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Get Started Free →Comprehensive security assessment across application, cloud, container, IAM, network, OS, supply chain, and zero trust using NIST CSF 2.0.
.claude/skills/williamzujkowski-security-assessment-orchestrator/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 145% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 467% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 215% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 292% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 134% | 0% |
Primary trigger conditions:
When NOT to use this skill:
Value proposition: Provides unified security posture across 10 security domains, correlates findings to identify attack paths, and prioritizes remediation based on CVSS 4.0 + business context. Organizations using comprehensive security orchestration reduce MTTD (Mean Time To Detect) by 62% and MTTR (Mean Time To Respond) by 74% compared to siloed assessments (IBM Security 2025).
Required inputs validation:
pythonNOW_ET = "2025-10-26T16:45:00-04:00" assert assessment_scope in ["application", "infrastructure", "cloud", "full-stack"], "Valid scopes required" assert target_environment in ["dev", "staging", "production", "all"], "Valid environment required" assert compliance_requirements in ["nist-csf", "cis", "owasp", "fedramp", "none"] assert depth_level in ["quick-scan", "standard", "comprehensive"], "Valid depth required" # Business context validation if business_context.get("internet_facing") and target_environment == "production": warn("Internet-facing production asset: elevating scan depth to comprehensive") # Scope validation required_skills = map_scope_to_skills(assessment_scope) if len(required_skills) > 5 and depth_level == "comprehensive": estimate_duration = len(required_skills) * 15 # minutes per skill at T2 warn(f"Comprehensive scan will invoke {len(required_skills)} skills, ~{estimate_duration} minutes")
Authority checks:
Source citations (accessed 2025-10-26T16:45:00-04:00):
Goal: Identify critical security risks across all domains in <15 minutes.
Steps:
application → security-appsec-validatorinfrastructure → security-network-validator, security-os-validatorcloud → security-cloud-analyzer, security-iam-reviewerfull-stack → all 10 security- skillscheck_level: critical-only for all delegated skills(count_critical × 10) + (count_high × 5)Token budget checkpoint: ~1.8k tokens for skill orchestration, aggregation, risk scoring, output formatting.
Goal: Generate detailed security posture report with NIST CSF 2.0 alignment and prioritized remediation roadmap.
Extends T1 with:
check_level: standard for delegated skillsSkill invocation matrix:
| Domain | Skill | NIST CSF Functions | Compliance | |--------|-------|-------------------|------------| | Application | security-appsec-validator | Protect (PR.AC, PR.DS) | OWASP Top 10, API Top 10 | | Cloud | security-cloud-analyzer | Identify (ID.AM), Protect (PR.AC) | CIS Benchmarks, Well-Architected | | Container | security-container-validator | Protect (PR.IP) | CIS Docker/K8s | | Cryptography | security-crypto-validator | Protect (PR.DS) | FIPS 140-2 | | IAM | security-iam-reviewer | Protect (PR.AC) | CIS IAM | | Network | security-network-validator | Protect (PR.PT), Detect (DE.CM) | CIS Network | | OS | security-os-validator | Protect (PR.IP) | CIS OS Benchmarks | | Supply Chain | security-supplychain-validator | Identify (ID.SC), Govern (GV.SC) | NIST SSDF, SLSA | | Zero Trust | security-zerotrust-architect | Govern (GV.PO), Protect (PR.AC) | NIST SP 800-207 | | Zero Trust Assess | security-zerotrust-assessor | Identify (ID.RA) | CISA ZT Maturity |
attack_path_id for tracking Govern (GV): 85% coverage (17/20 subcategories) Identify (ID): 90% coverage (27/30 subcategories) Protect (PR): 78% coverage (39/50 subcategories) Detect (DE): 65% coverage (26/40 subcategories) Respond (RS): 45% coverage (18/40 subcategories) ← low coverage, gap Recover (RC): 30% coverage (9/30 subcategories) ← low coverage, gap
Final risk score formula: Risk Score = (CVSS × Business Multiplier) + Exploit Modifier + Data Modifier + Exposure Modifier
Final Risk Score / Effort HoursAuthority sources (accessed 2025-10-26T16:45:00-04:00):
Output: JSON report with sections: executive_summary, findings_by_domain, nist_csf_coverage, security_maturity_assessment, attack_paths, prioritized_roadmap.
Token budget checkpoint: ~5.5k tokens (includes T1 + comprehensive skill orchestration + detailed analysis).
Goal: Deep governance alignment, continuous monitoring strategy, and board-level security metrics for organizations with >$100M revenue or regulatory requirements.
Extends T2 with:
Authority sources (accessed 2025-10-26T16:45:00-04:00):
Output: Full enterprise security governance package including CRQ analysis, compliance mapping, board metrics, continuous monitoring blueprint, and incident response readiness assessment.
Token budget checkpoint: ~11k tokens (includes T1 + T2 + enterprise-grade governance analysis).
When to abort:
Ambiguity thresholds:
Prioritization logic:
NIST CSF principle application (accessed 2025-10-26T16:45:00-04:00):
Per NIST CSF 2.0 (https://www.nist.gov/cyberframework):
Schema (JSON):
json{ "assessment_metadata": { "timestamp": "2025-10-26T16:45:00-04:00", "scope": "full-stack", "environment": "production", "depth": "comprehensive", "skills_invoked": 10 }, "executive_summary": { "overall_maturity_score": 6.2, "critical_risk_index": 47, "total_findings": 142, "breakdown": { "critical": 3, "high": 18, "medium": 67, "low": 54 }, "top_5_risks": [ { "finding_id": "IAM-001", "title": "Overpermissive admin role attached to 50+ users", "cvss": 8.8, "risk_score": 15.8, "domain": "iam" } ], "estimated_remediation_timeline": "90 days for all Critical+High findings" }, "findings_by_domain": [ { "domain": "application", "skill": "security-appsec-validator", "findings_count": 28, "findings": [ { "id": "APP-001", "title": "SQL injection vulnerability in /api/users endpoint", "severity": "critical", "cvss": 9.8, "risk_score": 19.3, "owasp_category": "A03:2021 - Injection", "remediation": "Use parameterized queries, ORM with escaping", "effort": "medium", "owner": "appsec-team" } ] } ], "nist_csf_coverage": { "govern": {"coverage_pct": 85, "maturity_score": 7.2}, "identify": {"coverage_pct": 90, "maturity_score": 6.8}, "protect": {"coverage_pct": 78, "maturity_score": 6.1}, "detect": {"coverage_pct": 65, "maturity_score": 5.5}, "respond": {"coverage_pct": 45, "maturity_score": 4.2}, "recover": {"coverage_pct": 30, "maturity_score": 3.8} }, "attack_paths": [ { "path_id": "AP-001", "description": "Public S3 bucket → overpermissive IAM → PII data exfiltration", "exploitability": "high", "combined_risk_score": 17.6, "findings": ["CLOUD-012", "IAM-001", "CRYPTO-005"] } ], "prioritized_roadmap": [ { "phase": "Phase 1 (0-30 days)", "findings_count": 21, "estimated_effort": "120 hours", "risk_reduction": 68.5, "items": [ { "finding_id": "APP-001", "priority": 1, "action": "Remediate SQL injection vulnerabilities", "owner": "appsec-team", "effort": "medium", "roi": 12.3 } ] } ] }
Required fields: assessment_metadata, executive_summary (with maturity_score, CRI, total_findings), nist_csf_coverage, prioritized_roadmap.
Optional fields: attack_paths (only if correlations found), findings_by_domain (can be filtered by severity).
yaml# Example: Full-stack security assessment for production SaaS application input: assessment_scope: full-stack target_environment: production compliance_requirements: nist-csf business_context: asset_criticality: mission-critical data_sensitivity: pii-phi internet_facing: true depth_level: comprehensive output: overall_maturity: 6.2 (Walk tier) critical_risk_index: 47 (immediate action) findings: 142 total (3 critical, 18 high, 67 medium, 54 low) top_risks: 1. SQL injection (CVSS 9.8, risk_score 19.3) 2. Overpermissive IAM (CVSS 8.8, risk_score 15.8) 3. Public S3 bucket with PII (CVSS 8.6, risk_score 15.2) attack_paths: - Public S3 → IAM escalation → PII exfiltration (risk 17.6) roadmap: Phase 1 (0-30d): 21 items, 120h effort, 68.5 risk reduction Phase 2 (31-90d): 45 items, 280h effort, 24.3 risk reduction Phase 3 (91-180d): 76 items, 450h effort, 7.2 risk reduction
Token budgets (enforced):
Accuracy requirements:
Safety constraints:
Auditability:
Determinism:
Official NIST CSF 2.0 documentation:
Risk scoring and vulnerability management:
Security frameworks and standards:
Compliance and governance:
Industry research:
Related skills:
This meta-skill orchestrates the following specialist skills:
security-appsec-validator: Application security (OWASP Top 10)security-cloud-analyzer: Cloud security posture (AWS, Azure, GCP)security-container-validator: Container and Kubernetes securitysecurity-crypto-validator: Cryptography and encryption validationsecurity-iam-reviewer: Identity and access management reviewsecurity-network-validator: Network security and segmentationsecurity-os-validator: Operating system hardeningsecurity-supplychain-validator: Software supply chain securitysecurity-zerotrust-architect: Zero trust architecture designsecurity-zerotrust-assessor: Zero trust maturity assessmentComplementary skills:
compliance-oscal-validator: OSCAL-formatted compliance validationcompliance-fedramp-validator: FedRAMP-specific compliancecompliance-automation-engine: Automated compliance monitoring| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 29,960 | 30,740 | +3% | 1 | 1 | 0% | 4,374 | 10,709 | +145% | 0 | 0 | — |
case-02 | fail→fail | 22,035 | 16,613 | -25% | 1 | 1 | 0% | 2,636 | 10,287 | +290% | 0 | 0 | — |
case-03 | fail→fail | 33,876 | 26,063 | -23% | 1 | 1 | 0% | 6,226 | 12,077 | +94% | 0 | 0 | — |
case-04 | pass→pass | 16,303 | 16,045 | -2% | 1 | 1 | 0% | 2,138 | 8,673 | +306% | 0 | 0 | — |
case-05 | fail→fail | 6,090 | 6,752 | +11% | 1 | 1 | 0% | 412 | 6,876 | +1569% | 0 | 0 | — |
case-06 | fail→fail | 9,010 | 13,148 | +46% | 1 | 1 | 0% | 825 | 7,711 | +835% | 0 | 0 | — |
case-07 | pass→pass | 8,049 | 4,590 | -43% | 1 | 1 | 0% | 1,476 | 7,074 | +379% | 0 | 0 | — |
case-08 | pass→pass | 9,088 | 3,476 | -62% | 1 | 1 | 0% | 1,627 | 6,882 | +323% | 0 | 0 | — |
case-09 | fail→pass | 6,755 | 2,213 | -67% | 1 | 1 | 0% | 1,171 | 6,634 | +467% | 0 | 0 | — |
case-10 | fail→pass | 11,669 | 4,957 | -58% | 1 | 1 | 0% | 2,308 | 7,261 | +215% | 0 | 0 | — |
case-11 | fail→pass | 9,926 | 4,492 | -55% | 1 | 1 | 0% | 1,843 | 7,230 | +292% | 0 | 0 | — |
case-12 | fail→pass | 13,629 | 5,798 | -57% | 1 | 1 | 0% | 3,245 | 7,592 | +134% | 0 | 0 | — |
case-13 | fail→pass | 6,984 | 1,924 | -72% | 1 | 1 | 0% | 1,393 | 6,517 | +368% | 0 | 0 | — |
case-14 | pass→pass | 4,638 | 4,418 | -5% | 1 | 1 | 0% | 970 | 7,050 | +627% | 0 | 0 | — |
case-15 | pass→pass | 8,809 | 2,145 | -76% | 1 | 1 | 0% | 1,785 | 6,622 | +271% | 0 | 0 | — |
case-16 | fail→pass | 5,850 | 3,024 | -48% | 1 | 1 | 0% | 1,127 | 6,703 | +495% | 0 | 0 | — |
case-17 | fail→pass | 8,924 | 2,563 | -71% | 1 | 1 | 0% | 1,710 | 6,673 | +290% | 0 | 0 | — |
case-18 | fail→pass | 11,908 | 3,167 | -73% | 1 | 1 | 0% | 2,066 | 6,799 | +229% | 0 | 0 | — |
case-19 | fail→pass | 14,976 | 4,268 | -72% | 1 | 1 | 0% | 2,938 | 7,063 | +140% | 0 | 0 | — |
case-20 | pass→pass | 7,220 | 3,192 | -56% | 1 | 1 | 0% | 1,239 | 6,857 | +453% | 0 | 0 | — |
case-21 | pass→pass | 11,646 | 5,156 | -56% | 1 | 1 | 0% | 2,047 | 7,249 | +254% | 0 | 0 | — |
case-22 | pass→pass | 8,213 | 3,483 | -58% | 1 | 1 | 0% | 1,366 | 6,817 | +399% | 0 | 0 | — |
case-23 | fail→pass | 8,304 | 2,252 | -73% | 1 | 1 | 0% | 1,458 | 6,607 | +353% | 0 | 0 | — |
case-24 | pass→pass | 11,915 | 1,585 | -87% | 1 | 1 | 0% | 2,250 | 6,431 | +186% | 0 | 0 | — |
case-25 | fail→pass | 10,790 | 2,064 | -81% | 1 | 1 | 0% | 2,008 | 6,581 | +228% | 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. 25 cases were attempted. The headline lift of +48 percentage points is the difference between those two pass rates over the 25 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.