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self-consistency

Generate multiple reasoning paths and aggregate final answers by majority voting.

Updated Apr 29, 2026
Search Tags:#chain-of-thought#reasoning#calibration#self-consistency#majority-vote#multi-paths
One-click install
npx skills add https://github.com/MantisWare/BizForge --skill self-consistency-mantisware
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: self-consistency
Source: https://github.com/MantisWare/BizForge/tree/main/library/skills/ai-patterns/self-consistency
Command: npx skills add https://github.com/MantisWare/BizForge --skill self-consistency-mantisware

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Reduces errors in reasoning by exploring multiple possible paths and selecting the most consistent answer.

Core Features & Use Cases

  • Multi-path reasoning: generates several reasoning traces for a given problem.
  • Majority-vote final answer: selects the most frequent final result among traces.
  • Improved accuracy on structured tasks: proven improvements on math and logic benchmarks.

Quick Start

Ask the AI to generate 5 diverse reasoning paths and select the most frequent result as the final answer.

Frequently Asked Questions about self-consistency

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is self-consistency reasoning and how does it improve LLM accuracy?▼

Self-consistency improves accuracy by generating multiple diverse reasoning paths for a single prompt and selecting the most frequent final answer through majority voting to reduce errors.

How do I apply multi-path reasoning to math problems and logic puzzles?▼

Generate several distinct chain-of-thought traces for the math or logic prompt, extract a final answer from each path, and aggregate the results using majority voting to find the most consistent solution.

When should I use majority voting instead of single-path reasoning for factual questions?▼

Use majority voting for factual questions when single-path reasoning proves unreliable, as aggregating multiple reasoning traces helps calibrate confidence and select the most consistent final answer.

How does chain-of-thought calibration work with multiple reasoning paths?▼

Chain-of-thought calibration works by generating multiple reasoning paths, extracting a final answer from each trace, and returning a report of path confidences alongside the majority-vote result.

What are the limitations of using majority voting for complex reasoning tasks?▼

A limitation of majority voting for complex reasoning is that it requires generating multiple traces, which increases processing time, and assumes the most frequent answer among paths is inherently the correct one.

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