---

name: zo-dataset-creator description: > Create Zo Datasets correctly formatted for the Zo UI. Use when creating a new dataset from scratch, setting up a dataset with proper datapackage.json, schema.yaml, and data.duckdb structure, fixing existing datasets that don't display tables in the Zo UI, or generating schema.yaml from an existing DuckDB database. Handles common pitfalls of manual schema formatting and ensures all required files are present and correctly structured. compatibility: Created for Zo Computer metadata: author: skeletorjs category: Community

Zo Dataset Creator

Create Zo Datasets with correct formatting for the Zo Datasets UI.

Quick Start

Creating a New Dataset

Use scripts/create_dataset.py to scaffold a new dataset:

python3 scripts/create_dataset.py <dataset-name>

This creates:

• datapackage.json with required metadata
• generate_schema.py for schema generation
• schema.yaml (auto-generated after database creation)
• ingest/, source/, assets/ directories
• README.md and PROCESS.md templates

Fixing an Existing Dataset

If tables don't show in the Zo UI:

1. Verify datapackage.json exists
2. Ensure data.duckdb is a valid database
3. Re-generate schema: python3 generate_schema.py

See TROUBLESHOOTING.md for common issues.

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Dataset Structure

A valid Zo Dataset requires:

dataset-name/
├── datapackage.json          # Required: Dataset metadata
├── schema.yaml                # Required: Auto-generated from database
├── data.duckdb                # Required: DuckDB database
├── generate_schema.py         # Required: Script to generate schema
├── README.md                  # Recommended: Dataset documentation
├── PROCESS.md                 # Recommended: Ingestion instructions
├── ingest/                    # Optional: Ingestion scripts
├── source/                    # Optional: Raw source files
└── assets/                    # Optional: Generated files

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Critical: Schema Format

ALWAYS auto-generate schema.yaml from the database. Never write it manually.

The Zo UI expects this format (list of tables with name: keys):

tables:
- name: my_table
  row_count: 10
  columns:
  - name: id
    type: VARCHAR
  - name: title
    type: VARCHAR
  - name: created_at
    type: TIMESTAMP

This format is generated by generate_schema.py.

Do NOT use manual YAML format (nested dictionaries):

# WRONG - Will not display in Zo UI
tables:
  my_table:
    columns:
      id:
        type: VARCHAR

---

Using the Scripts

create_dataset.py

Create a new dataset scaffold:

python3 scripts/create_dataset.py my-dataset

Creates the dataset in Datasets/my-dataset/ with all required files.

generate_schema.py

Generate schema from an existing database:

cd /home/workspace/Datasets/my-dataset
python3 generate_schema.py

When to run:

• After creating data.duckdb
• After modifying table structure (add/remove columns, create tables)
• After changing COMMENT annotations
• After any database schema changes

validate_dataset.py

Validate a dataset structure:

python3 scripts/validate_dataset.py /home/workspace/Datasets/my-dataset

Checks:

• datapackage.json exists and is valid
• data.duckdb exists and is readable
• schema.yaml exists and is in correct format
• Tables in schema match tables in database

---

Workflow Examples

Example 1: New Dataset from CSV

# 1. Create scaffold
python3 scripts/create_dataset.py sales-data

# 2. Copy CSV to source/
cp sales.csv /home/workspace/Datasets/sales-data/source/

# 3. Create database and import data
cd /home/workspace/Datasets/sales-data
python3 -c "
import duckdb
con = duckdb.connect('data.duckdb')
con.execute(\"CREATE TABLE sales AS SELECT * FROM 'source/sales.csv'\")
con.execute(\"COMMENT ON TABLE sales IS 'Monthly sales data'\")
con.close()
"

# 4. Generate schema
python3 generate_schema.py

# 5. View in Zo UI at /?t=datasets

Example 2: Fixing Broken Dataset

# 1. Validate to identify issues
python3 scripts/validate_dataset.py /home/workspace/Datasets/broken-dataset

# 2. If schema format is wrong, re-generate
cd /home/workspace/Datasets/broken-dataset
python3 generate_schema.py

# 3. Validate again
python3 scripts/validate_dataset.py /home/workspace/Datasets/broken-dataset

Example 3: Adding Comments to Tables

import duckdb

con = duckdb.connect('data.duckdb')

# Add table comment
con.execute("COMMENT ON TABLE videos IS 'YouTube videos from watchlist playlist'")

# Add column comments
con.execute("COMMENT ON COLUMN videos.title IS 'Video title from YouTube'")
con.execute("COMMENT ON COLUMN videos.view_count IS 'Total view count'")

con.close()

# Re-generate schema to include comments
# (run: python3 generate_schema.py)

---

Best Practices

Use COMMENT Annotations

Add inline comments to tables and columns — they're extracted into schema.yaml:

CREATE TABLE videos (
    id VARCHAR PRIMARY KEY COMMENT 'YouTube video ID',
    title VARCHAR COMMENT 'Video title from YouTube metadata',
    published_at TIMESTAMP COMMENT 'When the video was published'
) COMMENT 'Collection of videos from the watchlist playlist'

Keep Schema in Sync

Always re-run generate_schema.py after database changes:

• Adding/removing columns
• Changing column types
• Creating new tables
• Updating COMMENT annotations

Use Descriptive Names

• Tables: playlist_videos, user_sessions, transactions
• Columns: video_id, created_at, total_amount
• Use snake_case consistently

Document in README.md

Include:

• Purpose of the dataset
• What data it contains
• How to query it
• Example queries
• Business rules and caveats

---

Common Pitfalls

See TROUBLESHOOTING.md for detailed solutions to:

• Tables not showing in Zo UI
• Invalid schema.yaml format
• Missing datapackage.json
• Stale schema after database changes
• Locked database files

---

Reference Materials

• TROUBLESHOOTING.md - Common issues and solutions
• SCHEMA_GUIDE.md - Schema formatting details
• DATAPACKAGE_SPEC.md - datapackage.json reference
• DUCKDB_BASICS.md - DuckDB usage examples