How does the skill calculate a confidence percentage?

It uses a standardized scoring system that starts at 50% and adjusts based on direct evidence, verified assumptions, and whether alternative possibilities have been ruled out.

What is the Confidence Honesty skill for Claude Code?

Confidence Honesty is a capability that forces Claude to provide a specific percentage score for its conclusions, preventing it from presenting speculation as absolute certainty.

Can this skill reduce debugging time?

Yes, by preventing developers from chasing 'false positive' root causes that the AI is actually unsure about, it focuses effort on validated evidence.

Why is 'Why not 100%?' required?

This requirement ensures that Claude identifies and communicates the specific risks, missing data, or unverified assumptions that could invalidate its current theory.

Confidence Honesty

Name: Confidence Honesty
Author: NTCoding

byNTCoding

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생산성 및 워크플로

Mandates explicit percentage-based confidence assessments and evidence validation before Claude presents troubleshooting conclusions or root cause analyses.

소개

Confidence Honesty is a specialized Claude Code skill designed to eliminate premature certainty during complex technical investigations. By forcing Claude to calculate a confidence score based on hard evidence rather than reasoning quality alone, it ensures that users aren't misled by well-structured but unverified hypotheses. The skill requires Claude to perform a formal evidence inventory, conduct falsifiability checks, and explicitly explain gaps in its knowledge, making it an essential tool for high-stakes debugging, system analysis, and architectural decision-making where accuracy is paramount.

주요 기능

Percentage-based confidence scoring for all conclusive claims
Mandatory 'Why not 100%?' gap analysis for any confidence level below 95%
Self-validation rules that force Claude to verify assumptions through codebase searches before reporting
Structured response format with visual status icons (Red/Yellow/Green) for quick certainty assessment
78 GitHub stars
Evidence-based scoring adjustments that distinguish between direct logs and circumstantial patterns

사용 사례

Validating technical assumptions before communicating status updates to stakeholders
Performing security audits or identifying vulnerabilities in complex logic
Investigating production outages where multiple potential root causes exist

소개

주요 기능

Percentage-based confidence scoring for all conclusive claims
Mandatory 'Why not 100%?' gap analysis for any confidence level below 95%
Self-validation rules that force Claude to verify assumptions through codebase searches before reporting
Structured response format with visual status icons (Red/Yellow/Green) for quick certainty assessment
78 GitHub stars
Evidence-based scoring adjustments that distinguish between direct logs and circumstantial patterns

사용 사례

Validating technical assumptions before communicating status updates to stakeholders
Performing security audits or identifying vulnerabilities in complex logic
Investigating production outages where multiple potential root causes exist