skills.rest
RDEWAI
RDEWAIOFFICIAL
Explore all skills by this author →
GitHub

update-lld

Merge upstream artifacts into Low-Level Design documents while preserving unchanged content.

5|1|Updated Sep 23, 2025
Search Tags:#version control#data engineering#traceability#lld#low-level design#dag validation
One-click install
npx skills add https://github.com/RDEWAI/Redefining-DataEngineering-With-AI --skill update-lld
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: update-lld
Source: https://github.com/RDEWAI/Redefining-DataEngineering-With-AI/tree/main/chapter-6/technical-lead-plugin/skills/update-lld
Command: npx skills add https://github.com/RDEWAI/Redefining-DataEngineering-With-AI --skill update-lld

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Manually updating Low-Level Design documents for data engineering projects is error-prone, often breaks cross-section consistency, loses traceability to upstream design artifacts, and misses proper version tracking and changelog entries. This Skill automates the entire LLD revision workflow to eliminate these risks.

Core Features & Use Cases

  • Upstream Artifact Merging: Seamlessly incorporate changes from upstream design documents (business requirements, high-level design, data model, mapping, data quality specs) into existing LLDs while preserving all unchanged content.
  • Cross-Section Consistency Enforcement: Automatically assesses ripple effects across all 14 mandatory LLD sections (DAG, configuration, deployment, monitoring etc.) when a single section is updated, preventing misalignment.
  • Version Tracking & Validation: Increments LLD version numbers, adds changelog entries, and validates the final document against strict structural and content rules before handoff to development teams.
  • Derived Artifact Regeneration: Automatically triggers updates to related files like DAG definitions, configuration templates, and ingestion runners when corresponding LLD sections are modified.
  • Use Case: For example, if your data architect updates the high-level design with new Spark cluster sizing, this Skill will update the LLD's performance, configuration, and deployment sections, bump the version, and remind you to regenerate the related infrastructure files.

Quick Start

Use the update-lld skill to revise your existing Low-Level Design document with new infrastructure changes, DAG revisions, or updated upstream design artifacts while preserving all unchanged content and maintaining full traceability to source requirements.

Frequently Asked Questions about update-lld

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

FAQPage Schema
How do I update an existing Low-Level Design document without losing traceability to upstream artifacts?▼

To update a Low-Level Design document without losing traceability, merge new upstream artifacts like business requirements and data models while preserving unchanged content. This ensures all 14 mandatory LLD sections remain internally consistent and linked to their source design artifacts.

How do I maintain cross-section consistency in a data engineering LLD when the DAG or configuration changes?▼

Maintaining cross-section consistency in a data engineering LLD requires assessing ripple effects across DAG, configuration, deployment, and monitoring sections when changes occur. Updating one section should automatically trigger validation to prevent misalignment across the document.

What is the best way to track versions and changelogs when revising LLDs for data pipelines?▼

The best way to track versions and changelogs when revising LLDs is to increment version numbers and add detailed changelog entries automatically during the revision. This validates the final document against strict structural rules before development handoff.

Do I need to manually regenerate DAG definitions and ingestion runners after modifying configuration sections in an LLD?▼

You do not need to manually regenerate DAG definitions and ingestion runners after modifying LLD configuration sections. The revision process automatically triggers updates to these derived artifacts when their corresponding design sections are changed.

Can I merge infrastructure changes like Spark cluster sizing into an existing data engineering LLD?▼

You can merge infrastructure changes like Spark cluster sizing into an existing data engineering LLD. The process updates relevant performance, configuration, and deployment sections while bumping the version and reminding you to regenerate related infrastructure files.

What happens to unchanged LLD sections when merging new upstream design artifacts?▼

Unchanged LLD sections remain fully preserved when merging new upstream design artifacts. The revision process targets only the affected areas, ensuring existing content is not overwritten while enforcing traceability and consistency across the complete document.

Related Skills

Explore More Software Engineering Skills→
RDEWAIRDEWAI
5

create-lld

Generate a 14-section Low-Level Design document from approved data engineering artifacts.

Official
Advanced
RDEWAIRDEWAI
5

create-dag

Generate Apache Airflow DAG files from LLD artifacts and pipeline configs.

Official
Advanced
lionbenjaminlionbenjamin
15

analyze

Analyze development specs, plans, and tasks for traceability and gap detection.

Community
Intermediate
eternnoireternnoir
6

update-content

Update knowledge base files while preserving frontmatter and markdown structure.

Community
Basic