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Get Started Free →STRIDE threat modeling, DREAD risk scoring, secret detection, and secure architecture design. Use when conducting threat models, reviewing code for vulnerabilities, designing defense-in-depth, or scanning for hardcoded secrets.
.claude/skills/borghei-senior-security/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | 116% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 96% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 462% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 86% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 5% | 0% |
The agent performs STRIDE threat analysis with DREAD risk scoring, designs defense-in-depth security architectures with Zero Trust principles, conducts secure code reviews against OWASP Top 10, and scans codebases for hardcoded secrets across 20+ credential patterns.
secret_scanner.py finds 20+ credential patterns (AWS/GCP/Azure, GitHub/Slack/Stripe, private keys); CI/CD-ready exit codes.Before the threat model or scan, confirm these inputs. If any is unknown or vague, ASK — do not assume:
--component; defines the STRIDE analysis scope)Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
| Tool | Purpose | Command | |------|---------|---------| | threat_modeler.py | STRIDE threat analysis with DREAD risk scoring and mitigation recommendations | python scripts/threat_modeler.py --component "API Gateway" --json | | secret_scanner.py | Detect hardcoded secrets/credentials across 20+ patterns (CI/CD-ready exit codes) | python scripts/secret_scanner.py /path/to/project --severity high |
Load the reference that matches the task — keep this file lean and pull detail on demand:
threat_modeler.py and secret_scanner.py. Read when running the bundled scripts.This skill covers:
This skill does NOT cover:
| Skill | Integration | Data Flow | |-------|-------------|-----------| | senior-devops | CI/CD pipeline security gates | Threat model mitigations feed into pipeline hardening requirements; secret scanner runs as a pre-commit or CI step | | senior-secops | Security monitoring and incident response | Threat model outputs define detection rules; incident severity levels align with SecOps alerting tiers | | senior-backend | Secure API development | Secure code review checklist applied to backend PRs; authentication pattern selection guides API auth implementation | | senior-architect | Security architecture decisions | Defense-in-depth layers and Zero Trust principles inform architecture design reviews; STRIDE results feed architecture risk register | | senior-qa | Security testing integration | Vulnerability assessment findings become QA regression test cases; OWASP Top 10 mapping drives security test coverage | | ra-qm-team | Compliance framework alignment | Security controls mapped to SOC 2, PCI-DSS, and HIPAA requirements; threat model documentation satisfies audit evidence needs |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-03 | fail→pass | 21,160 | 28,215 | +33% | 1 | 1 | 0% | 2,958 | 6,397 | +116% | 0 | 0 | — |
case-01 | fail→pass | 24,584 | 36,006 | +46% | 1 | 1 | 0% | 3,742 | 7,353 | +96% | 0 | 0 | — |
case-02 | fail→pass | 9,546 | 30,805 | +223% | 1 | 1 | 0% | 851 | 4,782 | +462% | 0 | 0 | — |
case-04 | fail→pass | 7,089 | 2,463 | -65% | 1 | 1 | 0% | 993 | 1,851 | +86% | 0 | 0 | — |
case-05 | pass→pass | 12,773 | 22,285 | +74% | 1 | 1 | 0% | 1,987 | 4,798 | +141% | 0 | 0 | — |
case-06 | pass→pass | 13,936 | 15,362 | +10% | 1 | 1 | 0% | 2,213 | 3,930 | +78% | 0 | 0 | — |
case-07 | fail→pass | 11,665 | 2,634 | -77% | 1 | 1 | 0% | 1,753 | 1,849 | +5% | 0 | 0 | — |
case-08 | pass→pass | 6,556 | 11,602 | +77% | 1 | 1 | 0% | 1,108 | 3,266 | +195% | 0 | 0 | — |
case-09 | pass→pass | 12,492 | 17,507 | +40% | 1 | 1 | 0% | 1,869 | 4,409 | +136% | 0 | 0 | — |
case-10 | pass→pass | 6,451 | 8,251 | +28% | 1 | 1 | 0% | 974 | 2,965 | +204% | 0 | 0 | — |
case-11 | pass→pass | 6,361 | 4,854 | -24% | 1 | 1 | 0% | 1,039 | 2,176 | +109% | 0 | 0 | — |
case-12 | pass→pass | 13,629 | 10,138 | -26% | 1 | 1 | 0% | 1,975 | 2,933 | +49% | 0 | 0 | — |
case-13 | pass→pass | 11,136 | 10,631 | -5% | 1 | 1 | 0% | 1,568 | 3,028 | +93% | 0 | 0 | — |
case-14 | fail→pass | 12,880 | 7,873 | -39% | 1 | 1 | 0% | 1,865 | 2,615 | +40% | 0 | 0 | — |
case-15 | pass→pass | 19,504 | 29,757 | +53% | 1 | 1 | 0% | 2,888 | 6,014 | +108% | 0 | 0 | — |
case-16 | pass→pass | 5,573 | 9,414 | +69% | 1 | 1 | 0% | 731 | 2,843 | +289% | 0 | 0 | — |
case-17 | pass→pass | 7,882 | 7,986 | +1% | 1 | 1 | 0% | 1,164 | 2,620 | +125% | 0 | 0 | — |
case-18 | pass→pass | 15,862 | 16,001 | +1% | 1 | 1 | 0% | 2,343 | 3,870 | +65% | 0 | 0 | — |
case-19 | pass→pass | 10,990 | 9,596 | -13% | 1 | 1 | 0% | 1,563 | 2,901 | +86% | 0 | 0 | — |
case-20 | pass→pass | 11,360 | 14,026 | +23% | 1 | 1 | 0% | 1,724 | 3,454 | +100% | 0 | 0 | — |
case-21 | pass→pass | 16,647 | 19,022 | +14% | 1 | 1 | 0% | 2,578 | 4,822 | +87% | 0 | 0 | — |
case-22 | fail→fail | 22,474 | 31,398 | +40% | 1 | 1 | 0% | 4,370 | 7,129 | +63% | 0 | 0 | — |
case-23 | fail→fail | 25,925 | 30,407 | +17% | 1 | 1 | 0% | 4,158 | 6,614 | +59% | 0 | 0 | — |
case-24 | fail→fail | 19,075 | 18,181 | -5% | 1 | 1 | 0% | 2,546 | 4,052 | +59% | 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. 24 cases were attempted. The headline lift of +25 percentage points is the difference between those two pass rates over the 24 comparable cases.
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.