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Get Started Free →CrackMapExec Integration - SMB/WinRM/LDAP/MSSQL Swiss Army Knife
.claude/skills/shadd0wtaka-crackmapexec/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 97% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 23% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -56% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -74% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -53% | 0% |
CrackMapExec Integration - SMB/WinRM/LDAP/MSSQL Swiss Army Knife
Category: exploitation — Exploitation & Credential Attacks
CrackMapExec Integration - SMB/WinRM/LDAP/MSSQL Swiss Army Knife
pythonfrom tools.crackmapexec_integration import CrackMapExec async def main(): tool = CrackMapExec() result = await tool.__init__("target.com") print(result)
bashcrackmapexec --help
deep-recon --tool crackmapexec_integration --target example.comexploitationzen-agents_agent_run agent_type=exploitation tool=crackmapexec_integrationPOST /tools/execute with {"tool_name": "crackmapexec_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-01 | fail→pass | 6,565 | 10,263 | +56% | 1 | 1 | 0% | 967 | 1,906 | +97% | 0 | 0 | — |
case-02 | fail→pass | 10,776 | 10,390 | -4% | 1 | 1 | 0% | 1,674 | 2,054 | +23% | 0 | 0 | — |
case-03 | fail→fail | 6,951 | 13,239 | +90% | 1 | 1 | 0% | 488 | 1,593 | +226% | 0 | 0 | — |
case-04 | fail→pass | 6,032 | 1,662 | -72% | 1 | 1 | 0% | 990 | 437 | -56% | 0 | 0 | — |
case-05 | fail→pass | 15,031 | 2,987 | -80% | 1 | 1 | 0% | 2,798 | 723 | -74% | 0 | 0 | — |
case-06 | pass→pass | 7,196 | 3,648 | -49% | 1 | 1 | 0% | 1,258 | 805 | -36% | 0 | 0 | — |
case-07 | fail→pass | 18,976 | 8,232 | -57% | 1 | 1 | 0% | 1,569 | 743 | -53% | 0 | 0 | — |
case-08 | pass→pass | 9,236 | 1,847 | -80% | 1 | 1 | 0% | 1,337 | 519 | -61% | 0 | 0 | — |
case-09 | fail→pass | 18,738 | 11,789 | -37% | 1 | 1 | 0% | 1,110 | 502 | -55% | 0 | 0 | — |
case-10 | fail→pass | 9,115 | 2,892 | -68% | 1 | 1 | 0% | 1,571 | 689 | -56% | 0 | 0 | — |
case-11 | fail→pass | 7,607 | 9,797 | +29% | 1 | 1 | 0% | 1,223 | 675 | -45% | 0 | 0 | — |
case-12 | fail→pass | 12,070 | 7,801 | -35% | 1 | 1 | 0% | 1,848 | 518 | -72% | 0 | 0 | — |
case-13 | fail→pass | 12,171 | 12,024 | -1% | 1 | 1 | 0% | 1,003 | 1,523 | +52% | 0 | 0 | — |
case-14 | pass→pass | 8,781 | 1,614 | -82% | 1 | 1 | 0% | 1,313 | 512 | -61% | 0 | 0 | — |
case-15 | fail→pass | 12,763 | 3,719 | -71% | 1 | 1 | 0% | 2,009 | 896 | -55% | 0 | 0 | — |
case-16 | fail→pass | 9,399 | 4,966 | -47% | 1 | 1 | 0% | 1,467 | 407 | -72% | 0 | 0 | — |
case-17 | pass→pass | 2,918 | 5,544 | +90% | 1 | 1 | 0% | 380 | 372 | -2% | 0 | 0 | — |
case-18 | pass→pass | 6,642 | 1,949 | -71% | 1 | 1 | 0% | 1,005 | 497 | -51% | 0 | 0 | — |
case-19 | pass→pass | 5,596 | 2,401 | -57% | 1 | 1 | 0% | 992 | 520 | -48% | 0 | 0 | — |
case-20 | fail→pass | 5,950 | 3,850 | -35% | 1 | 1 | 0% | 1,111 | 945 | -15% | 0 | 0 | — |
case-21 | pass→pass | 8,265 | 8,748 | +6% | 1 | 1 | 0% | 1,699 | 1,368 | -19% | 0 | 0 | — |
case-22 | pass→pass | 8,374 | 8,910 | +6% | 1 | 1 | 0% | 1,487 | 1,963 | +32% | 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 +59 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.