parcadei/continuous-claude-v3

modular-code

Modular Code Organization

First seen Jan 22, 2026

Installation

$ npx skills add parcadei/continuous-claude-v3 --skill modular-code

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Repository health

Stars 3.9K
License LICENSE
Default branch main
Open issues 30
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 2,930 B
  • docs SUMMARY.md 45 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 555 installs

SKILL.md

Modular Code Organization

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

  1. By domain concept: auth.pyauth/login.py, auth/tokens.py, auth/permissions.py
  2. By abstraction layer: Separate interface from implementation
  3. By data type: Group operations on related data structures
  4. 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.py not utils2.py)
  • Keep init.py files 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:

  1. Identify clusters: Find groups of related functions
  2. Extract incrementally: Move one cluster at a time
  3. Update imports: Fix all import statements
  4. Run tests: Verify nothing broke after each move
  5. 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