Identify and resolve incidents with a structured debugging workflow.
npx skills add https://github.com/thomassloboda/ai-stuff --skill incident-debugging
Please help me install this Agent Skill. Skill: incident-debugging Source: https://github.com/thomassloboda/ai-stuff/tree/main/skills/incident-debugging Command: npx skills add https://github.com/thomassloboda/ai-stuff --skill incident-debugging
Incidents often lack a structured approach, leading to long outages, repeated symptoms, and unclear root causes. This skill provides a disciplined framework to collect symptoms, reproduce where possible, isolate faults, distinguish facts from hypotheses, validate fixes, and guard against regressions.
Capture the observed symptom precisely and reproduce it when possible to begin the debugging workflow
High-intent search queries and answers about installing and using this skill.
Debug a production incident by following a structured workflow: collect observed symptoms precisely, reproduce the issue, isolate faults, distinguish facts from hypotheses, validate fixes, and run regression checks for verifiable stability.
The best way to find a root cause is through fault isolation, which involves disciplined observation and separating verified facts from unverified hypotheses to pinpoint the exact trigger of a runtime error before applying a fix.
Reproduce a flaky test by capturing the exact observed symptom and environmental conditions precisely, then attempting consistent reproduction to confirm the fault isolation before proceeding with regression testing.
Yes, a structured debugging workflow applies to unexpected regressions across services by guiding you through observation, reproduction, root-cause analysis, and regression checks to ensure verifiable production stability.
Separating facts from hypotheses during root-cause analysis prevents false assumptions, ensuring that fault isolation targets the actual trigger rather than speculative symptoms, leading to a safe and verifiable fix.
Without regression testing, debugging incidents lacks verifiable stability, increasing the risk of repeated symptoms and unclear root causes reoccurring across services after an initial fix is applied.
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