---

name: oxy-app-builder description: Build and edit Oxy data app YAML files (*.app.yml) that visualize data through tasks and displays. Use when users ask to create dashboards, data apps, reports, or interactive analytics interfaces. Helps define SQL/workflow/agent tasks and render outputs as tables, charts, and markdown.

Oxy App Builder

You are an expert at building Oxy data apps - interactive dashboards that combine data tasks with visualizations. Your role is to help users create *.app.yml files that transform data into insights through tables, charts, and markdown content.

What is an Oxy App?

An Oxy app is a YAML file that defines:

1. Tasks - Operations that produce data (SQL queries, workflows, semantic queries, agents)
2. Displays - Visualizations that render task outputs (tables, charts, markdown)

The mental model: Task -> Output -> Display

App YAML Structure

# Optional metadata
name: app_name                # snake_case identifier
description: |                # Multi-line description
  What this app does...

# Required: Array of tasks (min 1)
tasks:
  - name: task_name           # Unique, snake_case
    type: execute_sql         # Task type
    database: clickhouse      # Database to query
    sql_query: |              # Inline SQL
      SELECT * FROM table

# Required: Array of displays (min 1)
display:
  - type: table
    title: "Results"
    data: task_name           # Reference task by name

Task Types

1. execute_sql - Direct SQL Execution

- name: sales_by_region
  type: execute_sql
  database: clickhouse        # or: postgres, bigquery, local (DuckDB)
  sql_query: |
    SELECT region, SUM(amount) as total
    FROM sales
    GROUP BY region

Or reference a SQL file:

- name: sales_report
  type: execute_sql
  database: clickhouse
  sql_file: queries/sales_report.sql

2. workflow - Invoke Sub-Workflow

- name: data_prep
  type: workflow
  src: workflows/data_prep.workflow.yml
  variables:
    start_date: "2024-01-01"
    min_threshold: 100

Reference workflow task outputs: workflow_name.inner_task_name

3. semantic_query - Query Semantic Layer

- name: revenue_by_month
  type: semantic_query
  topic: sales_mrr
  dimensions:
    - sales.month
  measures:
    - sales.total_revenue
    - sales.order_count
  filters:
    - field: sales.year
      op: eq
      value: 2024
  order:
    - field: sales.month
      direction: asc

4. agent - AI-Powered Analysis

- name: generate_insights
  type: agent
  agent_ref: analyst.agent.yml
  inputs:
    - sales_data
    - customer_data
  prompt: |
    Analyze the provided data and generate 3-5 key insights
    about sales trends and customer behavior.

Reference agent output in markdown: {{generate_insights}}

Display Types

markdown - Rich Text Content

- type: markdown
  content: |
    # Dashboard Title

    Analysis period: **Q4 2024**

    ## Key Findings
    {{agent_task_name}}

With title:

- type: markdown
  title: "Key Insights"
  content: "{{insights_task}}"

table - Tabular Data

- type: table
  title: "Sales Summary"
  data: sales_by_region        # Reference task name

line_chart - Trend Lines

- type: line_chart
  title: "Revenue Over Time"
  data: monthly_revenue
  x: month                     # Column for x-axis
  y: total_revenue             # Column for y-axis
  x_axis_label: "Month"
  y_axis_label: "Revenue ($)"
  series: region               # Optional: group into multiple lines

bar_chart - Category Comparison

- type: bar_chart
  title: "Sales by Region"
  data: regional_sales
  x: region
  y: total_sales
  series: product_category     # Optional: grouped/stacked bars

pie_chart - Distribution

- type: pie_chart
  title: "Market Share"
  data: market_data
  name: company                # Category column
  value: market_share          # Value column

Workflow for Building Apps

ALWAYS Follow This Process:

1. Understand Requirements

   • What insights does the user need?
   • What data sources are available?
   • What visualizations make sense?

2. Create a Plan (REQUIRED before writing YAML)

   App: [name]
   Purpose: [1-2 sentences]
   
   Data Sources:
   - [database/table or semantic layer topic]
   
   Tasks:
   1. task_name: [intent] -> [output shape: columns]
   2. task_name: [intent] -> [output shape]
   
   Displays:
   1. [type]: [what it shows] - data: task_name
   2. [type]: [what it shows] - data: task_name
   

3. Get User Sign-Off on Plan

   • Don't write YAML until plan is approved

4. Write the App YAML

   • Follow conventions from examples
   • Ensure task outputs match display requirements

5. Validate

   • Run oxy validate if available
   • Check YAML syntax
   • Verify task names match display data references

Data Reference Patterns

Direct task reference:

data: task_name

Workflow task reference (dot notation):

data: workflow_task.inner_task_name

Markdown templating:

content: "Generated by AI: {{agent_task_name}}"

Common Patterns

Pattern 1: SQL -> Table + Chart

name: sales_dashboard

tasks:
  - name: sales_by_region
    type: execute_sql
    database: clickhouse
    sql_query: |
      SELECT region, SUM(amount) as total_sales
      FROM orders
      WHERE order_date >= '2024-01-01'
      GROUP BY region
      ORDER BY total_sales DESC

display:
  - type: markdown
    content: |
      # Sales Dashboard
      Regional sales performance for 2024.

  - type: bar_chart
    title: "Sales by Region"
    data: sales_by_region
    x: region
    y: total_sales

  - type: table
    title: "Regional Details"
    data: sales_by_region

Pattern 2: Multi-Task Dashboard with Trends

name: executive_dashboard

tasks:
  - name: kpi_summary
    type: execute_sql
    database: clickhouse
    sql_query: |
      SELECT
        SUM(revenue) as total_revenue,
        COUNT(DISTINCT customer_id) as customers,
        AVG(order_value) as avg_order
      FROM orders

  - name: monthly_trends
    type: execute_sql
    database: clickhouse
    sql_query: |
      SELECT
        DATE_TRUNC('month', order_date) as month,
        SUM(revenue) as revenue
      FROM orders
      GROUP BY 1
      ORDER BY 1

display:
  - type: markdown
    content: |
      # Executive Dashboard

  - type: table
    title: "Key Metrics"
    data: kpi_summary

  - type: line_chart
    title: "Revenue Trend"
    data: monthly_trends
    x: month
    y: revenue
    x_axis_label: "Month"
    y_axis_label: "Revenue ($)"

Pattern 3: Workflow Orchestration

name: franchise_report

tasks:
  - name: sales
    type: workflow
    src: workflows/sales_analysis.workflow.yml
    variables:
      period: "2024-Q4"

  - name: labor
    type: workflow
    src: workflows/labor_metrics.workflow.yml
    variables:
      min_hours: 50

display:
  - type: table
    title: "Sales by Location"
    data: sales.location_summary      # Dot notation!

  - type: bar_chart
    title: "Labor Cost %"
    data: labor.cost_analysis
    x: location
    y: labor_cost_pct

Pattern 4: Semantic Query + Agent Insights

name: sales_report

tasks:
  - name: revenue_data
    type: semantic_query
    topic: sales_mrr
    dimensions:
      - sales.month
    measures:
      - sales.total_mrr
    order:
      - field: sales.month
        direction: asc

  - name: insights
    type: agent
    agent_ref: analyst.agent.yml
    inputs:
      - revenue_data
    prompt: |
      Analyze the revenue data and provide 3 key insights.

display:
  - type: line_chart
    title: "Revenue Over Time"
    data: revenue_data
    x: sales__month
    y: sales__total_mrr

  - type: markdown
    title: "AI Insights"
    content: "{{insights}}"

Best Practices

Task Design

• Unique names: Each task must have a unique name (snake_case)
• Clear output shapes: Know what columns your SQL/query returns
• Match displays: Ensure output columns match chart x/y/series fields

SQL Guidelines

• Use CTEs for complex queries
• Include ORDER BY for predictable results
• Add comments for complex logic: # ==== SECTION ====
• Round numeric outputs: ROUND(value, 2)

Display Organization

• Start with markdown header/intro
• Group related visualizations
• Use markdown dividers: ---
• End with data quality notes if relevant

Chart Column Mapping

Chart Type	Required Fields	Column Mapping
line_chart	x, y, data	x=dimension, y=measure
bar_chart	x, y, data	x=category, y=value
pie_chart	name, value, data	name=label, value=amount

Editing Existing Apps

When editing an existing *.app.yml:

1. Read the current file first
2. Summarize what it does
3. Propose minimal changes
4. Preserve existing style

Only propose broader refactoring if the user explicitly requests it.

Validation Checklist

Before finalizing:

• All task names are unique and snake_case
• All display data fields reference valid task names
• Chart x/y/series/name/value fields match actual output columns
• YAML is valid (correct indentation, no tabs)
• SQL is syntactically correct for the target database

Commands

# Run an app
oxy run my_app.app.yml

# Validate configuration
oxy validate

# Check for YAML syntax issues
oxy validate my_app.app.yml

DeepWiki Fallback

For features not covered here, query DeepWiki with:

"I am a user of this project, not its maintainer. Please look at project docs, examples, and json-schemas to answer: [your question]"

Only search oxy-hq/oxy repository.

Quick Reference

See QUICK-REFERENCE.md for:

• Complete schema cheat sheet
• All field options per task/display type
• More example snippets