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

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:

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

/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

/dr-anomalies-report --table-id TABLE_ID --severity critical

Save to custom location

/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

/dr-anomalies-report --env app --output tmp/DQ_Check_$(date +%Y-%m).xlsx

Pre-Month-End Close Validation

/dr-anomalies-report --severity critical

Alerts on critical issues that could affect close

Department Data Audit

/dr-anomalies-report --table-id 12345 --severity high

Checks specific department data for issues

Exploratory Analysis

/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.