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Get Started Free →KAOS-style recursive goal decomposition. AND decomposition for sub-goals that must ALL be satisfied. OR decomposition for alternative paths where any one suffices. Produces a GoalTree (DAG structure).
.claude/skills/yogsoth-ai-and-or-decompose/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | -20% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -55% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -32% | 0% |
| case-12 | ✗→✓ | ▲ Improved | -52% | 0% |
| case-16 | ✗→✓ | ▲ Improved | -50% | 0% |
Recursively decompose the top goal into a structured GoalTree.
Subagent — spawned via subagent-spawning/spawn-agent skill.
GoalTree — DAG with AND/OR nodes, leaf nodes representing actionable sub-goals.
<!-- 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-14 | fail→fail | 13,097 | 2,002 | -85% | 1 | 1 | 0% | 1,967 | 504 | -74% | 0 | 0 | — |
case-01 | fail→fail | 28,669 | 8,133 | -72% | 1 | 1 | 0% | 4,632 | 1,311 | -72% | 0 | 0 | — |
case-15 | pass→pass | 12,564 | 2,815 | -78% | 1 | 1 | 0% | 1,955 | 541 | -72% | 0 | 0 | — |
case-02 | fail→fail | 11,127 | 12,225 | +10% | 1 | 1 | 0% | 1,597 | 1,970 | +23% | 0 | 0 | — |
case-03 | pass→pass | 13,724 | 5,380 | -61% | 1 | 1 | 0% | 1,979 | 997 | -50% | 0 | 0 | — |
case-04 | fail→pass | 13,558 | 8,845 | -35% | 1 | 1 | 0% | 1,967 | 1,567 | -20% | 0 | 0 | — |
case-05 | fail→pass | 14,372 | 5,249 | -63% | 1 | 1 | 0% | 2,017 | 907 | -55% | 0 | 0 | — |
case-06 | pass→pass | 15,654 | 8,799 | -44% | 1 | 1 | 0% | 2,333 | 1,552 | -33% | 0 | 0 | — |
case-07 | fail→fail | 15,696 | 3,599 | -77% | 1 | 1 | 0% | 2,245 | 687 | -69% | 0 | 0 | — |
case-08 | pass→pass | 13,903 | 5,945 | -57% | 1 | 1 | 0% | 1,834 | 968 | -47% | 0 | 0 | — |
case-09 | pass→pass | 12,085 | 4,551 | -62% | 1 | 1 | 0% | 1,882 | 766 | -59% | 0 | 0 | — |
case-10 | fail→pass | 17,325 | 10,467 | -40% | 1 | 1 | 0% | 2,657 | 1,795 | -32% | 0 | 0 | — |
case-11 | pass→pass | 11,918 | 3,437 | -71% | 1 | 1 | 0% | 1,715 | 628 | -63% | 0 | 0 | — |
case-12 | fail→pass | 11,930 | 4,780 | -60% | 1 | 1 | 0% | 1,670 | 796 | -52% | 0 | 0 | — |
case-13 | pass→pass | 12,984 | 5,581 | -57% | 1 | 1 | 0% | 1,805 | 963 | -47% | 0 | 0 | — |
case-16 | fail→pass | 15,692 | 6,940 | -56% | 1 | 1 | 0% | 2,680 | 1,347 | -50% | 0 | 0 | — |
case-17 | pass→pass | 17,345 | 7,328 | -58% | 1 | 1 | 0% | 2,833 | 1,322 | -53% | 0 | 0 | — |
case-18 | fail→pass | 6,524 | 3,017 | -54% | 1 | 1 | 0% | 925 | 639 | -31% | 0 | 0 | — |
case-19 | pass→pass | 16,875 | 6,738 | -60% | 1 | 1 | 0% | 2,511 | 1,179 | -53% | 0 | 0 | — |
case-20 | fail→fail | 11,749 | 12,047 | +3% | 1 | 1 | 0% | 2,015 | 2,275 | +13% | 0 | 0 | — |
case-21 | fail→fail | 5,269 | 7,175 | +36% | 1 | 1 | 0% | 791 | 1,231 | +56% | 0 | 0 | — |
case-22 | pass→pass | 14,722 | 24,768 | +68% | 1 | 1 | 0% | 2,089 | 3,954 | +89% | 0 | 0 | — |
case-23 | pass→fail | 12,384 | 6,687 | -46% | 1 | 1 | 0% | 2,089 | 700 | -66% | 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. 23 cases were attempted. The headline lift of +22 percentage points is the difference between those two pass rates over the 23 comparable cases. 2 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.