Agent skill
dr-profile
Profile Datarails Finance OS table fields. Analyze numeric fields for statistics and outliers, categorical fields for cardinality and frequencies.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/profile-datarails-dr-claude-code-plugi
SKILL.md
Datarails Table Profiling
Deep profiling of Finance OS tables - analyze numeric statistics, categorical distributions, and data quality metrics.
Workflow
Step 1: Verify Authentication
If any tool call fails with an authentication or connection error, guide the user to connect via the Connectors UI ("+" > Connectors > Datarails > Connect).
Step 2: Profile the Table
Full profile (default):
- Get table schema with
get_table_schema - Run
profile_table_summaryfor overview - Run
profile_numeric_fieldsfor all numeric columns - Run
profile_categorical_fieldsfor all categorical columns - Present findings in organized sections
Numeric only (--numeric):
- Focus on
profile_numeric_fields - Show: min, max, mean, median, std dev, percentiles
- Highlight outliers (values beyond 3σ)
Categorical only (--categorical):
- Focus on
profile_categorical_fields - Show: cardinality, top values, null counts
- Highlight high-cardinality fields
Specific field (--field):
- Profile just that field with appropriate method
- Show detailed statistics and distribution
Arguments
| Argument | Description |
|---|---|
<table_id> |
Required - the table to profile |
--numeric |
Profile only numeric fields |
--categorical |
Profile only categorical fields |
--field <name> |
Profile a specific field |
--fields <a,b,c> |
Profile specific fields (comma-separated) |
Example Interactions
User: "/dr-profile 11442"
📊 Profile: GL Transactions (ID: 11442)
═══════════════════════════════════════════════════
📈 NUMERIC FIELDS (8 columns)
═══════════════════════════════════════════════════
amount
├── Range: -1,250,000 to 8,750,000
├── Mean: 45,231 | Median: 12,500
├── Std Dev: 125,432
├── Nulls: 0 (0%)
├── Percentiles: P25=5,000 | P50=12,500 | P75=35,000 | P95=250,000
└── ⚠️ Outliers: 127 values beyond 3σ
quantity
├── Range: 0 to 10,000
├── Mean: 125 | Median: 50
├── Nulls: 1,234 (0.98%)
└── Distribution: Normal
═══════════════════════════════════════════════════
📋 CATEGORICAL FIELDS (16 columns)
═══════════════════════════════════════════════════
account_code (156 unique values)
├── Top: 4000-100 (10.0%), 4000-200 (6.6%), 5100-300 (6.3%)
├── Nulls: 0 (0%)
└── Cardinality: Low (156 / 125,432 = 0.12%)
department
├── Top: Sales (35%), Marketing (22%), Operations (18%)
├── Nulls: 892 (0.71%)
└── Cardinality: Very Low (12 unique)
vendor_id (HIGH CARDINALITY ⚠️)
├── Unique: 45,231 (36% of rows)
├── Nulls: 2,341 (1.87%)
└── Note: Consider if this should be a dimension table
User: "/dr-profile 11442 --numeric"
📈 Numeric Profile: GL Transactions
| Field | Min | Max | Mean | Median | Std Dev | Nulls | Outliers |
|-----------|----------|------------|---------|---------|---------|-------|----------|
| amount | -1.25M | 8.75M | 45,231 | 12,500 | 125,432 | 0% | 127 ⚠️ |
| quantity | 0 | 10,000 | 125 | 50 | 342 | 0.98% | 23 |
| unit_cost | 0.01 | 15,000 | 89.50 | 45.00 | 245 | 0% | 45 |
...
User: "/dr-profile 11442 --field amount"
📊 Field Profile: amount (GL Transactions)
Type: DECIMAL
Nullable: No
Nulls: 0 (0%)
Statistics:
├── Count: 125,432
├── Min: -1,250,000
├── Max: 8,750,000
├── Mean: 45,231.45
├── Median: 12,500.00
├── Mode: 10,000.00
├── Std Dev: 125,432.18
├── Variance: 15,733,227,164
Percentiles:
├── P1: -50,000 P5: -10,000
├── P25: 5,000 P50: 12,500
├── P75: 35,000 P95: 250,000
└── P99: 750,000
Distribution: Right-skewed (many small values, few large)
Outliers (|z| > 3): 127 records
├── Below -3σ: 12 records (min: -1,250,000)
└── Above +3σ: 115 records (max: 8,750,000)
💡 Recommendation: Investigate the 127 outlier transactions
Use: /dr-query 11442 "amount > 500000 OR amount < -100000"
Data Quality Indicators
| Symbol | Meaning |
|---|---|
| ⚠️ | Potential issue requiring attention |
| ❌ | Data quality problem detected |
| ✅ | Field looks healthy |
| 📊 | Statistical insight |
Tips
- Profile before running anomaly detection to understand baseline
- High null rates (>5%) may indicate data collection issues
- High cardinality categorical fields may need normalization
- Outliers aren't always bad - verify against business rules
Related Skills
/dr-tables- Discover available tables/dr-anomalies- Automated anomaly detection/dr-query- Investigate specific records
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