Builds deterministic reproduction loops and structured hypothesis tests to identify root causes of bugs.
npx skills add https://github.com/kenlck/skills --skill diagnose-kenlck
Please help me install this Agent Skill. Skill: diagnose Source: https://github.com/kenlck/skills/tree/main/ken-swe/skills/diagnose Command: npx skills add https://github.com/kenlck/skills --skill diagnose-kenlck
Disciplined diagnosis for hard bugs and performance regressions, turning chaotic symptoms into repeatable, testable steps.
Start by constructing a fast, deterministic feedback loop to reproduce the bug and guide hypothesis-driven debugging.
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Disciplined debugging resolves hard bugs by building a deterministic reproduction loop and applying structured hypothesis testing with verifiable instrumentation. This phase-driven workflow transforms chaotic symptoms into repeatable, testable steps to isolate the root cause.
To build a deterministic feedback loop for bug reproduction, construct reproducible fixtures and use logs to guide the investigation. This creates a fast, repeatable cycle that validates fixes and ensures the bug is consistently reproduced before applying hypotheses.
Hypothesis-driven debugging applies across unit, integration, and end-to-end scopes. By leveraging domain glossaries and architecture decision records, it maintains a clear mental model of the system while investigating regressions across different testing layers.
The structured debugging workflow follows five phases: reproduce, hypothesize, instrument, fix, and regression-test. Safe guardrails are applied throughout the process to ensure reliable diagnosis and verifiable fixes for complex performance regressions and bugs.
Instrumentation strategies are needed when diagnosing performance regressions that lack clear symptoms. By applying targeted instrumentation within a defined feedback loop, you capture the necessary logs and data to validate hypotheses and guide the investigation toward the root cause.
Safe guardrails are required when fixing hard bugs to ensure verifiable fixes and prevent new regressions. They enforce a disciplined phase-driven workflow where the original bug reproduction loop validates that the applied fix actually resolves the targeted issue.
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