dr-anomalies
Detect data anomalies in one Datarails Finance OS table and answer IN CHAT — severity-ranked outliers, duplicates, missing/null rates, rare values — scoped to the latest complete fiscal year. Writes no file (use the anomalies-report skill for an Excel workbook; use the profile skill for unscoped whole-history statistics). The MCP profiling and aggregation tools return baseline aggregates only (raw rows come from the separate get_data_by_* calls); this skill computes the findings client-side.
Install
With your agent
Install OMGSkills skill "Datarails/dr-claude-code-plugins-re:skills/anomalies" 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/anomalies ~/.claude/skills/dr-anomaliesFrom README
Datarails Anomaly Detection Find data quality issues in Finance OS tables. The MCP server's profile tools are deliberately thin — they return baseline aggregates and nothing more. This skill computes the actual findings (outliers, severity, duplicate counts, null rates) by post-processing those aggregates. Be explicit about which numbers came from the tool versus which the skill derived.
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