9,417 | 2,909 | -69% | 1 | 1 | 0% | 814 | 2,048 | +152% | 0 | 0 | — |
case-05 | fail→pass | 8,775 | 8,777 | +0% | 1 | 1 | 0% | 1,724 | 2,247 | +30% | 0 | 0 | — |
case-06 | fail→pass | 12,763 | 11,411 | -11% | 1 | 1 | 0% | 1,404 | 2,057 | +47% | 0 | 0 | — |
case-07 | fail→pass | 12,358 | 8,037 | -35% | 1 | 1 | 0% | 1,192 | 2,043 | +71% | 0 | 0 | — |
case-08 | pass→pass | 10,262 | 16,461 | +60% | 1 | 1 | 0% | 2,335 | 4,034 | +73% | 0 | 0 | — |
case-09 | fail→pass | 13,721 | 9,339 | -32% | 1 | 1 | 0% | 1,675 | 3,524 | +110% | 0 | 0 | — |
case-10 | fail→pass | 12,538 | 17,971 | +43% | 1 | 1 | 0% | 2,625 | 4,323 | +65% | 0 | 0 | — |
case-11 | fail→pass | 15,635 | 14,491 | -7% | 1 | 1 | 0% | 2,259 | 3,510 | +55% | 0 | 0 | — |
case-12 | pass→pass | 28,197 | 23,013 | -18% | 1 | 1 | 0% | 4,819 | 5,201 | +8% | 0 | 0 | — |
case-13 | pass→pass | 14,866 | 11,871 | -20% | 1 | 1 | 0% | 2,039 | 3,949 | +94% | 0 | 0 | — |
case-14 | pass→pass | 18,672 | 14,170 | -24% | 1 | 1 | 0% | 3,062 | 4,724 | +54% | 0 | 0 | — |
case-15 | fail→fail | 13,866 | 9,318 | -33% | 1 | 1 | 0% | 1,751 | 3,495 | +100% | 0 | 0 | — |
case-16 | pass→fail | 9,761 | 13,392 | +37% | 1 | 1 | 0% | 2,147 | 3,269 | +52% | 0 | 0 | — |
case-17 | pass→pass | 11,667 | 16,204 | +39% | 1 | 1 | 0% | 2,290 | 3,817 | +67% | 0 | 0 | — |
case-18 | fail→pass | 10,090 | 14,881 | +47% | 1 | 1 | 0% | 2,142 | 3,460 | +62% | 0 | 0 | — |
case-19 | fail→pass | 8,429 | 11,680 | +39% | 1 | 1 | 0% | 1,670 | 2,860 | +71% | 0 | 0 | — |
case-20 | fail→pass | 9,944 | 17,231 | +73% | 1 | 1 | 0% | 2,111 | 4,173 | +98% | 0 | 0 | — |
case-21 | fail→fail | 9,089 | 3,688 | -59% | 1 | 1 | 0% | 1,791 | 2,148 | +20% | 0 | 0 | — |
case-22 | fail→pass | 27,779 | 9,513 | -66% | 1 | 1 | 0% | 1,215 | 2,226 | +83% | 0 | 0 | — |
case-23 | fail→pass | 11,289 | 2,746 | -76% | 1 | 1 | 0% | 1,223 | 2,057 | +68% | 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 22 counted toward the lift figure. The other 1 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 +48 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.