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Get Started Free →Identifies security vulnerabilities, generates structured audit reports with severity ratings, and provides actionable remediation guidance. Use when conducting security audits, reviewing code for vulnerabilities, or analyzing infrastructure security. Invoke for SAST scans, penetration testing, DevSecOps practices, cloud security reviews, dependency audits, secrets scanning, or compliance checks. Produces vulnerability reports, prioritized recommendations, and compliance checklists.
.claude/skills/jeffallan-security-reviewer/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 9% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 40% | 0% |
| case-18 | ✓→✗ | ▼ Worse | 24% | 0% |
| case-15 | ✓→✓ | = Same ✓ | 73% | 0% |
| case-03 | ✓→✓ | = Same ✓ | 36% | 0% |
Security analyst specializing in code review, vulnerability identification, penetration testing, and infrastructure security.
semgrep --config=auto .bandit -r ./srcgitleaks detect --source=.npm audit --audit-level=moderatetrivy fs .Load detailed guidance based on context:
| Topic | Reference | Load When | |-------|-----------|-----------| | SAST Tools | references/sast-tools.md | Running automated scans | | Vulnerability Patterns | references/vulnerability-patterns.md | SQL injection, XSS, manual review | | Secret Scanning | references/secret-scanning.md | Gitleaks, finding hardcoded secrets | | Penetration Testing | references/penetration-testing.md | Active testing, reconnaissance, exploitation | | Infrastructure Security | references/infrastructure-security.md | DevSecOps, cloud security, compliance | | Report Template | references/report-template.md | Writing security report |
ID: FIND-001
Severity: High (CVSS 8.1)
Title: SQL Injection in user search endpoint
File: src/api/users.py, line 42
Description: User-supplied input is concatenated directly into a SQL query without parameterization.
Impact: An attacker can read, modify, or delete database contents.
Remediation: Use parameterized queries or an ORM. Replace `cursor.execute(f"SELECT * FROM users WHERE name='{name}'")`
with `cursor.execute("SELECT * FROM users WHERE name=%s", (name,))`.
References: CWE-89, OWASP A03:2021OWASP Top 10, CWE, Semgrep, Bandit, ESLint Security, gosec, npm audit, gitleaks, trufflehog, CVSS scoring, nmap, Burp Suite, sqlmap, Trivy, Checkov, HashiCorp Vault, AWS Security Hub, CIS benchmarks, SOC2, ISO27001
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-02 | fail→fail | 45,328 | 30,123 | -34% | 1 | 1 | 0% | 3,956 | 4,963 | +25% | 0 | 0 | — |
case-01 | fail→fail | 8,289 | 8,758 | +6% | 1 | 1 | 0% | 724 | 1,955 | +170% | 0 | 0 | — |
case-15 | pass→pass | 17,711 | 17,833 | +1% | 1 | 1 | 0% | 985 | 1,702 | +73% | 0 | 0 | — |
case-03 | pass→pass | 20,848 | 13,382 | -36% | 1 | 1 | 0% | 2,321 | 3,156 | +36% | 0 | 0 | — |
case-04 | fail→pass | 29,120 | 28,050 | -4% | 1 | 1 | 0% | 6,183 | 6,746 | +9% | 0 | 0 | — |
case-05 | pass→pass | 16,129 | 15,912 | -1% | 1 | 1 | 0% | 2,915 | 3,646 | +25% | 0 | 0 | — |
case-06 | pass→pass | 5,274 | 4,052 | -23% | 1 | 1 | 0% | 1,005 | 1,553 | +55% | 0 | 0 | — |
case-16 | pass→pass | 13,396 | 17,047 | +27% | 1 | 1 | 0% | 1,162 | 2,702 | +133% | 0 | 0 | — |
case-07 | pass→pass | 5,647 | 4,932 | -13% | 1 | 1 | 0% | 978 | 1,771 | +81% | 0 | 0 | — |
case-08 | pass→pass | 6,911 | 3,853 | -44% | 1 | 1 | 0% | 1,232 | 1,519 | +23% | 0 | 0 | — |
case-09 | pass→pass | 7,359 | 4,041 | -45% | 1 | 1 | 0% | 1,291 | 1,609 | +25% | 0 | 0 | — |
case-10 | fail→pass | 5,184 | 6,041 | +17% | 1 | 1 | 0% | 978 | 1,368 | +40% | 0 | 0 | — |
case-11 | pass→pass | 10,782 | 11,120 | +3% | 1 | 1 | 0% | 1,695 | 2,649 | +56% | 0 | 0 | — |
case-12 | pass→pass | 13,498 | 11,548 | -14% | 1 | 1 | 0% | 2,115 | 3,046 | +44% | 0 | 0 | — |
case-13 | pass→pass | 11,732 | 10,075 | -14% | 1 | 1 | 0% | 1,903 | 2,142 | +13% | 0 | 0 | — |
case-14 | pass→pass | 11,647 | 7,103 | -39% | 1 | 1 | 0% | 2,146 | 2,264 | +5% | 0 | 0 | — |
case-17 | pass→pass | 8,299 | 5,586 | -33% | 1 | 1 | 0% | 1,377 | 1,772 | +29% | 0 | 0 | — |
case-18 | pass→fail | 14,126 | 13,331 | -6% | 1 | 1 | 0% | 2,344 | 2,915 | +24% | 0 | 0 | — |
case-19 | pass→pass | 13,208 | 11,826 | -10% | 1 | 1 | 0% | 1,764 | 2,254 | +28% | 0 | 0 | — |
case-20 | pass→pass | 11,525 | 7,053 | -39% | 1 | 1 | 0% | 2,218 | 2,209 | -0% | 0 | 0 | — |
case-21 | pass→pass | 10,398 | 6,309 | -39% | 1 | 1 | 0% | 1,748 | 1,992 | +14% | 0 | 0 | — |
case-22 | pass→pass | 11,928 | 11,992 | +1% | 1 | 1 | 0% | 2,154 | 2,944 | +37% | 0 | 0 | — |
case-23 | pass→pass | 13,257 | 12,735 | -4% | 1 | 1 | 0% | 2,408 | 3,145 | +31% | 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. The headline lift of 0 percentage points is the difference between those two pass rates over the 23 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.