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huangjunsen0406
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trellis-check

Run linting, type-checking, and cross-layer data flow analysis before code commits.

77|13|Updated Apr 26, 2025
Search Tags:#testing#architecture#quality assurance#code review#linting#compliance
One-click install
npx skills add https://github.com/huangjunsen0406/UnifyPy --skill trellis-check-huangjunsen0406
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: trellis-check
Source: https://github.com/huangjunsen0406/UnifyPy/tree/main/.claude/skills/trellis-check
Command: npx skills add https://github.com/huangjunsen0406/UnifyPy --skill trellis-check-huangjunsen0406

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This skill addresses the challenge of maintaining code quality and architectural integrity during development by automating verification steps and ensuring cross-layer consistency.

Core Features & Use Cases

  • Comprehensive Verification: Combines linting, type-checking, and unit testing to ensure code reliability.
  • Cross-Layer Data Flow: Validates that data schemas and logic remain consistent across storage, service, API, and UI layers.
  • Use Case: Before committing a major feature, use this skill to verify that your new code passes all project tests, adheres to defined specifications, and maintains proper data flow across the entire application stack.

Quick Start

Run the trellis-check skill to perform a full quality verification on the current codebase and ensure all architectural specs are met.

Frequently Asked Questions about trellis-check

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

FAQPage Schema
How do I ensure cross-layer data flow consistency before committing code?▼

To ensure cross-layer data flow consistency, you can perform comprehensive quality verification that validates data schemas and logic across storage, service, API, and UI layers before code commits.

What is cross-layer data flow analysis in software architecture?▼

Cross-layer data flow analysis is a verification process that validates data schemas and logic remain consistent across storage, service, API, and UI layers to maintain architectural integrity.

How do I automate spec compliance and linting checks for a new feature?▼

Automate spec compliance and linting checks by running a full quality verification skill that combines linting, type-checking, and unit testing to ensure code passes all project tests.

Can I use automated quality verification to catch context drift in my codebase?▼

Yes, automated quality verification targets software development workflows specifically to catch context drift and ensure architectural consistency before code commits are finalized.

Do I need project-specific testing tools to run cross-layer consistency checks?▼

Yes, performing comprehensive quality verification requires the execution of project-specific linting, testing, and documentation tools to validate code integrity and architectural specs.

What's the best way to validate code reliability and architectural specs before a commit?▼

The best way to validate code reliability is to combine linting, type-checking, and unit testing into a comprehensive verification step that ensures all architectural specs are met.

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