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.claude/skills/dokhacgiakhoa-security-scanning-security-dependencies/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-11 | ✗→✓ | ▲ Improved | -40% | 0% |
| case-02 | ✓→✓ | = Same ✓ | 41% | 0% |
| case-04 | ✓→✓ | = Same ✓ | 22% | 0% |
| case-05 | ✓→✓ | = Same ✓ | 37% | 0% |
| case-06 | ✓→✓ | = Same ✓ | -8% | 0% |
You are a security expert specializing in dependency vulnerability analysis, SBOM generation, and supply chain security. Scan project dependencies across multiple ecosystems to identify vulnerabilities, assess risks, and provide automated remediation strategies.
The user needs comprehensive dependency security analysis to identify vulnerable packages, outdated dependencies, and license compliance issues. Focus on multi-ecosystem support, vulnerability database integration, SBOM generation, and automated remediation using modern 2024/2025 tools.
$ARGUMENTS
resources/implementation-playbook.md.resources/implementation-playbook.md for detailed patterns and examples.| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 11,076 | 13,614 | +23% | 1 | 1 | 0% | 972 | 1,753 | +80% | 0 | 0 | — |
case-02 | pass→pass | 14,431 | 15,767 | +9% | 1 | 1 | 0% | 1,365 | 1,921 | +41% | 0 | 0 | — |
case-03 | fail→fail | 19,696 | 21,147 | +7% | 1 | 1 | 0% | 2,766 | 3,635 | +31% | 0 | 0 | — |
case-04 | pass→pass | 16,347 | 22,278 | +36% | 1 | 1 | 0% | 2,809 | 3,431 | +22% | 0 | 0 | — |
case-05 | pass→pass | 17,552 | 19,564 | +11% | 1 | 1 | 0% | 2,314 | 3,176 | +37% | 0 | 0 | — |
case-06 | pass→pass | 18,203 | 20,924 | +15% | 1 | 1 | 0% | 2,165 | 1,994 | -8% | 0 | 0 | — |
case-07 | pass→pass | 14,570 | 20,997 | +44% | 1 | 1 | 0% | 2,426 | 3,040 | +25% | 0 | 0 | — |
case-08 | pass→pass | 16,510 | 16,630 | +1% | 1 | 1 | 0% | 1,996 | 2,249 | +13% | 0 | 0 | — |
case-09 | pass→pass | 20,649 | 15,028 | -27% | 1 | 1 | 0% | 2,668 | 2,931 | +10% | 0 | 0 | — |
case-10 | pass→pass | 11,047 | 10,536 | -5% | 1 | 1 | 0% | 2,063 | 2,361 | +14% | 0 | 0 | — |
case-11 | fail→pass | 28,135 | 11,446 | -59% | 1 | 1 | 0% | 4,321 | 2,581 | -40% | 0 | 0 | — |
case-12 | pass→pass | 11,542 | 15,294 | +33% | 1 | 1 | 0% | 2,185 | 2,239 | +2% | 0 | 0 | — |
case-13 | pass→pass | 18,832 | 22,826 | +21% | 1 | 1 | 0% | 2,215 | 3,307 | +49% | 0 | 0 | — |
case-14 | pass→pass | 14,913 | 9,034 | -39% | 1 | 1 | 0% | 1,725 | 1,945 | +13% | 0 | 0 | — |
case-15 | pass→pass | 14,189 | 15,295 | +8% | 1 | 1 | 0% | 1,568 | 1,999 | +27% | 0 | 0 | — |
case-16 | pass→pass | 14,420 | 17,515 | +21% | 1 | 1 | 0% | 2,636 | 3,625 | +38% | 0 | 0 | — |
case-17 | pass→pass | 18,276 | 20,843 | +14% | 1 | 1 | 0% | 2,209 | 3,029 | +37% | 0 | 0 | — |
case-18 | pass→pass | 19,269 | 23,484 | +22% | 1 | 1 | 0% | 2,379 | 3,221 | +35% | 0 | 0 | — |
case-19 | pass→pass | 7,911 | 14,829 | +87% | 1 | 1 | 0% | 1,351 | 1,994 | +48% | 0 | 0 | — |
case-20 | fail→fail | 6,714 | 12,376 | +84% | 1 | 1 | 0% | 627 | 926 | +48% | 0 | 0 | — |
case-21 | fail→fail | 19,936 | 25,646 | +29% | 1 | 1 | 0% | 2,054 | 3,827 | +86% | 0 | 0 | — |
case-22 | fail→fail | 13,223 | 12,888 | -3% | 1 | 1 | 0% | 1,838 | 1,158 | -37% | 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 +5 percentage points is the difference between those two pass rates over the 22 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.