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app-quality-patterns

Provide cross-project quality patterns for LLM-built app workflows.

6|Updated May 29, 2026
Search Tags:#data integrity#acceptance criteria#ui polish#app quality#defect prevention#smoke testing#quality patterns
One-click install
npx skills add https://github.com/fusebase-dev/fusebase-flow --skill app-quality-patterns
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: app-quality-patterns
Source: https://github.com/fusebase-dev/fusebase-flow/tree/main/flow-skills/app-quality-patterns
Command: npx skills add https://github.com/fusebase-dev/fusebase-flow --skill app-quality-patterns

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

LLM-built applications frequently miss critical behavioral quality requirements that lead to recurring, avoidable defects across projects, including broken navigation, orphaned data, and poor UI state handling. This Skill solves that problem by providing a curated, cross-project library of proven quality patterns distilled from real-world defect data.

Core Features & Use Cases

  • Curated Quality Pattern Library: Access categorized quality patterns (QP-xx) covering state & navigation, data integrity, and UI polish, each with clear requirements and copy-ready smoke test recipes.
  • Workflow Integration: Automatically surface applicable patterns during spec drafting, design planning, implementation, and code review to embed quality requirements directly into your existing development workflow.
  • Use Case Example: When building a list view with filters, search, and delete functionality, this Skill automatically identifies relevant patterns like QP-01 (URL reflects view state) and QP-10 (delete cascade policy) to include in your acceptance criteria and smoke tests.

Quick Start

Use the app-quality-patterns skill to identify and cite the relevant quality pattern IDs when drafting acceptance criteria for a new app feature with filters, search, and delete flows.

Frequently Asked Questions about app-quality-patterns

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

FAQPage Schema
How do I prevent recurring app defects like broken navigation and orphaned data in LLM-built applications?▼

To prevent recurring app defects in LLM-built applications, apply a curated library of cross-project quality patterns covering state navigation, data integrity, and UI polish requirements directly into your development workflow.

What are quality patterns for app development and when should I use them?▼

Quality patterns are proven, copy-ready behavioral requirements distilled from real-world defect data that you use during spec drafting, UI planning, feature implementation, and code review to enforce consistent app quality across projects.

How do I write acceptance criteria and smoke test recipes for app features with filters and delete flows?▼

Write acceptance criteria and smoke test recipes by citing specific quality pattern IDs, such as QP-01 for URL view state reflection and QP-10 for delete cascade policy, to cover filters, search, and delete functionality comprehensively.

Can I integrate app quality patterns into my existing code review and spec drafting workflows?▼

Yes, you can integrate app quality patterns into existing code review and spec drafting workflows to automatically surface applicable state navigation, data integrity, and UI polish requirements during your standard development process.

What is the best way to enforce consistent UI state handling and data integrity across multiple app projects?▼

The best way to enforce consistent UI state handling and data integrity across multiple app projects is by applying a standardized library of cross-project quality patterns as acceptance criteria and smoke test recipes.

Why do LLM-built applications frequently miss critical behavioral quality requirements and how can I fix it?▼

LLM-built applications frequently miss critical behavioral quality requirements due to a lack of standardized defect prevention rules, which you can fix by embedding a curated library of proven quality patterns into your feature implementation workflow.

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