What is the 'Trust but Verify' principle in this context?

It means the AI will not take a user's statement as fact immediately. It will first check the codebase, run tests, or consult documentation to confirm the statement is true before proceeding with a fix or implementation.

Why would I want the AI to disagree with me?

Blind agreement can lead to implementing incorrect solutions or overlooking hidden bugs. Honest feedback ensures your software is technically sound and follows best practices rather than just following a flawed suggestion.

Does this skill make the AI rude or unhelpful?

Not at all. The skill focuses on professional objectivity and direct communication. It uses evidence and technical reasoning to explain why an approach might fail, which is ultimately more helpful than polite but incorrect validation.

How does this help with debugging?

In debugging, it's common to assume the wrong component is at fault. This skill forces the AI to look at stack traces and logs objectively, often correcting the developer's initial diagnosis and saving time on 'false-positive' fixes.

Professional Honesty

Name: Professional Honesty
Author: TheBushidoCollective

byTheBushidoCollective

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Herramientas para Desarrolladores

Prioritizes technical accuracy and objective evidence over agreeableness to ensure high-integrity software development.

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The Professional Honesty skill acts as a rigorous technical filter for Claude, ensuring it prioritizes factual truth and verification over blind validation of user assumptions. By adhering to a 'trust but verify' philosophy, this skill prevents the AI from becoming a 'yes-man,' helping developers identify real root causes of bugs and avoid suboptimal architectural patterns. It is an essential tool for high-stakes engineering environments where directness and technical accuracy are more valuable than social validation, leading to more robust and reliable codebases.

Características Principales

Constructively challenges technical assumptions and hypotheses
Mandates verification of code, logs, and documentation before confirmation
Prioritizes evidence-based reasoning over user validation
Identifies potential critical failures like data corruption or race conditions
Replaces excessive agreeableness with direct, actionable feedback
39 GitHub stars

Casos de Uso

Validation of technical claims against project documentation
Root cause analysis during complex debugging sessions
Critical review of proposed software architectures and patterns

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Características Principales

Constructively challenges technical assumptions and hypotheses
Mandates verification of code, logs, and documentation before confirmation
Prioritizes evidence-based reasoning over user validation
Identifies potential critical failures like data corruption or race conditions
Replaces excessive agreeableness with direct, actionable feedback
39 GitHub stars

Casos de Uso

Validation of technical claims against project documentation
Root cause analysis during complex debugging sessions
Critical review of proposed software architectures and patterns