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Get Started Free →Tear down a competitor's product from screenshots of its actual UI — onboarding, pricing page, core flows. Use when given screenshots of a rival's app or website and asked what they're doing, how their flow works, or what to learn/steal/avoid. Produces a UX-and-strategy teardown grounded in what is visibly on screen, with an inferences-vs-observations split. Requires image input. For a market-level teardown without screenshots use competitor-teardown.
.claude/skills/mohitagw15856-screenshot-teardown/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | -66% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -71% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -41% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 24% | 0% |
| case-16 | ✗→✓ | ▲ Improved | -49% | 0% |
Marketing pages say what a competitor claims; screenshots show what they shipped. This skill reads real UI evidence — layout, copy, defaults, friction, what's promoted and what's buried — and turns it into competitive insight you can defend, because every claim points at pixels.
[inference]Evidence base: n] screenshots of what], captured date if known]. What this evidence can't show: limits].
Screen-by-screen: #1 — screen name] — Shows: observed]. Optimised for: read]. Friction: count/notes]. Notable copy: "verbatim]".
What they're optimising for overall: 2-3 lines synthesising the design intent]
Strategic signals: | Signal | Evidence (screenshot #) | Observed / Inference | |---|---|---|
For us — learn / steal / avoid:
[inference]competitor-teardown's job| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 35,062 | 11,500 | -67% | 1 | 1 | 0% | 6,054 | 1,935 | -68% | 0 | 0 | — |
case-06 | pass→pass | 25,776 | 25,006 | -3% | 1 | 1 | 0% | 3,052 | 4,236 | +39% | 0 | 0 | — |
case-02 | fail→pass | 39,289 | 9,631 | -75% | 1 | 1 | 0% | 4,642 | 1,569 | -66% | 0 | 0 | — |
case-03 | fail→pass | 41,092 | 9,296 | -77% | 1 | 1 | 0% | 5,581 | 1,604 | -71% | 0 | 0 | — |
case-04 | pass→fail | 30,985 | 7,668 | -75% | 1 | 1 | 0% | 3,564 | 1,819 | -49% | 0 | 0 | — |
case-05 | fail→fail | 10,166 | 4,915 | -52% | 1 | 1 | 0% | 643 | 1,563 | +143% | 0 | 0 | — |
case-15 | pass→pass | 20,530 | 24,006 | +17% | 1 | 1 | 0% | 2,991 | 4,256 | +42% | 0 | 0 | — |
case-07 | fail→pass | 25,556 | 4,868 | -81% | 1 | 1 | 0% | 2,661 | 1,560 | -41% | 0 | 0 | — |
case-08 | fail→fail | 26,837 | 9,306 | -65% | 1 | 1 | 0% | 3,077 | 1,457 | -53% | 0 | 0 | — |
case-09 | pass→fail | 27,847 | 14,132 | -49% | 1 | 1 | 0% | 2,944 | 2,124 | -28% | 0 | 0 | — |
case-10 | fail→fail | 19,605 | 9,785 | -50% | 1 | 1 | 0% | 2,576 | 1,494 | -42% | 0 | 0 | — |
case-11 | fail→pass | 24,398 | 22,516 | -8% | 1 | 1 | 0% | 2,958 | 3,671 | +24% | 0 | 0 | — |
case-12 | pass→fail | 24,837 | 9,673 | -61% | 1 | 1 | 0% | 3,122 | 1,617 | -48% | 0 | 0 | — |
case-13 | fail→fail | 7,667 | 7,840 | +2% | 1 | 1 | 0% | 1,078 | 1,316 | +22% | 0 | 0 | — |
case-14 | fail→fail | 38,608 | 5,240 | -86% | 1 | 1 | 0% | 3,296 | 1,580 | -52% | 0 | 0 | — |
case-16 | fail→pass | 28,544 | 11,279 | -60% | 1 | 1 | 0% | 3,615 | 1,831 | -49% | 0 | 0 | — |
case-17 | fail→pass | 24,995 | 8,484 | -66% | 1 | 1 | 0% | 3,193 | 1,406 | -56% | 0 | 0 | — |
case-18 | fail→fail | 17,830 | 10,369 | -42% | 1 | 1 | 0% | 2,728 | 1,674 | -39% | 0 | 0 | — |
case-19 | fail→pass | 22,896 | 21,007 | -8% | 1 | 1 | 0% | 2,536 | 3,817 | +51% | 0 | 0 | — |
case-20 | fail→fail | 28,149 | 31,139 | +11% | 1 | 1 | 0% | 3,110 | 4,780 | +54% | 0 | 0 | — |
case-21 | pass→pass | 23,388 | 15,003 | -36% | 1 | 1 | 0% | 2,434 | 2,237 | -8% | 0 | 0 | — |
case-22 | fail→fail | 22,808 | 8,688 | -62% | 1 | 1 | 0% | 2,905 | 1,465 | -50% | 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 +18 percentage points is the difference between those two pass rates over the 22 comparable cases. 3 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.