balls-mode

Decompose complex problems into scored sub-questions and synthesize a final confidence-rated answer.

1|Updated Dec 26, 2025
One-click install
npx skills add https://github.com/DammianMiller/universal-agent-memory --skill balls-mode
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: balls-mode
Source: https://github.com/DammianMiller/universal-agent-memory/tree/main/.factory/skills/balls-mode
Command: npx skills add https://github.com/DammianMiller/universal-agent-memory --skill balls-mode

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps break down complex problems into smaller, verifiable reasoning units, allowing for explicit confidence scoring at each step to prevent wasted effort on uncertain assumptions.

Core Features & Use Cases

  • Decomposition: Breaks down complex questions into independent "balls" or sub-questions.
  • Confidence Scoring: Assigns a numerical confidence level (0.0-1.0) to the answer of each ball.
  • Synthesis: Integrates ball answers to provide a final, reasoned conclusion with an overall confidence score.
  • Use Case: Before implementing a new architectural feature, use balls-mode to decompose the decision into sub-questions like "Is this approach secure?", "Does it scale?", "Are there existing libraries?", and score the confidence for each, identifying the weakest link before coding begins.

Quick Start

Use balls-mode to analyze the feasibility of implementing a new caching strategy for the user authentication service.

Frequently Asked Questions about balls-mode

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

FAQPage Schema
How do I evaluate architectural decisions with confidence scoring?

You can evaluate architectural decisions by decomposing them into independent sub-questions, assigning a 0.0-1.0 confidence score to each, and synthesizing the findings into a final conclusion with an overall confidence score.

What is the best way to debug complex system failures systematically?

Systematic debugging of complex failures is achieved by breaking the problem into verifiable reasoning units, evaluating each sub-question's uncertainty, and identifying the weakest assumptions before synthesizing a final answer.

How does decomposed reasoning improve pre-implementation analysis?

Decomposed reasoning improves pre-implementation analysis by breaking down complex questions into independent balls, scoring the confidence of each answer, and identifying the weakest link to prevent wasted effort on uncertain assumptions.

Can I identify weak assumptions in a software architecture before coding begins?

Yes, you can identify weak assumptions by systematically evaluating sub-questions about your architecture, assigning explicit confidence levels to each, and highlighting the lowest scoring areas before implementation starts.

How do I break down complex problems into verifiable reasoning units?

Complex problems are broken down by decomposing them into independent sub-questions or balls, analyzing each one separately with a numerical confidence score, and then integrating the answers into a reasoned conclusion.

When should I use confidence scoring for decision making?

Confidence scoring for decision making should be used when evaluating complex architectural choices or post-failure debugging, ensuring systematic analysis by identifying weakest assumptions and synthesizing findings with an overall score.