Dr Claude Code Plugins Re

Test API field compatibility and performance. Discovers which fields work with aggregation and suggests sibling alternatives for failed fields, then reports the results. Self-contained — discovers the client's financials table and candidate fields on its own, no profile or setup step required.

Generate an audit-support evidence package over FinanceOS data - completeness, reconciliation, mapping-integrity, and substantive-sample checks with a PDF report and Excel evidence workbook. Not a SOX certification - access-control, change-management, and IT-general-control evidence is out of scope.

Query Datarails Finance OS tables with filters. Fetch specific records or page through rows for investigation. The filter API supports value-list AND advanced operators — comparisons, ranges, text matching, null checks, and date ranges all work.

List and explore Datarails Finance OS tables — discover what data is available and view one table's SCHEMA (fields, types, distinct values of a field) to understand its structure. For per-field STATISTICS (ranges, percentiles, null rates, cardinality) use the profile skill. Works with or without an open workbook — in Excel, "list my Datarails tables/models/fields" still routes HERE via the MCP connector (the Excel bridge's agent.list_functions lists workbook widgets, not org tables).

Extract validated financial data from Datarails Finance OS to Excel — a RAW FULL-YEAR export — a 4-sheet workbook (P&L, Balance Sheet, KPIs including SaaS metrics where sourceable from the org's data, and validation checks); no analysis or narrative. For an analyzed workbook use intelligence; for an executive deck use insights. Self-contained — discovers the client's tables and fields on its own, no profile or setup step required.

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.

Executive-ready insights with trend analysis and visualizations — a FULL-FISCAL-YEAR narrative deck — multi-slide PowerPoint presentation plus a supporting Excel data book. For a one-month KPI snapshot use dashboard; for a 10-sheet analysis workbook (no PowerPoint) use intelligence.

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.

Executive KPI dashboard — a ONE-MONTH metrics snapshot for the latest closed month (not real-time; the in-progress month is excluded) — Excel dashboard + single-slide PowerPoint one-pager. For a full-fiscal-year narrative deck use insights; for a year-long analysis workbook use intelligence; for a raw data export use extract.

Drill down on a cell in the Datarails Excel Add-in to see underlying detail — also the skill to use when the user asks to "explain the variance", "explain this number", "what's driving this", "what's behind this cell", or break a figure into its line items. In a live Excel context (add-in agent bridge available) DR formula cells drill through the add-in's own drill-down; with an .xlsx file (Claude Code) it resolves DR.GET formulas, reads hidden "dr control" filters, queries Datarails, and validates totals; with no workbook at all, the user can paste a DR.GET formula or give structured filters (no-file mode). Self-contained — discovers the client's financials table and fields on its own, no profile or setup step required.

Analyze P&L and performance by department. Creates departmental reports and comparative analysis with Excel and PowerPoint outputs.

Generate Excel workbooks with DR.GET formulas that pull live financial data from Datarails. Creates P&L templates, budget models, and variance reports with validated dimension values. Self-contained — discovers the client's financials table and fields on its own, no profile or setup step required.

Comprehensive FP&A financial-analysis intelligence workbook for a FULL FISCAL YEAR — Excel, up to 10 sheets, with ranked auto-detected insights, findings and recommendations, and professional formatting; no PowerPoint. For a one-month KPI snapshot use dashboard; for an executive deck use insights. Self-contained — discovers the client's tables and fields on its own, no profile or setup step required.

Whole-period consistency WORKBOOK - run independent-source checks across Finance OS data (cross-endpoint agreement, balance-sheet identity, cross-grain roll-ups, scenario/period integrity). Validates the data pipeline and mappings, not source systems. NOT a per-metric layer comparison - that is the dev-only cross-layer-reconcile harness.

Revenue trends, growth rates, and composition from real aggregated monthly data. Self-contained — discovers the client's financials table and fields itself, no profile or setup step required.

Detect data anomalies and generate a comprehensive data-quality Excel WORKBOOK from Finance OS tables, computed over the table's ALL-TIME history (use the anomalies skill for a chat-only answer scoped to the latest fiscal year — the two baselines differ by design, so counts won't match). The MCP tools return baseline aggregates only; this skill derives findings, severity buckets, and the Data Quality Score client-side, then writes a multi-sheet workbook. Self-contained — pass --table-id to target a table directly, or it discovers the financials table on its own; no profile or setup step required.

Top expense categories, monthly expense trends, and concentration analysis from real aggregated data. Self-contained — discovers the client's financials table and fields itself, no profile or setup step required.

Quick snapshot of revenue, expenses, gross profit, and margin from real aggregated totals. Self-contained — discovers the client's financials table and fields on its own, no profile or setup step required.

Analyze budget vs forecast vs actual variances. Compares multi-scenario financial data for planning and performance review.