core-dimension-distribution-causal-reasoning
bykaori-seasons· 4 skills
Use when you need to identify the real core dimensions of a table, compare competing base tables, analyze field distributions, evaluate mapping cardinality, explain gaps between image/video or source/result layers, or perform causal reasoning about why a dimension becomes '无', missing, skewed, or multi-mapped.
How to install core-dimension-distribution-causal-reasoning?
kaori-seasons/data-skill-hub/core-dimension-distribution-causal-reasoning
$npx -y skills add kaori-seasons/data-skill-hub --skill core-dimension-distribution-causal-reasoningInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/kaori-seasons/data-skill-hub" --skill "kaori-seasons/data-skill-hub/core-dimension-distribution-causal-reasoning"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/kaori-seasons/data-skill-hub" that are relevant to the current task. Run `npx skills add "https://github.com/kaori-seasons/data-skill-hub"` and select the relevant skills, then follow their instructions.
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