dr-profile
Profile Datarails Finance OS table fields — how is a field distributed? Per-field statistics over the table's whole history — ranges, approximate percentiles, null rates, cardinality interpretation, and range-outlier flags — no severity ranking, no period scope. The MCP tools return baseline aggregates (SUM/AVG/MIN/MAX/COUNT for numeric, distinct-value samples for categorical); this skill derives the statistics client-side. For severity-ranked data-quality findings scoped to the latest fiscal year, use the anomalies skill.
Install
With your agent
Install OMGSkills skill "datarails/dr-claude-code-plugins-re:skills/profile" into this agent's normal skills directory: call the OMGSkills MCP get_install_plan tool for your agent and run the script it returns (it installs only the pinned commit and verifies SKILL.md), and stop if MCP or the pin is unavailable. Do not overwrite or change existing skills without asking me.
Manual command
git clone https://github.com/Datarails/dr-claude-code-plugins-re /tmp/dr-claude-code-plugins-re && ln -s /tmp/dr-claude-code-plugins-re/skills/profile ~/.claude/skills/dr-profileFrom README
Datarails Table Profiling Field-level statistics for Finance OS tables. The MCP profile tools are thin — they return only the basic aggregates listed below. Everything richer (percentiles, std dev, outlier flags, null rates, cardinality interpretation) is computed by this skill, not the tool. State the provenance of every number you present.
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