Set up your development environment for declarative pipelines

Overview

This guide covers setting up VS Code with YAML schema validation to edit Qlik pipeline YAML files with autocompletion and inline error detection. VS Code is the recommended editor for this workflow, but you can use any editor that supports YAML schema validation.

Prerequisites

• A Qlik Cloud pipeline project connected to GitHub version control. For setup steps, see Manage your pipeline projects with version control.
• A GitHub account with read access to the repository that contains the project.
• Git installed on your machine.

Step 1: Install the Red Hat YAML extension and configure schema validation

The Red Hat YAML extension adds schema-based autocompletion and inline validation to the VS Code YAML editor.

1. Open VS Code.
2. Go to Extensions (Ctrl+Shift+X on Windows, Cmd+Shift+X on macOS).
3. Search for YAML and install the extension published by Red Hat.

Qlik pipeline YAML schemas are published to SchemaStore.org. The Red Hat YAML extension automatically downloads schemas from SchemaStore and applies them based on file naming conventions. Files named qtcp_project.yaml and files under qtcp_tasks/ are recognized automatically.

With schemas configured, VS Code shows:

• Inline errors for invalid or missing required properties.
• Autocompletion suggestions as you type property names.
• Hover documentation for each property.

Step 2: Use AI assistants to configure pipelines

Use AI assistants (such as GitHub Copilot or Anthropic Claude) to streamline YAML configuration creation and editing. To ensure accuracy and adherence to project standards, follow these best practices:

1. Select a high-capability model. Use a premium, large-context model (for example, Anthropic Claude Sonnet 4.6 or higher) rather than a standard or “auto” model for complex YAML generation and schema adherence. Advanced models correctly interpret complex pipeline schemas and produce more accurate configurations.

2. Configure project-specific AI instructions. Improve AI-generated configuration quality by providing project context to the model. Create an instruction file in the root of your VS Code workspace. Choose one of the following:

   • CLAUDE.md (for Anthropic Claude Code extension)
   • .github/copilot-instructions.md (for GitHub Copilot)

   The content of your instruction file should be as follows:

   This is a Qlik Talend Cloud Pipeline (QTCP) project.
   Before any project advice or YAML edits, I MUST read the source of truth first:
   https://raw.githubusercontent.com/qlik-oss/schemas/refs/heads/main/qtcp/ai-instructions.md

Step 3: Clone the repository

You can clone the GitHub repository containing your pipeline project using VS Code’s built-in Git features or your preferred Git method.

1. Clone the repository to your local machine.
2. Open the project folder in VS Code. You should see the project YAML files in the Explorer panel.
3. Open qtcp_project.yaml to confirm that schema validation is active. VS Code displays the associated schema for the open YAML file. Hover over a property to view its documentation.

Using schemas with other IDEs

Qlik publishes pipeline YAML schemas to SchemaStore.org and maintains them in the qlik-oss/schemas GitHub repository.

Most editors that support JSON Schema validation can use these schemas. The general approach is:

1. Locate the schema for the file type you want to validate. Browse the qlik-oss/schemas repository or search the SchemaStore catalog for entries starting with qtcp.
2. Configure your editor to associate the schema URL with the corresponding file pattern: qtcp_project.yaml for the project file, or the task file pattern under qtcp_tasks/. The configuration method varies by editor. Refer to your editor’s documentation for details.

Next steps

With your environment set up, you can edit a declarative pipeline with version control.