Does it support local testing?

Yes, it includes specialized commands for setting up local data directories (KBC_DATADIR) and running components with tools like 'uv' and 'pytest'.

How does it access my Keboola jobs?

It uses the Keboola MCP server tools (mcp__keboola__*) to query job details, configurations, and logs when the MCP server is configured and accessible.

Can it fix my code automatically?

It provides specific, actionable code recommendations and configuration updates which you can apply and then verify using the built-in testing workflows.

What types of errors can it detect?

It is optimized to handle Python-specific exceptions, configuration schema violations, API rate limiting, and data-specific issues like malformed CSVs or encoding errors.

Does it differentiate between user and system errors?

Yes, it uses Keboola exit code standards (Exit Code 1 for user/config errors and Exit Code 2 for system/code exceptions) to help categorize the nature of the failure.

Keboola Component Debugger

Name: Keboola Component Debugger
Author: keboola

bykeboola

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Analytics & Monitoring

Diagnoses and fixes failures in Keboola Python components using specialized MCP tools, logs, and local testing environments.

About

The Keboola Component Debugger is a specialized skill designed to streamline the troubleshooting of Python-based data components within the Keboola ecosystem. It leverages Keboola-specific MCP tools to inspect job statuses, configurations, and data outputs while utilizing local CLI tools for reproduction and testing. By analyzing stack traces, exit codes, and common data-related pitfalls like null characters or encoding issues, this skill provides developers with precise root-cause analysis and actionable code or configuration fixes to restore pipeline stability quickly.

Key Features

Pattern-matching for common Python data processing exceptions
Root cause identification for configuration, code, and environment errors
Automated job failure analysis via Keboola MCP integration
Local reproduction workflows using uv and environment variables
7 GitHub stars
Step-by-step verification plans for proposed bug fixes

Use Cases

Validating component configurations against schema requirements before deployment
Resolving data-specific errors like UnicodeDecodeError or null character issues in CSV inputs
Debugging a production job failure in the Keboola platform using Job IDs

About

Key Features

Pattern-matching for common Python data processing exceptions
Root cause identification for configuration, code, and environment errors
Automated job failure analysis via Keboola MCP integration
Local reproduction workflows using uv and environment variables
7 GitHub stars
Step-by-step verification plans for proposed bug fixes

Use Cases

Validating component configurations against schema requirements before deployment
Resolving data-specific errors like UnicodeDecodeError or null character issues in CSV inputs
Debugging a production job failure in the Keboola platform using Job IDs