OhMyCode: Skill for Claude Code

Customize OhMyCode's System Prompt

Change the AI assistant's personality, behavior rules, or domain expertise without writing code.

When to Use

• User says "make it more formal", "act like a senior engineer", "focus on Python"
• User wants to add custom instructions or constraints
• User wants the AI to have domain-specific knowledge

Three Ways to Customize

Method 1: OHMYCODE.md (Per-Project, Recommended)

Create a OHMYCODE.md (or CLAUDE.md) file in your project root:

# Project Instructions

You are working on a Django REST API project.

## Rules
- Always use type hints
- Prefer class-based views over function-based
- Write docstrings for all public functions
- Use Black formatting style

## Tech Stack
- Python 3.12, Django 5.0, PostgreSQL
- pytest for testing, factory_boy for fixtures

OhMyCode auto-detects this file and injects it into the system prompt.

Scope: Only applies when running ohmycode in this directory.

Method 2: system_prompt_append (Global Config)

Add to ~/.ohmycode/config.json:

{
  "system_prompt_append": "Always respond in Chinese. Prefer simple solutions over clever ones. Never use global variables."
}

Scope: Applies to ALL conversations.

Method 3: Modify system_prompt.py (Advanced)

Edit ohmycode/core/system_prompt.py → build_system_prompt() to change the base role description or add new sections.

Scope: Permanent change to the codebase.

Examples

Friendly Tutor

# OHMYCODE.md
You are a patient coding tutor. Explain concepts step by step.
When the user makes a mistake, explain why it's wrong before showing the fix.
Use analogies to explain complex concepts.

Strict Code Reviewer

# OHMYCODE.md
You are a strict senior engineer doing code review.
Point out every issue: bugs, performance, readability, security.
Don't write code for the user — give them guidance to fix it themselves.
Rate code quality on a 1-10 scale.

Domain Expert

# OHMYCODE.md
You are an expert in machine learning with PyTorch.
When suggesting solutions, prefer PyTorch over TensorFlow.
Always consider GPU memory usage and training efficiency.
Reference relevant papers when explaining algorithms.