What happens if confidence is below 80%?

The skill instructs the AI to look for more evidence independently. If it can't increase confidence through code searches or tests, it must clearly state what data is missing to reach a higher level of certainty.

What is a falsifiability check in this context?

It is a requirement for the AI to define what specific data would prove its current theory wrong and to actively check for that data before presenting its findings to the user.

Why use percentage-based confidence instead of descriptive words?

Percentages provide a precise, calibrated scale that eliminates ambiguity. It forces the AI to quantify its level of certainty, preventing users from being misled by highly polished but speculative explanations.

How does this prevent AI hallucinations during debugging?

By requiring an 'Evidence Inventory' and 'Assumption Audit,' the AI must distinguish between hard data (logs, code) and unverified assumptions, effectively grounding its conclusions in reality.

Confidence Levels

Name: Confidence Levels
Author: jagreehal

byjagreehal

Herramientas para Desarrolladores

Enforces honest, percentage-based confidence assessments and evidence-backed validation to prevent AI overconfidence during technical analysis.

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The Confidence Levels skill transforms how Claude presents findings by replacing vague certainty with quantifiable, calibrated assessments. It mandates a rigorous pre-conclusion checkpoint including evidence inventory, assumption audits, and falsifiability checks. By requiring Claude to 'show its math' and explain exactly why a conclusion isn't 100% certain, this skill helps developers avoid following incorrect leads during complex debugging or system architecture tasks. It ensures that the quality of evidence drives the conclusion, rather than the quality of the explanation.

Características Principales

Mandatory 'Why not 100%?' gap analysis for all findings
Structured evidence inventory to score and weight supporting data
Quantifiable percentage-based confidence scoring (0-100%)
Integrated falsifiability checks to rule out alternative theories
Automated self-validation prompts to gather more data before concluding
0 GitHub stars

Casos de Uso

Root cause analysis for complex system failures and production bugs
Validating code refactoring impacts when direct evidence is limited
Evaluating architectural trade-offs where multiple solutions are plausible

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Características Principales

Mandatory 'Why not 100%?' gap analysis for all findings
Structured evidence inventory to score and weight supporting data
Quantifiable percentage-based confidence scoring (0-100%)
Integrated falsifiability checks to rule out alternative theories
Automated self-validation prompts to gather more data before concluding
0 GitHub stars

Casos de Uso

Root cause analysis for complex system failures and production bugs
Validating code refactoring impacts when direct evidence is limited
Evaluating architectural trade-offs where multiple solutions are plausible