Install any skill in seconds. Free to start, no credit card required.
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. Use when the user asks to threat model a codebase or path, enumerate threats or abuse paths, or perform AppSec threat modeling. Do NOT use for general architecture summaries, code review, security best practices (use security-best-practices), or non-security design work.
.claude/skills/tech-leads-club-security-threat-model/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 168% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 40% | 0% |
| case-14 | ✓→✓ | = Same ✓ | 15% | 0% |
| case-02 | ✓→✓ | = Same ✓ | 151% | 0% |
| case-04 | ✓→✓ | = Same ✓ | 390% | 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.
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-14 | pass→pass | 112,486 | 10,437 | -91% | 1 | 1 | 0% | 2,419 | 2,771 | +15% | 0 | 0 | — |
case-01 | fail→pass | 8,695 | 13,822 | +59% | 1 | 1 | 0% | 933 | 2,505 | +168% | 0 | 0 | — |
case-02 | pass→pass | 4,616 | 4,386 | -5% | 1 | 1 | 0% | 699 | 1,752 | +151% | 0 | 0 | — |
case-03 | fail→pass | 9,524 | 7,212 | -24% | 1 | 1 | 0% | 1,573 | 2,197 | +40% | 0 | 0 | — |
case-04 | pass→pass | 2,027 | 2,498 | +23% | 1 | 1 | 0% | 304 | 1,490 | +390% | 0 | 0 | — |
case-05 | pass→pass | 6,839 | 9,364 | +37% | 1 | 1 | 0% | 1,074 | 2,628 | +145% | 0 | 0 | — |
case-06 | pass→pass | 11,744 | 18,759 | +60% | 1 | 1 | 0% | 1,908 | 3,977 | +108% | 0 | 0 | — |
case-07 | pass→pass | 11,703 | 11,125 | -5% | 1 | 1 | 0% | 1,857 | 2,804 | +51% | 0 | 0 | — |
case-08 | pass→pass | 14,177 | 23,819 | +68% | 1 | 1 | 0% | 2,441 | 2,813 | +15% | 0 | 0 | — |
case-09 | pass→pass | 15,773 | 16,969 | +8% | 1 | 1 | 0% | 1,625 | 2,921 | +80% | 0 | 0 | — |
case-10 | pass→pass | 8,697 | 10,382 | +19% | 1 | 1 | 0% | 1,585 | 2,883 | +82% | 0 | 0 | — |
case-11 | pass→pass | 48,702 | 4,278 | -91% | 1 | 1 | 0% | 1,453 | 1,782 | +23% | 0 | 0 | — |
case-12 | pass→pass | 10,441 | 5,969 | -43% | 1 | 1 | 0% | 1,677 | 2,147 | +28% | 0 | 0 | — |
case-13 | pass→pass | 12,578 | 6,574 | -48% | 1 | 1 | 0% | 2,036 | 2,216 | +9% | 0 | 0 | — |
case-15 | pass→pass | 13,201 | 10,105 | -23% | 1 | 1 | 0% | 2,087 | 2,767 | +33% | 0 | 0 | — |
case-16 | pass→pass | 12,354 | 5,623 | -54% | 1 | 1 | 0% | 1,862 | 1,988 | +7% | 0 | 0 | — |
case-17 | pass→pass | 7,588 | 4,566 | -40% | 1 | 1 | 0% | 1,220 | 1,867 | +53% | 0 | 0 | — |
case-18 | pass→pass | 10,005 | 9,536 | -5% | 1 | 1 | 0% | 1,726 | 2,748 | +59% | 0 | 0 | — |
case-19 | pass→pass | 8,204 | 13,445 | +64% | 1 | 1 | 0% | 1,395 | 3,265 | +134% | 0 | 0 | — |
case-20 | pass→pass | 11,652 | 8,100 | -30% | 1 | 1 | 0% | 610 | 1,975 | +224% | 0 | 0 | — |
case-21 | pass→pass | 10,119 | 9,192 | -9% | 1 | 1 | 0% | 1,615 | 2,622 | +62% | 0 | 0 | — |
case-22 | pass→pass | 10,219 | 15,116 | +48% | 1 | 1 | 0% | 1,627 | 3,466 | +113% | 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 +9 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.