Build MECE logic trees and falsifiable If/Then/Because hypotheses to diagnose metric gaps.
npx skills add https://github.com/hungv47/meta-skills --skill diagnose-hungv47
Please help me install this Agent Skill. Skill: diagnose Source: https://github.com/hungv47/meta-skills/tree/main/skills/research/diagnose Command: npx skills add https://github.com/hungv47/meta-skills --skill diagnose-hungv47
Diagnose identifies the root cause of a metric gap by constructing a MECE logic tree and testable If/Then/Because hypotheses, ensuring outputs are falsifiable and auditable.
Describe the metric gap and target, and I will build the MECE tree and hypotheses.
High-intent search queries and answers about installing and using this skill.
To diagnose a metric gap, you build a 2-3 level MECE logic tree with mutually exclusive branches, formulate testable If/Then/Because hypotheses, and validate them with discriminating data to find the root cause.
The best way to formulate falsifiable hypotheses for root-cause analysis is using the If/Then/Because framework, which ensures your assumptions are testable, auditable, and mapped to specific data requirements for validation.
You structure root-cause analysis findings by validating hypotheses with a final verdict and organizing outputs into a canonical artifact at docs/forsvn/canonical/research/DIAGNOSE.md for downstream prioritization.
Yes, you can use this MECE tree approach for any metric gap analysis by simply describing the metric gap and your target, which triggers the generation of testable tree branches and ranked hypotheses.
A MECE logic tree ensures branches are mutually exclusive and collectively exhaustive, preventing overlapping causes and ensuring comprehensive root-cause diagnosis, unlike unstructured brainstorming which can miss key factors.
You need discriminating data that uniquely supports or refutes specific hypotheses to validate them during root-cause analysis, ensuring the final verdict is based on falsifiable evidence rather than assumptions.
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