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Get Started Free →Multi-path reasoning with evaluation and backtracking - 74% success on complex tasks
.claude/skills/miosa-osa-tree-of-thoughts/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | -30% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 17% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -62% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 29% | 0% |
| case-14 | ✗→✓ | ▲ Improved | -21% | 0% |
Multi-path reasoning that explores, evaluates, and backtracks. Achieved 74% success rate on Game of 24 vs 4% with standard CoT.
Triggers on:
Break problem into intermediate steps that can be evaluated independently.
At each step, generate 3 candidate "thoughts" (reasoning paths):
STEP N:
├─ Thought A: [approach 1]
├─ Thought B: [approach 2]
└─ Thought C: [approach 3]Score each thought (0-10) on:
If path scores drop below threshold (5/10), backtrack to last good node.
Use this simple prompt for ToT without explicit tree construction:
"Imagine three different experts are answering this question.
All experts will write down 1 step of their thinking, then share it with the group.
Then all experts will go on to the next step.
If any expert realizes they're wrong at any point, they leave.
Continue until consensus."Based on Princeton/DeepMind research - arXiv:2305.10601
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 18,613 | 24,137 | +30% | 1 | 1 | 0% | 3,041 | 4,261 | +40% | 0 | 0 | — |
case-02 | pass→pass | 45,994 | 26,527 | -42% | 1 | 1 | 0% | 8,264 | 5,258 | -36% | 0 | 0 | — |
case-03 | fail→fail | 28,802 | 25,522 | -11% | 1 | 1 | 0% | 5,928 | 5,536 | -7% | 0 | 0 | — |
case-04 | fail→pass | 24,168 | 14,143 | -41% | 1 | 1 | 0% | 3,833 | 2,690 | -30% | 0 | 0 | — |
case-05 | pass→pass | 21,123 | 16,091 | -24% | 1 | 1 | 0% | 3,428 | 3,237 | -6% | 0 | 0 | — |
case-06 | pass→fail | 7,733 | 2,753 | -64% | 1 | 1 | 0% | 1,171 | 844 | -28% | 0 | 0 | — |
case-07 | fail→pass | 18,993 | 19,343 | +2% | 1 | 1 | 0% | 3,297 | 3,864 | +17% | 0 | 0 | — |
case-08 | pass→pass | 5,859 | 3,596 | -39% | 1 | 1 | 0% | 913 | 1,004 | +10% | 0 | 0 | — |
case-09 | fail→pass | 10,450 | 1,733 | -83% | 1 | 1 | 0% | 1,812 | 691 | -62% | 0 | 0 | — |
case-10 | pass→pass | 17,896 | 18,961 | +6% | 1 | 1 | 0% | 3,086 | 3,591 | +16% | 0 | 0 | — |
case-11 | pass→pass | 17,590 | 10,263 | -42% | 1 | 1 | 0% | 2,732 | 2,194 | -20% | 0 | 0 | — |
case-12 | pass→pass | 4,709 | 4,138 | -12% | 1 | 1 | 0% | 767 | 1,115 | +45% | 0 | 0 | — |
case-13 | fail→pass | 10,095 | 9,885 | -2% | 1 | 1 | 0% | 1,543 | 1,993 | +29% | 0 | 0 | — |
case-14 | fail→pass | 6,156 | 4,555 | -26% | 1 | 1 | 0% | 940 | 746 | -21% | 0 | 0 | — |
case-15 | pass→pass | 11,633 | 7,270 | -38% | 1 | 1 | 0% | 1,841 | 1,593 | -13% | 0 | 0 | — |
case-16 | pass→pass | 10,888 | 9,948 | -9% | 1 | 1 | 0% | 1,902 | 2,167 | +14% | 0 | 0 | — |
case-17 | pass→pass | 8,782 | 2,273 | -74% | 1 | 1 | 0% | 1,350 | 767 | -43% | 0 | 0 | — |
case-18 | fail→pass | 12,598 | 1,967 | -84% | 1 | 1 | 0% | 1,850 | 750 | -59% | 0 | 0 | — |
case-19 | fail→pass | 14,621 | 1,925 | -87% | 1 | 1 | 0% | 2,115 | 749 | -65% | 0 | 0 | — |
case-20 | pass→pass | 4,685 | 4,282 | -9% | 1 | 1 | 0% | 819 | 1,157 | +41% | 0 | 0 | — |
case-21 | pass→pass | 2,969 | 3,651 | +23% | 1 | 1 | 0% | 377 | 981 | +160% | 0 | 0 | — |
case-22 | pass→pass | 4,908 | 5,731 | +17% | 1 | 1 | 0% | 946 | 1,496 | +58% | 0 | 0 | — |
case-23 | pass→pass | 4,901 | 2,152 | -56% | 1 | 1 | 0% | 835 | 727 | -13% | 0 | 0 | — |
case-24 | pass→pass | 7,615 | 2,785 | -63% | 1 | 1 | 0% | 1,423 | 843 | -41% | 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. 24 cases were attempted. The headline lift of +25 percentage points is the difference between those two pass rates over the 24 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.