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Get Started Free →Systematically surface hidden assumptions underlying a decision with confidence levels.
.claude/skills/yogsoth-ai-convergence-assumption-extraction/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 47% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 56% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 21% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 28% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 46% | 0% |
Surfaces all hidden assumptions underlying a decision — the unstated beliefs that must be true for the decision to be correct. Each assumption is tagged with a confidence level indicating how certain we are that it holds.
Spawns a subagent that systematically examines the decision and its evidence base to extract implicit assumptions across multiple categories.
Output must include:
<!-- BEGIN available-tables (generated) -->
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. |
<!-- END available-tables (generated) -->
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 14,254 | 16,078 | +13% | 1 | 1 | 0% | 2,261 | 2,901 | +28% | 0 | 0 | — |
case-02 | fail→pass | 13,490 | 17,974 | +33% | 1 | 1 | 0% | 2,076 | 3,052 | +47% | 0 | 0 | — |
case-03 | fail→fail | 17,252 | 13,080 | -24% | 1 | 1 | 0% | 2,652 | 812 | -69% | 0 | 0 | — |
case-04 | pass→fail | 20,117 | 20,352 | +1% | 1 | 1 | 0% | 2,906 | 3,354 | +15% | 0 | 0 | — |
case-05 | pass→fail | 13,320 | 22,117 | +66% | 1 | 1 | 0% | 2,037 | 3,962 | +95% | 0 | 0 | — |
case-06 | pass→fail | 15,095 | 18,736 | +24% | 1 | 1 | 0% | 2,639 | 3,474 | +32% | 0 | 0 | — |
case-07 | fail→fail | 17,938 | 17,340 | -3% | 1 | 1 | 0% | 2,649 | 2,916 | +10% | 0 | 0 | — |
case-08 | fail→pass | 13,143 | 17,187 | +31% | 1 | 1 | 0% | 1,971 | 3,084 | +56% | 0 | 0 | — |
case-09 | fail→fail | 14,423 | 14,767 | +2% | 1 | 1 | 0% | 2,119 | 2,605 | +23% | 0 | 0 | — |
case-10 | fail→pass | 14,005 | 14,271 | +2% | 1 | 1 | 0% | 2,025 | 2,450 | +21% | 0 | 0 | — |
case-11 | fail→pass | 14,463 | 19,294 | +33% | 1 | 1 | 0% | 2,300 | 2,955 | +28% | 0 | 0 | — |
case-12 | fail→fail | 16,547 | 15,647 | -5% | 1 | 1 | 0% | 2,559 | 2,689 | +5% | 0 | 0 | — |
case-13 | fail→fail | 13,535 | 18,484 | +37% | 1 | 1 | 0% | 2,026 | 3,053 | +51% | 0 | 0 | — |
case-14 | fail→pass | 11,614 | 16,060 | +38% | 1 | 1 | 0% | 1,919 | 2,797 | +46% | 0 | 0 | — |
case-15 | fail→pass | 14,624 | 20,114 | +38% | 1 | 1 | 0% | 2,185 | 2,536 | +16% | 0 | 0 | — |
case-16 | fail→fail | 15,828 | 13,932 | -12% | 1 | 1 | 0% | 2,262 | 2,361 | +4% | 0 | 0 | — |
case-17 | fail→pass | 12,961 | 15,655 | +21% | 1 | 1 | 0% | 1,954 | 2,646 | +35% | 0 | 0 | — |
case-18 | fail→fail | 15,118 | 17,108 | +13% | 1 | 1 | 0% | 2,092 | 2,757 | +32% | 0 | 0 | — |
case-19 | fail→pass | 14,293 | 19,678 | +38% | 1 | 1 | 0% | 2,009 | 3,508 | +75% | 0 | 0 | — |
case-20 | fail→fail | 17,826 | 15,295 | -14% | 1 | 1 | 0% | 2,591 | 2,642 | +2% | 0 | 0 | — |
case-21 | fail→pass | 12,829 | 18,663 | +45% | 1 | 1 | 0% | 1,818 | 3,012 | +66% | 0 | 0 | — |
case-22 | fail→pass | 14,785 | 14,948 | +1% | 1 | 1 | 0% | 2,123 | 2,728 | +28% | 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, and 21 counted toward the lift figure. The other 1 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +32 percentage points is the difference between those two pass rates over the 21 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.