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Get Started Free →Define distinct stakeholder or analytical perspectives with their values, concerns, and evaluation criteria.
.claude/skills/yogsoth-ai-perspective-assignment/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 105% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 54% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 68% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 16% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 203% | 0% |
Defines distinct perspectives for multi-perspective attack — each with unique values, concerns, success criteria, and likely objections. Perspectives must be genuinely different to ensure comprehensive coverage.
Spawns a subagent that analyzes the decision context and stakeholder landscape to produce well-differentiated perspective briefs.
Output must include:
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Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | spawn-agent | Spawn a customized CC subagent with full MCP tool access. Used by SOPs that declare execution: subagent. |
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| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-04 | fail→pass | 26,178 | 22,186 | -15% | 1 | 1 | 0% | 1,396 | 2,861 | +105% | 0 | 0 | — |
case-01 | fail→pass | 33,178 | 31,489 | -5% | 1 | 1 | 0% | 1,643 | 2,534 | +54% | 0 | 0 | — |
case-02 | pass→pass | 21,507 | 28,526 | +33% | 1 | 1 | 0% | 2,481 | 3,727 | +50% | 0 | 0 | — |
case-03 | fail→pass | 18,075 | 32,360 | +79% | 1 | 1 | 0% | 1,927 | 3,231 | +68% | 0 | 0 | — |
case-05 | fail→fail | 48,378 | 21,596 | -55% | 1 | 1 | 0% | 2,666 | 2,970 | +11% | 0 | 0 | — |
case-06 | fail→pass | 29,883 | 35,244 | +18% | 1 | 1 | 0% | 3,466 | 4,015 | +16% | 0 | 0 | — |
case-07 | fail→pass | 15,517 | 121,748 | +685% | 1 | 1 | 0% | 1,548 | 4,683 | +203% | 0 | 0 | — |
case-08 | fail→pass | 12,496 | 24,618 | +97% | 1 | 1 | 0% | 1,043 | 2,720 | +161% | 0 | 0 | — |
case-09 | fail→pass | 18,695 | 24,678 | +32% | 1 | 1 | 0% | 2,134 | 3,394 | +59% | 0 | 0 | — |
case-10 | fail→pass | 18,721 | 27,759 | +48% | 1 | 1 | 0% | 2,180 | 4,039 | +85% | 0 | 0 | — |
case-11 | fail→pass | 19,940 | 24,550 | +23% | 1 | 1 | 0% | 2,951 | 3,443 | +17% | 0 | 0 | — |
case-12 | fail→pass | 15,749 | 36,451 | +131% | 1 | 1 | 0% | 2,352 | 3,791 | +61% | 0 | 0 | — |
case-13 | fail→pass | 33,016 | 24,924 | -25% | 1 | 1 | 0% | 2,465 | 3,440 | +40% | 0 | 0 | — |
case-14 | fail→pass | 22,591 | 18,720 | -17% | 1 | 1 | 0% | 2,297 | 3,377 | +47% | 0 | 0 | — |
case-15 | fail→pass | 20,651 | 31,257 | +51% | 1 | 1 | 0% | 2,178 | 4,808 | +121% | 0 | 0 | — |
case-16 | fail→fail | 52,588 | 25,188 | -52% | 1 | 1 | 0% | 8,233 | 3,588 | -56% | 0 | 0 | — |
case-17 | fail→pass | 16,477 | 18,939 | +15% | 1 | 1 | 0% | 1,695 | 2,466 | +45% | 0 | 0 | — |
case-18 | fail→pass | 23,235 | 19,236 | -17% | 1 | 1 | 0% | 2,884 | 2,538 | -12% | 0 | 0 | — |
case-19 | fail→pass | 21,655 | 28,512 | +32% | 1 | 1 | 0% | 2,169 | 3,592 | +66% | 0 | 0 | — |
case-20 | pass→fail | 18,751 | 36,142 | +93% | 1 | 1 | 0% | 2,365 | 5,323 | +125% | 0 | 0 | — |
case-21 | pass→fail | 25,868 | 32,877 | +27% | 1 | 1 | 0% | 2,987 | 4,666 | +56% | 0 | 0 | — |
case-22 | pass→fail | 18,156 | 39,168 | +116% | 1 | 1 | 0% | 2,291 | 5,722 | +150% | 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 +59 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.