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Get Started Free →BloodHound Integration - Active Directory Analysis
.claude/skills/shadd0wtaka-bloodhound/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 20% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -53% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -43% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -56% | 0% |
| case-12 | ✗→✓ | ▲ Improved | -49% | 0% |
BloodHound Integration - Active Directory Analysis
Category: reconnaissance — Active Directory Enumeration
BloodHound Integration - Active Directory Analysis
pythonfrom tools.bloodhound_integration import BloodHoundAnalyzer async def main(): tool = BloodHoundAnalyzer() result = await tool.__init__("target.com") print(result)
bashbloodhound --help
deep-recon --tool bloodhound_integration --target example.comreconnaissancezen-agents_agent_run agent_type=reconnaissance tool=bloodhound_integrationPOST /tools/execute with {"tool_name": "bloodhound_integration", "target": "example.com"}| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-02 | fail→pass | 13,900 | 14,824 | +7% | 1 | 1 | 0% | 2,251 | 2,706 | +20% | 0 | 0 | — |
case-01 | fail→fail | 11,491 | 14,982 | +30% | 1 | 1 | 0% | 1,568 | 2,609 | +66% | 0 | 0 | — |
case-03 | fail→fail | 7,120 | 12,812 | +80% | 1 | 1 | 0% | 508 | 1,465 | +188% | 0 | 0 | — |
case-04 | pass→pass | 11,687 | 6,970 | -40% | 1 | 1 | 0% | 1,940 | 1,373 | -29% | 0 | 0 | — |
case-05 | pass→pass | 14,221 | 13,055 | -8% | 1 | 1 | 0% | 2,622 | 2,436 | -7% | 0 | 0 | — |
case-06 | pass→pass | 8,625 | 5,439 | -37% | 1 | 1 | 0% | 1,499 | 1,090 | -27% | 0 | 0 | — |
case-07 | fail→pass | 11,381 | 3,605 | -68% | 1 | 1 | 0% | 1,894 | 894 | -53% | 0 | 0 | — |
case-08 | pass→pass | 13,274 | 1,677 | -87% | 1 | 1 | 0% | 2,067 | 418 | -80% | 0 | 0 | — |
case-09 | pass→pass | 4,791 | 1,843 | -62% | 1 | 1 | 0% | 785 | 458 | -42% | 0 | 0 | — |
case-10 | fail→pass | 5,505 | 10,959 | +99% | 1 | 1 | 0% | 928 | 528 | -43% | 0 | 0 | — |
case-11 | fail→pass | 11,117 | 3,255 | -71% | 1 | 1 | 0% | 1,763 | 771 | -56% | 0 | 0 | — |
case-12 | fail→pass | 11,654 | 1,810 | -84% | 1 | 1 | 0% | 880 | 451 | -49% | 0 | 0 | — |
case-13 | pass→pass | 7,691 | 3,240 | -58% | 1 | 1 | 0% | 1,257 | 661 | -47% | 0 | 0 | — |
case-14 | pass→pass | 7,342 | 2,373 | -68% | 1 | 1 | 0% | 1,182 | 586 | -50% | 0 | 0 | — |
case-15 | fail→pass | 3,991 | 1,802 | -55% | 1 | 1 | 0% | 589 | 470 | -20% | 0 | 0 | — |
case-16 | pass→pass | 3,689 | 1,862 | -50% | 1 | 1 | 0% | 537 | 475 | -12% | 0 | 0 | — |
case-17 | fail→pass | 10,342 | 1,955 | -81% | 1 | 1 | 0% | 1,552 | 473 | -70% | 0 | 0 | — |
case-18 | fail→pass | 6,350 | 2,644 | -58% | 1 | 1 | 0% | 1,089 | 642 | -41% | 0 | 0 | — |
case-19 | fail→pass | 7,332 | 1,803 | -75% | 1 | 1 | 0% | 1,222 | 400 | -67% | 0 | 0 | — |
case-20 | fail→pass | 4,354 | 1,892 | -57% | 1 | 1 | 0% | 743 | 442 | -41% | 0 | 0 | — |
case-21 | fail→pass | 8,446 | 4,931 | -42% | 1 | 1 | 0% | 1,428 | 347 | -76% | 0 | 0 | — |
case-22 | fail→pass | 13,018 | 2,822 | -78% | 1 | 1 | 0% | 2,193 | 691 | -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. 22 cases were attempted. The headline lift of +55 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.