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Get Started Free →Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Trigger only when the user explicitly asks to threat model a codebase or path, enumerate threats/abuse paths, or perform AppSec threat modeling. Do not trigger for general architecture summaries, code review, or non-security design work.
.claude/skills/davila7-security-threat-model/SKILL.md| Model | Eval pass | Runs |
|---|---|---|
| gemini-3.6-flash | 100% | 1 |
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 6% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -16% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 11% | 0% |
| case-22 | ✗→✓ | ▲ Improved | 91% | 0% |
| case-11 | ✓→✓ | = Same ✓ | 13% | 0% |
Deliver an actionable AppSec-grade threat model that is specific to the repository or a project path, not a generic checklist. Anchor every architectural claim to evidence in the repo and keep assumptions explicit. Prioritizing realistic attacker goals and concrete impacts over generic checklists.
1) Collect (or infer) inputs:
references/prompt-template.md to generate a repository summary.references/prompt-template.md. Use it verbatim when possible.references/prompt-template.md<repo-or-dir-name>-threat-model.md (use the basename of the repo root, or the in-scope directory if you were asked to model a subpath).references/prompt-template.mdreferences/security-controls-and-assets.mdOnly load the reference files you need. Keep the final result concise, grounded, and reviewable.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-11 | pass→pass | 11,652 | 5,862 | -50% | 1 | 1 | 0% | 1,737 | 1,960 | +13% | 0 | 0 | — |
case-01 | fail→pass | 26,978 | 11,576 | -57% | 1 | 1 | 0% | 2,273 | 2,406 | +6% | 0 | 0 | — |
case-02 | fail→pass | 22,852 | 19,902 | -13% | 1 | 1 | 0% | 3,986 | 3,341 | -16% | 0 | 0 | — |
case-03 | fail→pass | 19,105 | 13,291 | -30% | 1 | 1 | 0% | 2,527 | 2,801 | +11% | 0 | 0 | — |
case-04 | pass→pass | 4,822 | 5,170 | +7% | 1 | 1 | 0% | 915 | 1,966 | +115% | 0 | 0 | — |
case-05 | pass→pass | 18,061 | 25,654 | +42% | 1 | 1 | 0% | 3,090 | 5,146 | +67% | 0 | 0 | — |
case-06 | pass→pass | 17,977 | 20,700 | +15% | 1 | 1 | 0% | 3,041 | 4,398 | +45% | 0 | 0 | — |
case-07 | fail→fail | 13,324 | 13,272 | -0% | 1 | 1 | 0% | 2,323 | 1,333 | -43% | 0 | 0 | — |
case-08 | pass→pass | 10,017 | 8,868 | -11% | 1 | 1 | 0% | 1,734 | 2,427 | +40% | 0 | 0 | — |
case-09 | pass→pass | 11,371 | 6,711 | -41% | 1 | 1 | 0% | 1,713 | 2,107 | +23% | 0 | 0 | — |
case-10 | pass→pass | 16,486 | 9,549 | -42% | 1 | 1 | 0% | 2,881 | 2,660 | -8% | 0 | 0 | — |
case-12 | pass→pass | 17,349 | 16,673 | -4% | 1 | 1 | 0% | 2,742 | 3,706 | +35% | 0 | 0 | — |
case-13 | pass→pass | 12,613 | 3,619 | -71% | 1 | 1 | 0% | 2,027 | 1,704 | -16% | 0 | 0 | — |
case-14 | pass→pass | 8,805 | 5,341 | -39% | 1 | 1 | 0% | 1,408 | 1,933 | +37% | 0 | 0 | — |
case-15 | pass→pass | 10,943 | 8,711 | -20% | 1 | 1 | 0% | 1,714 | 2,341 | +37% | 0 | 0 | — |
case-20 | pass→pass | 15,015 | 11,485 | -24% | 1 | 1 | 0% | 2,602 | 3,167 | +22% | 0 | 0 | — |
case-16 | pass→pass | 9,582 | 6,688 | -30% | 1 | 1 | 0% | 1,701 | 1,943 | +14% | 0 | 0 | — |
case-17 | pass→pass | 9,254 | 5,258 | -43% | 1 | 1 | 0% | 1,688 | 2,021 | +20% | 0 | 0 | — |
case-18 | pass→pass | 5,206 | 4,237 | -19% | 1 | 1 | 0% | 839 | 1,800 | +115% | 0 | 0 | — |
case-19 | pass→pass | 6,220 | 3,172 | -49% | 1 | 1 | 0% | 1,159 | 1,657 | +43% | 0 | 0 | — |
case-21 | pass→pass | 13,248 | 8,190 | -38% | 1 | 1 | 0% | 2,031 | 2,468 | +22% | 0 | 0 | — |
case-22 | fail→pass | 16,032 | 1,508 | -91% | 1 | 1 | 0% | 694 | 1,324 | +91% | 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, and 20 counted toward the lift figure. The other 2 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +18 percentage points is the difference between those two pass rates over the 20 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.