Qualytics CLI

Command-line interface for the Qualytics data quality platform.

Manage connections, datastores, containers, quality checks, anomalies, and operations as code. Export your entire Qualytics configuration to git-tracked YAML files and deploy across environments through CI/CD pipelines.

Installation

pip install qualytics-cli

Or with uv (faster):

uv pip install qualytics-cli

Requirements: Python 3.10 or higher.

Quick Start

# 1. Authenticate via browser (recommended)
uv run qualytics auth login --url "https://your-instance.qualytics.io/"

# 2. Check connectivity and API compatibility
qualytics doctor

# 3. Export your datastore configuration to YAML
qualytics config export --datastore-id 1 --output ./qualytics-config

# 4. Preview what an import would do (without making changes)
qualytics config import --input ./qualytics-config --dry-run

Commands

Group	Description
`auth`	Authenticate and manage credentials
`connections`	Create and manage database connections
`datastores`	Create and manage datastores
`containers`	Create and manage computed containers
`checks`	Create and manage quality checks
`dbt`	Migrate dbt tests to Qualytics quality checks
`anomalies`	View and manage detected anomalies
`operations`	Trigger sync, profile, and scan operations
`config`	Export and import configuration as code
`users`	List and view users
`teams`	List and view teams
`tags`	Manage tags (list, create, delete)
`schedule`	Schedule recurring operations
`mcp`	Start the MCP server for LLM integration
`doctor`	Check CLI health, connectivity, and API compatibility

Run qualytics <command> --help for full details on any command.

Documentation

Guide	Description
Getting Started	Authentication, configuration, environment variables
Connections	Creating and managing database connections
Datastores	Creating and managing datastores
Quality Checks	Creating checks from YAML (single and bulk)
Operations	Sync, profile, scan workflows
Export/Import	Config-as-code: export, import, CI/CD promotion
dbt Migration	Converting a dbt manifest into quality checks
Anomalies	Viewing and managing anomalies
Computed Fields	User-defined computed fields in export/import
Computed Tables	Bulk import of computed tables from Excel/CSV
MCP Server	LLM integration with Claude Code, Cursor, etc.
CI/CD Promotion	GitHub Actions workflow for environment promotion

Development

git clone https://github.com/Qualytics/qualytics-cli.git
cd qualytics-cli
uv sync                              # Install dependencies
uv run pytest                        # Run tests
uv run pre-commit run --all-files    # Lint, format, type checks

For architecture details and contribution guidelines, see AGENTS.md.

Releasing

Releases are automated via GitHub Actions. The version lives in pyproject.toml and is managed by uv version.

Steps to release a new version

Ensure main is green -- CI (lint + tests across Python 3.10-3.14 + pre-commit) must pass.
Trigger the Release workflow -- Go to Actions > Release and click Run workflow. Select the bump type:
- patch -- bug fixes (1.0.0 → 1.0.1)
- minor -- new features (1.0.0 → 1.1.0)
- major -- breaking changes (1.0.0 → 2.0.0)
The workflow automatically:
- Bumps the version in pyproject.toml via uv version --bump <type>
- Commits the change and creates a v{version} git tag
- Pushes the commit and tag to main
The tag push triggers the Publish workflow, which:
- Builds the package (uv build)
- Publishes to PyPI via OIDC trusted publishing (no API tokens needed)
- Creates a GitHub Release with auto-generated release notes and attached artifacts

Manual version check

# Current version in pyproject.toml
uv version --short

# Installed version
qualytics --version

License

MIT License -- see LICENSE for details.

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