dr-anomalies-report

# Anomaly Detection Report Generate comprehensive data quality assessment report with automated anomaly detection. This skill automatically discovers your data structure and detects issues without requiring pre-configuration. Works with any Datarails Finance OS table. ## Design Principles **General-Purpose**: - ✅ No hardcoded table IDs or field names - ✅ Adapts to any client structure - ✅ Works with and without client profiles - ✅ Falls back to discovery mode if profile missing ## Arguments | Argument | Description | Default | |----------|-------------|---------| | `--table-id <id>` | Specific table to analyze | Uses profile or discovers automatically | | `--severity <level>` | Filter results: critical, high, medium, low | All | | `--output <file>` | Output filename | `tmp/Anomaly_Report_TIMESTAMP.xlsx` | ## What It Reports ### Summary Sheet - **Data Quality Score** (0-100) - Health status indicator - Anomaly count by severity - Key metrics ### Critical Findings Sheet - Anomalies requiring immediate attention - Sample records for investigation - Field-specific details - Recommended actions ### High Priority Sheet - Issues to address this week - Full descriptions - Count and context ### Analysis Sheets - **Numeric Analysis**: Min, max, mean, std dev for numeric fields - **Categorical Analysis**: Distinct values, cardinality, frequency - **Sample Records**: Actual data samples for top anomalies ## Workflow **Phase 1: Discovery** 1. Verify connection (if tools fail, guide user to Connectors UI) 2. If no `--table-id`, discover tables or use profile 3. Load table schema **Phase 2: Anomaly Detection** 1. Run `detect_anomalies` - Automated data quality checks 2. Profile numeric fields - Statistics and outliers 3. Profile categorical fields - Cardinality and frequencies 4. Fetch sample records - Get actual data for investigation ## Datarails Brand Styling When generating Excel or PowerPoint files, apply Datarails brand styling: **Font:** Poppins (fall back to Calibri if unavailable). Weights: 400 regular, 600 semibold, 700 bold. **Colors:** | Role | Hex | Use | |------|-----|-----| | Navy | `0C142B` | Header/banner background | | Main text | `333333` | Primary text | | Secondary | `6D6E6F` | Muted/subtitle text | | Border | `9EA1AA` | Cell borders | | Section bg | `F2F2FB` | Section header / row header background (lavender) | | Input bg | `EAEAFF` | Editable/input cell background | | Input text | `4646CE` | Editable cell text (indigo) | | Favorable | `2ECC71` | Positive variance / good KPI delta | | Unfavorable | `E74C3C` | Negative variance / bad KPI delta | | Chart 1 | `0C142B` | Actuals (navy) | | Chart 2 | `F93576` | Budget (hot pink) | | Chart 3 | `00B4D8` | Teal | | Chart 4 | `FFA30F` | Amber | **Excel layout:** - Content starts at column B (column A is a narrow gutter) - Rows 1-6: header banner with navy background, white title text, white subtitle - Gridlines OFF. Freeze panes at B7. - Footer as last row with generation date - Every cell must have font, fill, alignment, and number format set **Number formats:** `_(* #,##0_);_(* (#,##0);_(* "-"_);_(@_)` (default), `$#,##0` (dollars), `$#,##0.0,,"M"` (millions), `0.0%` (percent) **Variance coloring:** Any cell showing a delta/change: green (`2ECC71`) if favorable, red (`E74C3C`) if unfavorable. Apply automatically based on value sign and metric context. **PowerPoint:** Navy (`0C142B`) background, 16:9 widescreen, Poppins font, white text, amber (`FFA30F`) accent lines, card backgrounds `001F37`. **Phase 3: Report Generation** 1. Categorize findings by severity 2. Generate Excel workbook with multiple sheets 3. Apply professional formatting 4. Calculate data quality score **Phase 4: Summary** 1. Display key findings 2. Show health status 3. Guide next steps ## Examples ### Analyze default financials table ```bash /dr-anomalies-report ``` Output: ``` 🔍 Discovering financials table... ✓ Found financials table: TABLE_ID 📊 Analyzing table TABLE_ID... 🔬 Running anomaly detection... 📈 Profiling numeric fields... 📝 Profiling categorical fields... 🔍 Fetching sample records... 📊 Summarizing results... 📄 Generating Excel report... ✅ Report generated: tmp/Anomaly_Report_2026-02-03_143022.xlsx ================================================== ANOMALY DETECTION SUMMARY ================================================== Table: TABLE_ID Total Anomalies: 45 Data Quality Score: 87/100 By Severity: Critical: 2 High: 8 Medium: 23 Low: 12 Report: tmp/Anomaly_Report_2026-02-03_143022.xlsx ================================================== ``` ### Analyze specific table for critical issues only ```bash /dr-anomalies-report --table-id TABLE_ID --severity critical ``` ### Save to custom location ```bash /dr-anomalies-report --env app --output tmp/Quality_Check_Feb_2026.xlsx ``` ## Data Quality Score Score ranges from 0-100: - **90-100** ✅ **Excellent** - Minimal issues, data is reliable - **80-90** 🟢 **Good** - Minor issues, generally usable - **70-80** 🟡 **Fair** - Moderate issues, needs attention - **70** 🟠 **Poor** - Significant issues, requires action - **<70** 🔴 **Critical** - Major issues, immediate action required Calculation: ``` Score = 100 - (critical×10 + high×5 + medium×2 + low×0.5) Clamped to 0-100 range ``` ## Adaptive Behavior ### With Client Profile - Uses table IDs from `config/client-profiles/<env>.json` - Uses discovered field names and mappings - Applies business rules from profile notes ### Without Client Profile - Lists available tables - Automatically discovers table schema - Infers field purposes from names and data types - Uses general data quality rules ### Fallback Discovery If profile incomplete or unavailable: 1. List all Finance OS tables 2. Identify likely data tables (those with numeric fields) 3. Get full schema 4. Discover field purposes automatically 5. Run analysis ## Use Cases ### Monthly Data Quality Check ```bash /dr-anomalies-report --env app --output tmp/DQ_Check_$(date +%Y-%m).xlsx ``` ### Pre-Month-End Close Validation ```bash /dr-anomalies-report --severity critical ``` *Alerts on critical issues that could affect close* ### Department Data Audit ```bash /dr-anomalies-report --table-id 12345 --severity high ``` *Checks specific department data for issues* ### Exploratory Analysis ```bash /dr-anomalies-report --table-id unknown_table_id ``` *Discovers what's in an unfamiliar table* ## Output Files Reports are saved to: `tmp/Anomaly_Report_YYYY-MM-DD_HHMMSS.xlsx` Each report includes: - Professional formatting with colors - Severity-based highlighting - Embedded sample data - Statistical analysis - Investigation queries ## Troubleshooting **"Not authenticated" error** - Connect via Connectors UI ("+" > Connectors > Datarails > Connect) **"No tables found" error** - Check that authentication succeeded - Verify you have access to Finance OS **"Table not found" error** - Verify table ID is correct - Run `/dr-tables` to see available tables **"Incomplete profile" error** - Run `/dr-learn` to refresh profile - Or specify `--table-id` to override ## Related Skills - `/dr-tables` - List and explore available tables - `/dr-learn` - Discover and create client profiles - `/dr-extract` - Extract validated financial data - `/dr-reconcile` - Compare P&L vs KPI data ## Performance - Small tables (< 10K rows): ~30 seconds - Medium tables (10-100K rows): ~1-2 minutes - Large tables (100K+ rows): ~5-10 minutes Scaling handled automatically via pagination and efficient MCP tools.