Diagnose empirical results from CSV logs and CI outputs to identify patterns and root causes.
npx skills add https://github.com/smanist/a-exp --skill diagnose-smanist
Please help me install this Agent Skill. Skill: diagnose Source: https://github.com/smanist/a-exp/tree/main/.agents/skills/diagnose Command: npx skills add https://github.com/smanist/a-exp --skill diagnose-smanist
Diagnoses empirical results to identify patterns, root causes, and interpretation challenges, enabling teams to understand what the data really means.
Run a diagnostic pass on your results set to produce an actionable diagnosis.
High-intent search queries and answers about installing and using this skill.
You can diagnose root causes in experiment results and CSV logs by running a structured workflow that formulates testable hypotheses, evaluates statistical validity, and identifies systematic error patterns to clarify what the empirical data means.
The best way to interpret empirical results from product experiments is by assessing construct, statistical, external, and ground-truth validity to identify patterns and generate recommended actions for subsequent testing steps.
Yes, you can analyze CI outputs and performance metrics by diagnosing empirical results to identify systematic error patterns, formulate root-cause hypotheses, and generate recommended next steps across validation pipelines.
You assess statistical validity when diagnosing data interpretation issues by evaluating construct, external, and ground-truth validity to identify systematic errors and formulate testable root-cause hypotheses.
Diagnosing empirical results requires no specific dependencies and accepts CSV logs, experiment results, performance metrics, and CI outputs across studies, product experiments, and validation pipelines.
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