What are confidence ceilings in this context?

They are probabilistic caps assigned to diagnoses and fixes (e.g., 0.70 for inferences) to ensure the AI remains realistic about its certainty and avoids overconfidence.

Is this skill safe for production environments?

It is designed with safety as a priority, incorporating mandatory rollback paths, blast radius analysis, and audit logs to preserve system integrity.

When should I use this skill instead of a regular fix command?

Use it for complex defects, performance regressions, or security issues where the cause is unclear and the risk of collateral damage to the system is high.

Does it include automated testing?

Yes, the protocol requires creating a minimal reproduction, adding regression tests, and performing comprehensive validation checks before finalizing any fix.

How does Smart Bug Fix differ from standard debugging?

Unlike simple debugging, this skill uses a structured, evidence-based approach including hypothesis discipline, confidence ceilings, and strict validation to handle high-risk production issues.

Smart Bug Fix

Name: Smart Bug Fix
Author: DNYoussef

byDNYoussef

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Seguridad y Pruebas

Eradicates complex software defects using hypothesis-driven root cause analysis and comprehensive validation protocols.

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Smart Bug Fix is an advanced debugging skill for Claude Code designed to handle high-risk production defects and complex system failures. It employs a rigorous, evidence-based methodology including hypothesis discipline with confidence ceilings, 5-Whys analysis, and blast radius assessments. By enforcing a structure-first approach with dedicated testing and resource management, it ensures fixes are not only effective but also guarded against regressions, making it ideal for critical maintenance in production-grade environments where uptime and data safety are paramount.

Características Principales

Hypothesis-driven root cause analysis with evidence-backed confidence ceilings
Integrated rollback planning and post-fix monitoring for production safety
Systematic 5-Whys interrogation to identify underlying architectural flaws
9 GitHub stars
Comprehensive regression protection with mandatory unit and integration testing
Automated constraint extraction (Hard/Soft/Inferred) to prevent side effects

Casos de Uso

Fixing regressions in complex multi-service architectures with strict uptime targets
Resolving high-priority production defects where the root cause is non-obvious
Addressing performance degradations and security vulnerabilities in legacy systems

Acerca de

Características Principales

Hypothesis-driven root cause analysis with evidence-backed confidence ceilings
Integrated rollback planning and post-fix monitoring for production safety
Systematic 5-Whys interrogation to identify underlying architectural flaws
9 GitHub stars
Comprehensive regression protection with mandatory unit and integration testing
Automated constraint extraction (Hard/Soft/Inferred) to prevent side effects

Casos de Uso

Fixing regressions in complex multi-service architectures with strict uptime targets
Resolving high-priority production defects where the root cause is non-obvious
Addressing performance degradations and security vulnerabilities in legacy systems