python-script_skill

Overview

This skill helps you design and run robust Python automation with structured logging, safety checks, and reproducible execution. It is tailored for tasks that require complex data processing, authenticated API interactions, conditional file work, or advanced error handling. The skill provides a clear workflow, logging conventions, and templates to produce maintainable scripts.

How this skill works

You start by confirming inputs, outputs, constraints, and preferred libraries. The skill identifies risky operations and requires explicit approval before destructive actions. It scaffolds a PEP-compliant script with structured logging to both file and console, adds linting and run commands, and surfaces results, risks, and follow-up steps after execution.

When to use it

• Complex ETL jobs that need validation, retries, and temporary intermediates
• Authenticated API automation with response validation and rate-limit handling
• File system changes that must be conditional, reversible, or logged for audit
• Long-running tasks requiring rotating logs and progress timestamps
• Scripts that must pass linting and be reproducibly executed by agents

Best practices

• Confirm inputs, outputs, constraints, and any external dependencies before coding
• Mark risky operations and require explicit approval or a dry-run flag
• Log start/end times, parameters, file operations, and all exceptions to file and console
• Use temporary directories for intermediates and provide rollback or dry-run modes
• Lint scripts with the recommended command and use RotatingFileHandler for large logs

Example use cases

• Pull data from an authenticated API, transform it, and write versioned CSVs with full audit logs
• Batch rename or move files conditionally, with a dry-run report and rollback option
• Run a scheduled data cleaning pipeline that logs parameters, timing, and errors to a central log store
• Perform multi-step deployments where each destructive step requires explicit approval and logging