# Draxlr
> Draxlr Integration
- Skill: `jgtolentino/draxlr` (Agent Skill)
- Install (CLI): `npx skillmds@latest add jgtolentino/draxlr`
- Raw SKILL.md: https://api.skillmd.com/api/skills/jgtolentino/draxlr/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Integrations & APIs
- Author: jgtolentino (https://skillmd.com/u/jgtolentino)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/jgtolentino/draxlr
---
# Draxlr Integration
**Skill ID:** `draxlr_integration`
**Version:** 1.0.0
**Category:** Analytics, Business Intelligence
**Expertise Level:** Intermediate
---
## 🎯 Purpose
This skill enables low-lift analytics and BI integration using Draxlr, a lightweight alternative to traditional BI tools like Tableau or Power BI. Draxlr provides instant SQL-to-dashboard capabilities with minimal setup.
### Key Capabilities
- Quick dataset configuration from PostgreSQL/Supabase
- SQL-based chart and dashboard creation
- Embedded analytics for web applications
- Real-time data visualization
- Shareable dashboards and reports
- Role-based access control
---
## 🧠Core Competencies
### 1. Database Connection Setup
#### Supabase/PostgreSQL Connection
```json
{
"type": "postgres",
"host": "aws-1-us-east-1.pooler.supabase.com",
"port": 6543,
"database": "postgres",
"username": "postgres.PROJECT_REF",
"password": "YOUR_PASSWORD",
"ssl": true,
"schema": "public"
}
```
#### Connection Configuration
```python
# scripts/draxlr/configure_connection.py
import os
import requests
from typing import Dict, Any
DRAXLR_API_KEY = os.getenv('DRAXLR_API_KEY')
DRAXLR_BASE_URL = 'https://api.draxlr.com/v1'
def create_connection(config: Dict[str, Any]) -> Dict[str, Any]:
"""Create a new database connection in Draxlr"""
response = requests.post(
f'{DRAXLR_BASE_URL}/connections',
headers={
'Authorization': f'Bearer {DRAXLR_API_KEY}',
'Content-Type': 'application/json'
},
json={
'name': config['name'],
'type': config['type'],
'config': {
'host': config['host'],
'port': config['port'],
'database': config['database'],
'username': config['username'],
'password': config['password'],
'ssl': config.get('ssl', True)
}
}
)
response.raise_for_status()
return response.json()
# Example usage
connection = create_connection({
'name': 'InsightPulse Supabase',
'type': 'postgres',
'host': os.getenv('SUPABASE_DB_HOST'),
'port': int(os.getenv('SUPABASE_DB_PORT', 6543)),
'database': os.getenv('SUPABASE_DB_NAME'),
'username': os.getenv('SUPABASE_DB_USER'),
'password': os.getenv('SUPABASE_DB_PASSWORD'),
'ssl': True
})
print(f"✅ Connection created: {connection['id']}")
```
### 2. Dataset Configuration
#### Analytics View Setup
```sql
-- warehouse/draxlr/expense_analytics.sql
-- View optimized for Draxlr dashboards
CREATE OR REPLACE VIEW draxlr.vw_expense_analytics AS
SELECT
e.id,
e.name AS expense_name,
e.employee_id,
emp.name AS employee_name,
emp.department_id,
dept.name AS department_name,
e.total_amount,
e.date AS expense_date,
DATE_TRUNC('month', e.date) AS expense_month,
DATE_TRUNC('week', e.date) AS expense_week,
e.state AS approval_status,
e.x_category AS category,
e.x_merchant AS merchant,
e.x_policy_breach AS has_policy_breach,
e.x_ocr_confidence AS ocr_confidence,
e.x_approval_latency_days AS approval_latency_days,
CASE
WHEN e.x_approval_latency_days <= 1 THEN 'Fast'
WHEN e.x_approval_latency_days <= 3 THEN 'Normal'
ELSE 'Slow'
END AS approval_speed,
CASE
WHEN e.total_amount < 50 THEN 'Small'
WHEN e.total_amount < 200 THEN 'Medium'
WHEN e.total_amount < 500 THEN 'Large'
ELSE 'Very Large'
END AS expense_size_category
FROM staging_odoo.hr_expense e
LEFT JOIN staging_odoo.hr_employee emp ON e.employee_id = emp.id
LEFT JOIN staging_odoo.hr_department dept ON emp.department_id = dept.id
WHERE e.date >= CURRENT_DATE - INTERVAL '90 days';
-- Grant access
GRANT SELECT ON draxlr.vw_expense_analytics TO draxlr_viewer;
```
#### Dataset Registration
```json
{
"name": "Expense Analytics",
"connection_id": "conn_123abc",
"type": "view",
"schema": "draxlr",
"table": "vw_expense_analytics",
"refresh_schedule": "0 */6 * * *",
"description": "90-day expense analytics with employee and department context",
"columns": [
{"name": "id", "type": "integer", "description": "Expense ID"},
{"name": "expense_name", "type": "text", "description": "Expense description"},
{"name": "employee_name", "type": "text", "description": "Employee name"},
{"name": "department_name", "type": "text", "description": "Department"},
{"name": "total_amount", "type": "numeric", "description": "Expense amount", "format": "currency"},
{"name": "expense_date", "type": "date", "description": "Expense date"},
{"name": "expense_month", "type": "date", "description": "Month bucket"},
{"name": "category", "type": "text", "description": "Expense category"},
{"name": "approval_status", "type": "text", "description": "Current status"},
{"name": "has_policy_breach", "type": "boolean", "description": "Policy violation flag"}
]
}
```
### 3. Chart Creation
#### SQL-Based Chart Definitions
```yaml
# draxlr/charts/expense_by_month.yml
name: "Monthly Expense Trends"
type: "line"
dataset: "expense_analytics"
query: |
SELECT
expense_month AS month,
SUM(total_amount) AS total_expenses,
COUNT(*) AS expense_count,
AVG(total_amount) AS avg_expense
FROM draxlr.vw_expense_analytics
WHERE expense_month >= CURRENT_DATE - INTERVAL '12 months'
GROUP BY expense_month
ORDER BY expense_month
x_axis: "month"
y_axes:
- column: "total_expenses"
label: "Total Amount"
format: "currency"
- column: "expense_count"
label: "Count"
format: "number"
filters:
- column: "department_name"
type: "multi-select"
label: "Department"
- column: "expense_month"
type: "date-range"
label: "Date Range"
```
#### Python Chart Builder
```python
# scripts/draxlr/create_chart.py
def create_chart(config: Dict[str, Any]) -> Dict[str, Any]:
"""Create a chart in Draxlr"""
response = requests.post(
f'{DRAXLR_BASE_URL}/charts',
headers={
'Authorization': f'Bearer {DRAXLR_API_KEY}',
'Content-Type': 'application/json'
},
json={
'name': config['name'],
'type': config['type'],
'dataset_id': config['dataset_id'],
'query': config['query'],
'config': {
'x_axis': config.get('x_axis'),
'y_axes': config.get('y_axes', []),
'filters': config.get('filters', []),
'colors': config.get('colors', ['#3B82F6', '#10B981', '#F59E0B'])
}
}
)
response.raise_for_status()
return response.json()
# Example: Top 10 merchants by spend
chart = create_chart({
'name': 'Top Merchants by Spend',
'type': 'bar',
'dataset_id': 'ds_expense_analytics',
'query': '''
SELECT merchant, SUM(total_amount) AS total_spend
FROM draxlr.vw_expense_analytics
WHERE merchant IS NOT NULL
GROUP BY merchant
ORDER BY total_spend DESC
LIMIT 10
''',
'x_axis': 'merchant',
'y_axes': [{'column': 'total_spend', 'label': 'Total Spend', 'format': 'currency'}]
})
```
### 4. Dashboard Assembly
#### Dashboard Configuration
```json
{
"name": "T&E Executive Dashboard",
"description": "Real-time travel and expense analytics",
"layout": {
"type": "grid",
"columns": 12,
"widgets": [
{
"chart_id": "chart_monthly_trends",
"position": {"x": 0, "y": 0, "w": 8, "h": 4}
},
{
"chart_id": "chart_top_merchants",
"position": {"x": 8, "y": 0, "w": 4, "h": 4}
},
{
"chart_id": "chart_policy_violations",
"position": {"x": 0, "y": 4, "w": 6, "h": 3}
},
{
"chart_id": "chart_approval_latency",
"position": {"x": 6, "y": 4, "w": 6, "h": 3}
}
]
},
"filters": [
{"type": "date-range", "default": "last_90_days"},
{"type": "department", "default": "all"}
],
"refresh_interval": 300
}
```
### 5. Embedded Analytics
#### Embed Code Generation
```html
```
#### Dynamic Embed with Filters
```javascript
// odoo/modules/ip_expense_ocr/static/src/js/draxlr_embed.js
odoo.define('ip_expense_ocr.draxlr_embed', function (require) {
"use strict";
const AbstractField = require('web.AbstractField');
const fieldRegistry = require('web.field_registry');
const DraxlrDashboard = AbstractField.extend({
template: 'DraxlrDashboardWidget',
start: function () {
this._super.apply(this, arguments);
this._renderDashboard();
},
_renderDashboard: function () {
const dashboardId = this.recordData.dashboard_id || 'default';
const embedToken = this.recordData.embed_token;
const department = this.recordData.department_id;
const iframeUrl = `https://app.draxlr.com/embed/dashboard/${dashboardId}` +
`?token=${embedToken}` +
`&filter_department=${department}`;
this.$el.html(`
`);
}
});
fieldRegistry.add('draxlr_dashboard', DraxlrDashboard);
});
```
---
## ✅ Validation Criteria
### Integration Quality
- ✅ Connection established with <2s latency
- ✅ Datasets refresh on schedule (every 6 hours)
- ✅ Charts render in <3s
- ✅ Dashboards support 100+ concurrent viewers
- ✅ Embed tokens have expiry and rotation
### Data Accuracy
- ✅ SQL views match Odoo source data
- ✅ Aggregations validated against source
- ✅ Filters work correctly with parameters
- ✅ Real-time data sync lag <5 minutes
---
## 🎯 Usage Examples
### Example 1: Quick Dashboard Setup
```bash
# Create analytics views
psql $SUPABASE_DB_URL -f warehouse/draxlr/expense_analytics.sql
# Configure connection (via Draxlr UI or API)
python3 scripts/draxlr/configure_connection.py
# Create dataset and charts
python3 scripts/draxlr/setup_dashboard.py
```
### Example 2: Embedded Report in Odoo
```xml
```
### Example 3: Automated Report Distribution
```python
# scripts/draxlr/scheduled_report.py
def email_dashboard_snapshot(dashboard_id: str, recipients: list):
"""Email dashboard snapshot via Draxlr API"""
response = requests.post(
f'{DRAXLR_BASE_URL}/dashboards/{dashboard_id}/snapshot',
headers={'Authorization': f'Bearer {DRAXLR_API_KEY}'},
json={
'format': 'pdf',
'recipients': recipients,
'subject': 'Weekly Expense Report',
'filters': {'date_range': 'last_7_days'}
}
)
return response.json()
# Schedule with cron or pg_cron
email_dashboard_snapshot('dash_tne_exec', ['finance@company.com'])
```
---
## 📊 Success Metrics
### Adoption Metrics
- **Dashboard Views**: 100+ per day
- **Unique Users**: 20+ per week
- **Report Exports**: 10+ per week
- **Embed Usage**: 50+ views per day
### Performance Metrics
- **Query Response Time**: <2s p95
- **Dashboard Load Time**: <3s
- **Data Freshness**: <5min lag
- **Uptime**: >99.5%
---
## 🔗 Related Skills
- `superset-dashboard-automation` - Apache Superset alternative
- `superset-chart-builder` - Advanced BI charting
- `odoo-finance-automation` - Source data for analytics
---
## 📚 References
- [Draxlr Documentation](https://docs.draxlr.com/)
- [Draxlr API Reference](https://api.draxlr.com/docs)
- [Draxlr vs Superset Comparison](https://draxlr.com/vs/superset)
- [Embedded Analytics Guide](https://docs.draxlr.com/embedding)
---
**Maintained by:** InsightPulse AI Team
**License:** AGPL-3.0
**Integration Type:** Optional - Analytics Enhancement