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Get Started Free →Simulate your thesis defense before the real one — a committee of examiner archetypes probing YOUR actual thesis, the questions you hoped nobody would ask, and a debrief with preparation priorities. Use when asked simulate my thesis defense, grill me on my dissertation, what will my committee ask, or prep me for my viva. Produces the defense transcript with your answers stress-tested, the committee's private deliberation, and a debrief ranking the exposed weaknesses by preparability.
.claude/skills/mohitagw15856-the-thesis-defense/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -12% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 20% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -21% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 344% | 0% |
| case-05 | ✓→✗ | ▼ Worse | 86% | 0% |
Every thesis has three or four questions its author prays nobody asks — and committees are selected for exactly the expertise that asks them. This skill runs the defense early: examiner archetypes with different agendas probing the actual thesis (methods, alternatives, scope, the gap between claims and evidence), pushing back on weak answers the way real committees do. Then it breaks character and turns every wobble into a preparation item.
Ask for these if not provided:
| Archetype | What they probe | Their tell | |---|---|---| | The Methodologist | Whether the methods can carry the claims | "Walk me through why n=22 supports that verb" | | The Alternative-Explainer | Every rival account of the findings | "Couldn't selection effects produce exactly this?" | | The Scope-Enforcer | Claims that outrun the evidence | "Your title says 'workers' — your data says one platform in one city" | | The Generous-Elder | The contribution's actual worth | Asks the easy-sounding question that's hardest: "So what?" |
Defense mechanics to honor: the first ten minutes set the committee's posture — the opening summary gets probed as delivered · "I don't know, but here's how I'd find out" outscores a bluff every time — the simulation rewards it · committees defend their own fields — the adjacent-field question is about their literature, not yours · limitations volunteered read as maturity; limitations extracted read as concealment · the dread question always arrives, usually reworded.
> Simulation — a plausible adversarial reading, not a prediction.
The examination, grounded ONLY in the supplied thesis; where the committee finds a gap, the gap is the finding. Includes at least one bluffed answer punished and one honest "I don't know" rewarded.]
After the candidate leaves: pass / pass-with-revisions / major concerns — the real assessment vs. what gets said in the room]
| Weakness exposed | Preparable before the real defense? | The answer-shape that holds | |---|---|---| Plus: the opening-summary rewrite, the volunteer-these-limitations list, and the dread question's prepared answer in full]
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 65,435 | 52,929 | -19% | 1 | 1 | 0% | 8,382 | 7,350 | -12% | 0 | 0 | — |
case-02 | fail→pass | 34,634 | 59,864 | +73% | 1 | 1 | 0% | 4,853 | 5,823 | +20% | 0 | 0 | — |
case-03 | fail→pass | 56,645 | 44,808 | -21% | 1 | 1 | 0% | 7,826 | 6,175 | -21% | 0 | 0 | — |
case-04 | pass→pass | 41,018 | 23,207 | -43% | 1 | 1 | 0% | 4,523 | 3,412 | -25% | 0 | 0 | — |
case-05 | pass→fail | 14,755 | 18,654 | +26% | 1 | 1 | 0% | 1,573 | 2,932 | +86% | 0 | 0 | — |
case-06 | pass→pass | 18,596 | 27,169 | +46% | 1 | 1 | 0% | 1,859 | 3,988 | +115% | 0 | 0 | — |
case-07 | fail→pass | 14,219 | 33,396 | +135% | 1 | 1 | 0% | 1,136 | 5,049 | +344% | 0 | 0 | — |
case-08 | pass→pass | 16,496 | 28,758 | +74% | 1 | 1 | 0% | 1,988 | 4,368 | +120% | 0 | 0 | — |
case-09 | pass→pass | 29,476 | 11,238 | -62% | 1 | 1 | 0% | 3,494 | 2,522 | -28% | 0 | 0 | — |
case-10 | pass→pass | 24,553 | 40,002 | +63% | 1 | 1 | 0% | 2,418 | 5,165 | +114% | 0 | 0 | — |
case-11 | pass→pass | 24,510 | 39,444 | +61% | 1 | 1 | 0% | 2,421 | 5,637 | +133% | 0 | 0 | — |
case-12 | pass→pass | 28,095 | 39,526 | +41% | 1 | 1 | 0% | 3,157 | 5,641 | +79% | 0 | 0 | — |
case-13 | pass→fail | 16,229 | 10,229 | -37% | 1 | 1 | 0% | 1,727 | 2,354 | +36% | 0 | 0 | — |
case-14 | pass→pass | 30,599 | 36,984 | +21% | 1 | 1 | 0% | 2,839 | 5,668 | +100% | 0 | 0 | — |
case-15 | pass→pass | 32,259 | 46,819 | +45% | 1 | 1 | 0% | 3,462 | 6,248 | +80% | 0 | 0 | — |
case-16 | pass→fail | 26,331 | 16,346 | -38% | 1 | 1 | 0% | 2,748 | 2,519 | -8% | 0 | 0 | — |
case-17 | pass→pass | 24,344 | 38,839 | +60% | 1 | 1 | 0% | 2,777 | 5,572 | +101% | 0 | 0 | — |
case-18 | pass→pass | 12,283 | 41,807 | +240% | 1 | 1 | 0% | 1,688 | 5,913 | +250% | 0 | 0 | — |
case-19 | fail→fail | 13,254 | 22,931 | +73% | 1 | 1 | 0% | 1,303 | 3,595 | +176% | 0 | 0 | — |
case-20 | pass→pass | 39,629 | 44,796 | +13% | 1 | 1 | 0% | 5,084 | 6,905 | +36% | 0 | 0 | — |
case-21 | pass→pass | 18,968 | 49,645 | +162% | 1 | 1 | 0% | 1,954 | 7,105 | +264% | 0 | 0 | — |
case-22 | pass→fail | 58,093 | 9,608 | -83% | 1 | 1 | 0% | 8,241 | 2,634 | -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 0 percentage points is the difference between those two pass rates over the 22 comparable cases. 4 cases got worse with the skill loaded, and they are included in that figure.
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.