architectural-patterns-large-react
Establish scalable architecture using modular boundaries, domain-driven design, and consistent data access patterns.
Best use case
architectural-patterns-large-react is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Establish scalable architecture using modular boundaries, domain-driven design, and consistent data access patterns.
Teams using architectural-patterns-large-react should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/architectural-patterns-large-react/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How architectural-patterns-large-react Compares
| Feature / Agent | architectural-patterns-large-react | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Establish scalable architecture using modular boundaries, domain-driven design, and consistent data access patterns.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Architectural Patterns for Large React 18 Systems ## Summary Establish scalable architecture using modular boundaries, domain-driven design, and consistent data access patterns. ## Key Capabilities - Define domain modules with strict dependency rules. - Implement façade services to stabilize API contracts. - Enforce architectural constraints via tooling and lint rules. ## PhD-Level Challenges - Prove architectural consistency under codebase growth. - Analyze coupling metrics and refactor for minimal entropy. - Build formal dependency graphs and detect cycles. ## Acceptance Criteria - Deliver a module dependency map and constraint rules. - Provide a refactor plan with quantified coupling reductions. - Demonstrate stable data-access patterns across modules.
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