Does it integrate with my existing CI/CD?

Yes, a core principle of this skill is 'Automate Enforcement,' which includes setting up linters and CI blocks to prevent non-compliant code from merging.

How is the ROI of a file determined?

It uses a specific formula factoring in user impact, the number of error sites, time investment, and the expected project lifespan to categorize files into Tiers.

What languages does this skill support?

The skill provides validated patterns and support for Go, Python, JavaScript, and Rust, with 80-90% transferability across most modern programming languages.

How does this improve debugging speed?

By enforcing a 'Context is King' principle, it ensures errors include operations, resource IDs, and guidance, which has been shown to reduce diagnosis time by 60-75%.

Can I use this on a small project?

While it works on any size, it is most effective for codebases larger than 1,000 lines of code where manual pattern enforcement becomes difficult to scale.

Cross-Cutting Concerns Standardizer

Name: Cross-Cutting Concerns Standardizer
Author: yaleh

byyaleh

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배포 및 DevOps

Standardizes error handling, logging, and configuration across codebases using pattern extraction and automated CI enforcement.

소개

This skill provides a systematic methodology for identifying and unifying inconsistent patterns in complex codebases. It focuses on three critical areas—error handling, logging, and configuration—using a value-driven framework that prioritizes high-impact files to ensure maximum return on investment. By automating detection and integrating compliance into CI/CD pipelines, it helps engineering teams maintain consistent coding standards, reduce debugging time by up to 75%, and scale development without technical debt accumulation.

주요 기능

Pattern extraction and inventory across Go, Python, JavaScript, and Rust
15 GitHub stars
Centralized configuration management and environment variable validation
Context-rich error handling standardization for faster incident diagnosis
Automated linter development and CI/CD integration for enforcement
ROI-based file tier prioritization framework for efficient refactoring

사용 사례

Preparing a repository for team scaling by enforcing unified coding conventions
Improving observability and debugging speed through structured context enrichment
Standardizing inconsistent error handling and logging in legacy codebases

소개

주요 기능

Pattern extraction and inventory across Go, Python, JavaScript, and Rust
15 GitHub stars
Centralized configuration management and environment variable validation
Context-rich error handling standardization for faster incident diagnosis
Automated linter development and CI/CD integration for enforcement
ROI-based file tier prioritization framework for efficient refactoring

사용 사례

Preparing a repository for team scaling by enforcing unified coding conventions
Improving observability and debugging speed through structured context enrichment
Standardizing inconsistent error handling and logging in legacy codebases