Diagnose hardware performance shortfalls with measurable problem definition, first-principles models, equation-led decomposition, 5M1E coverage, multidisciplinary review and evidence-ranked causal trees. Use for root-cause analysis, causal diagrams, out-of-spec parameters, 5 Whys, engineering contradictions or hardware troubleshooting.
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sha256:fba75343970d444779a16135df712c9d076ee88e3bde3b8090d7c143a7a4dcabscripts/ceae_tree_export.pyskill-card.mdSKILL.mdInitial release – provides a robust, structured workflow for diagnosing hardware performance shortfalls. - Transforms vague hardware issues into testable causal models, hypotheses, and experiment plans. - Emphasizes multidisciplinary review (mechanical, materials, electrical, manufacturing, measurement/environment). - Guides detailed root-cause decomposition: measurable problem definition, first-principles modeling, 5M1E broadening, causal trees. - Classifies evidence at every branch; marks causes as observed, calculated, tested, contradicted or untested. - Highlights engineering contradictions and valid endpoints; encourages design of validation experiments. - Cautions about use in safety-critical and regulatory contexts—always escalate to qualified engineers where appropriate.