Modular Code Organization
SkillAI & modelsHelps your agent organize code into clean, reusable modules.
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Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Modular Code Organization skill
About this capability
Modular Code Organization
What this skill tells your AI
The instructions your AI receives, as published by parcadei/continuous-claude-v3 in .claude/skills/modular-code/SKILL.md and read by ahel’s review.
Write modular Python code with files sized for maintainability and AI-assisted development.
File Size Guidelines
| Lines | Status | Action |
|---|---|---|
| 150-500 | Optimal | Sweet spot for AI code editors and human comprehension |
| 500-1000 | Large | Look for natural split points |
| 1000-2000 | Too large | Refactor into focused modules |
| 2000+ | Critical | Must split - causes tooling issues and cognitive overload |
When to Split
Split when ANY of these apply:
- File exceeds 500 lines
- Multiple unrelated concerns in same file
- Scroll fatigue finding functions
- Tests for the file are hard to organize
- AI tools truncate or miss context
How to Split
Natural Split Points
- By domain concept:
auth.py→auth/login.py,auth/tokens.py,auth/permissions.py - By abstraction layer: Separate interface from implementation
- By data type: Group operations on related data structures
- By I/O boundary: Isolate database, API, file operations
Package Structure
feature/
├── __init__.py # Keep minimal, just exports
├── core.py # Main logic (under 500 lines)
├── models.py # Data structures
├── handlers.py # I/O and side effects
└── utils.py # Pure helper functions
DO
- Use meaningful module names (
data_storage.pynotutils2.py) - Keep
__init__.pyfiles minimal or empty - Group related functions together
- Isolate pure functions from side effects
- Use snake_case for module names
DON'T
- Split files arbitrarily by line count alone
- Create single-function modules
- Over-modularize into "package hell"
- Use dots or special characters in module names
- Hide dependencies with "magic" imports
Refactoring Large Files
When splitting an existing large file:
- Identify clusters: Find groups of related functions
- Extract incrementally: Move one cluster at a time
- Update imports: Fix all import statements
- Run tests: Verify nothing broke after each move
- Document: Update any references to old locations
Current Codebase Candidates
Files over 2000 lines that need attention:
- Math compute modules (scipy, mpmath, numpy) - domain-specific, may be acceptable
- patterns.py - consider splitting by pattern type
- memory_backfill.py - consider splitting by operation type
Sources
Signals
- GitHub stars
- 4k
- Forks
- 300
- Last commit
- Jan 2026
Advanced
- Catalog kind
- skill
- Gateway key
modular-code- Source
- github.com/parcadei/continuous-claude-v3