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refresh-libraries

Automates upstream verification and approval-based updating of pinned library versions in pattern handbooks.

5|1|Updated Sep 23, 2025
Search Tags:#data-engineering#dependency-updates#canonical-imports#version-verification#library-version-management#pattern-handbook#spark-versioning
One-click install
npx skills add https://github.com/RDEWAI/Redefining-DataEngineering-With-AI --skill refresh-libraries
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: refresh-libraries
Source: https://github.com/RDEWAI/Redefining-DataEngineering-With-AI/tree/main/chapter-6/developer-plugin/skills/refresh-libraries
Command: npx skills add https://github.com/RDEWAI/Redefining-DataEngineering-With-AI --skill refresh-libraries

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pyyaml, and includes scripts (resource) components.

What problem does it solve?

Manually tracking and updating pinned library versions in data engineering pattern handbooks is time-consuming and error-prone, especially for complex stacks with interdependent libraries like Spark, Delta Lake, and DuckDB where version mismatches can cause runtime failures or broken generated code.

Core Features & Use Cases

  • Upstream Version Verification: Automatically checks pinned library versions against Context7, PyPI, GitHub releases, and official docs to detect stale or outdated pins.
  • Approval-Gated Updates: Generates a clear diff of version changes, breaking change notes, and floor violations, then requests explicit user approval before modifying the version catalogue.
  • Import Overlay Enforcement: Integrates with curated library-imports.yaml files to enforce minimum version floors and canonical import rules, preventing broken generated code from version mismatches. Use Case: A data engineering team using the pattern handbook can run this skill to quickly verify all their pinned library versions are current, avoid compatibility issues with Spark and Delta Lake, and ensure generated code uses the correct import paths without manual cross-referencing.

Quick Start

Use the refresh-libraries skill to check for outdated library versions in your pattern handbook and apply approved updates.

Frequently Asked Questions about refresh-libraries

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate tracking and updating pinned library versions for a data engineering pattern handbook?▼

Automating pinned library version updates for a data engineering pattern handbook involves verifying upstream sources like PyPI, GitHub releases, and Context7. This Skill resolves the latest versions for data stack libraries like PySpark and Delta Lake, then prompts for approval before modifying the catalogue.

What is the best way to prevent broken generated code from Spark and Delta Lake version mismatches?▼

Preventing broken generated code from Spark and Delta Lake version mismatches requires enforcing minimum version floor constraints from curated import overlays. This Skill checks library versions against canonical import rules and blocks writes when floor violations or unresolved versions are detected.

How do library version verification tools handle approval gates before modifying a catalogue?▼

Library version verification tools handle approval gates by generating a diff of version changes, breaking change notes, and floor violations. This Skill requests explicit user approval before applying any updates to the pinned version catalogue.

Does this approach work with checking Airflow and DuckDB versions across different upstream sources?▼

Yes, this approach works with checking Airflow and DuckDB versions by resolving the latest releases across PyPI, GitHub releases, and Context7. It automatically detects stale or outdated pins for these data stack libraries.

Why does updating library versions block writes when a minimum version floor violation is detected?▼

Updating library versions blocks writes when a floor violation is detected to prevent runtime failures caused by incompatible dependencies. This safety mechanism ensures generated code adheres to curated import rules and minimum version constraints before any catalogue modifications are committed.

When do I need to run library version verification on my pattern handbook?▼

You need to run library version verification on your pattern handbook when you want to ensure pinned versions for complex stacks like Spark and DuckDB are current. It eliminates manual cross-referencing and logs refresh events to track outdated dependencies.

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