Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Autopsy a slide deck from photos or screenshots of its slides — the narrative arc, the numbers, and what each slide is hiding. Use when given slide images (a competitor's pitch, a conference talk, your own deck before a big meeting) and asked what the deck argues, whether it holds up, or how to counter or improve it. Produces a slide-by-slide read, the reconstructed argument chain, weak links, and the questions the deck is engineered to avoid. Requires image input.
.claude/skills/mohitagw15856-deck-autopsy/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | 44% | 0% |
| case-01 | ✗→✓ | ▲ Improved | -75% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 12% | 0% |
| case-21 | ✓→✗ | ▼ Worse | 25% | 0% |
| case-04 | ✓→✓ | = Same ✓ | 32% | 0% |
A deck is an argument wearing design. This skill reads slide images the way a sceptical partner does — reconstructing the claim chain, checking the numbers against each other across slides, and naming the questions the deck is built to keep the room from asking.
The deck's argument, reconstructed:
∴ the ask/conclusion — slide #] Weakest link: which step, why]
Slide-by-slide: #n] — Claims: headline]. Support: what's actually shown]. Design notes: emphasis/burial/chart crimes]. Verdict: holds / overreaches / unproven.
Numbers cross-check: | Figure | Slide(s) | Consistent? | Note | |---|---|---|---|
Questions this deck is built to avoid:
If it's your deck] Hardening list: the 3-5 fixes, in order of how likely each hole is to be found in the room]
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-02 | fail→fail | 17,320 | 3,239 | -81% | 1 | 1 | 0% | 1,350 | 1,457 | +8% | 0 | 0 | — |
case-03 | fail→pass | 16,577 | 19,368 | +17% | 1 | 1 | 0% | 2,894 | 4,177 | +44% | 0 | 0 | — |
case-01 | fail→pass | 32,208 | 2,999 | -91% | 1 | 1 | 0% | 6,269 | 1,558 | -75% | 0 | 0 | — |
case-04 | pass→pass | 8,862 | 7,385 | -17% | 1 | 1 | 0% | 1,514 | 2,005 | +32% | 0 | 0 | — |
case-05 | pass→pass | 11,328 | 7,567 | -33% | 1 | 1 | 0% | 1,816 | 2,088 | +15% | 0 | 0 | — |
case-06 | pass→pass | 14,225 | 13,370 | -6% | 1 | 1 | 0% | 2,393 | 3,140 | +31% | 0 | 0 | — |
case-07 | pass→pass | 10,645 | 8,891 | -16% | 1 | 1 | 0% | 1,500 | 2,318 | +55% | 0 | 0 | — |
case-08 | pass→pass | 15,051 | 14,091 | -6% | 1 | 1 | 0% | 2,303 | 3,191 | +39% | 0 | 0 | — |
case-09 | fail→pass | 10,248 | 4,488 | -56% | 1 | 1 | 0% | 1,435 | 1,611 | +12% | 0 | 0 | — |
case-10 | pass→pass | 10,365 | 6,808 | -34% | 1 | 1 | 0% | 1,602 | 1,908 | +19% | 0 | 0 | — |
case-11 | pass→pass | 7,778 | 5,786 | -26% | 1 | 1 | 0% | 1,304 | 1,892 | +45% | 0 | 0 | — |
case-12 | pass→pass | 10,707 | 7,563 | -29% | 1 | 1 | 0% | 1,842 | 2,166 | +18% | 0 | 0 | — |
case-13 | pass→pass | 5,167 | 6,177 | +20% | 1 | 1 | 0% | 787 | 1,854 | +136% | 0 | 0 | — |
case-14 | pass→pass | 7,385 | 7,129 | -3% | 1 | 1 | 0% | 1,312 | 2,272 | +73% | 0 | 0 | — |
case-15 | pass→pass | 14,288 | 9,612 | -33% | 1 | 1 | 0% | 2,330 | 2,487 | +7% | 0 | 0 | — |
case-16 | pass→pass | 12,787 | 8,343 | -35% | 1 | 1 | 0% | 1,705 | 2,252 | +32% | 0 | 0 | — |
case-17 | pass→pass | 9,514 | 7,324 | -23% | 1 | 1 | 0% | 1,432 | 2,051 | +43% | 0 | 0 | — |
case-18 | pass→pass | 15,492 | 10,164 | -34% | 1 | 1 | 0% | 2,127 | 2,334 | +10% | 0 | 0 | — |
case-19 | pass→pass | 7,514 | 8,981 | +20% | 1 | 1 | 0% | 1,181 | 2,313 | +96% | 0 | 0 | — |
case-20 | pass→pass | 11,250 | 7,370 | -34% | 1 | 1 | 0% | 2,437 | 2,440 | +0% | 0 | 0 | — |
case-21 | pass→fail | 16,784 | 16,549 | -1% | 1 | 1 | 0% | 2,605 | 3,265 | +25% | 0 | 0 | — |
case-22 | pass→pass | 5,940 | 5,868 | -1% | 1 | 1 | 0% | 933 | 2,119 | +127% | 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. 1 case got worse with the skill loaded, and it is 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.