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Multi-path reasoning with evaluation and backtracking - 74% success on complex tasks
Passed 21 of 24 test cases with the skill, and 15 of 24 without it.
and 4 more in the full report
7 fixed14 passed either way2 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
In the zero-shot expert consensus prompt structure, what action does an expert take upon discovering a mistake in their step?
| Checked | Without skill | With skill |
|---|---|---|
| Specifies that an expert leaves when realizing they are wrong | Missed | Passed |
The instructions your model reads when the skill loads.
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.
test cases passed, vs 15 / 24 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.