When analyzing tabular data, always: 1. Validate the shape of the input before computing statistics (check for empty data, all-null columns). 2. Distinguish numeric columns from categorical ones — apply appropriate stats to each type. 3. Report count alongside mean/min/max so the reader can assess sample size. 4. Flag outliers when max > 3× mean for any numeric column. 5. Return a structured dict, not free text, so downstream agents can reliably parse the output.
Scanned 9/3/2026
Install to Claude Code
Skills Directorynpx -y skills add CorvinLabs/CorvinOS --skill data_analysis --agent claude-codeInstalls into .claude/skills of the current project.
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# data_analysis
When analyzing tabular data, always:
1. Validate the shape of the input before computing statistics (check for empty data, all-null columns).
2. Distinguish numeric columns from categorical ones — apply appropriate stats to each type.
3. Report count alongside mean/min/max so the reader can assess sample size.
4. Flag outliers when max > 3× mean for any numeric column.
5. Return a structured dict, not free text, so downstream agents can reliably parse the output.
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