name: project-arch-core description: Language-agnostic project architecture principles for AI-assisted development. Use when creating or reviewing language-specific arch skills, or when evaluating whether a codebase needs restructuring. Not a substitute for language-specific skills — provides the shared foundation they build on.

Project Architecture Core — AI-Friendly Development

Language-agnostic architecture principles that keep codebases small enough for AI agents to read and edit efficiently.

When to Apply

✅ Apply when:

• Building a web service, API, or full-stack app with multiple files
• Codebase has grown past ~500 lines total
• AI agent is hitting context limits or making edit errors on large files
• Creating a new <lang>-project-arch skill

❌ Don't apply when:

• One-off scripts or utilities under ~200 lines — just keep it in one file
• Prototyping / POC phase — premature splitting slows you down, refactor later
• Framework has strong conventions (Next.js app router, Django apps, Rails) — follow the framework first, apply these principles where the framework is silent
• Pure libraries with no runtime (config externalization / admin dashboard don't apply)

Core Principles

1. File Size Limits

• Source file ≤ 400 lines — beyond this, AI context usage spikes and edit accuracy drops
• Entry point ≤ 100 lines — only wiring/mounting, zero business logic
• HTML/template files ≤ 200 lines — skeleton only, logic in separate files

2. Entry Point = Wiring Only

The main entry file does exactly three things:

1. Initialize dependencies (DB, middleware, plugins)
2. Mount modules / register handlers
3. Start the process

No business logic, no handler implementations, no data processing.

3. Config Externalization

• All tunable values in a config file (config.json, .env, config.yaml, etc.)
• Loaded at runtime, never hardcoded
• Editable without code changes or restarts (hot-reload preferred for web services)
• Secrets stripped before exposing to frontend/clients

4. Layered Separation

Separate interface (how users/systems talk to you) from logic (what you do) from data (where you store it):

Entry (wiring) → Interface → Logic → Data

Common mappings:

5. Observability & Admin

For web services and long-running processes:

• Auth-protected admin interface for config editing + hot-reload
• Runtime stats (uptime, memory, request counts)
• Key business metrics
• Operation log

For CLIs and libraries: not required. Use logging and --verbose flags instead.

6. Frontend Splitting (if applicable)

• HTML = structure only (no inline JS/CSS beyond minimal bootstrap)
• CSS in separate files
• JS split by responsibility (each ≤ 400 lines)

Splitting Decision Guide

See references/splitting-guide.md for a language-agnostic decision flow:

• When to split vs. when to leave alone
• How to identify split boundaries
• Post-split verification checklist

Why This Matters for AI

Splitting files → 10-15 productive AI rounds vs 3-5 with monoliths. 70-93% token savings per file read.

Language-Specific Skills

This skill provides the universal foundation. For concrete directory structures, code patterns, and framework-specific rules, use the appropriate language skill:

Creating a New Language Skill

If you're building a <lang>-project-arch skill, follow these core principles and add:

1. Concrete directory structure — not abstract, copy-pasteable
2. Entry point pattern — actual code showing wiring-only entry
3. Config pattern — language-idiomatic config loading + hot-reload
4. Splitting rules table — "this monolith → these modules" with max lines
5. Project type references — separate files for different project types

Keep the same naming convention: <lang>-project-arch with references/ subdirectory.

Quality Checklist for Language Skills

Before publishing, verify your skill passes these:

• Can a developer copy the directory structure and start coding immediately?
• Entry point example is real, runnable code (not pseudocode)?
• Config pattern uses the language's idiomatic approach (not a generic JSON example)?
• Splitting rules table covers the 2-3 most common monolith patterns in that ecosystem?
• At least 2 project type references (e.g., web app + CLI, or API + worker)?
• Tested on a real project — not just theoretical?