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Modular Code

parcadei/modular-code

Modular Code Organization

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on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/parcadei/Continuous-Claude-v3 --skill modular-code

The instruction itself

11 sections, as written by the author

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

  • By domain concept: auth.pyauth/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.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:

  • 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

How to use it

Copy the folder

Take parcadei/modular-code from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.