Claude Code Output Styles: Making AI Programming Assistants Adapt to Your Learning Pace

Claude Code’s Output Styles feature addresses a very practical problem: How can AI assistants adapt to different learning and working needs while maintaining their core capabilities?

The core idea of this feature is simple—by modifying the system prompt, the same Claude Code can exhibit different “working styles” in different scenarios. It won’t change Claude’s basic abilities to read/write files or run scripts, but it will completely transform how it communicates with you.

Three Built-in Modes, Each with Its Purpose

Claude Code provides three built-in Output Styles to accommodate different scenarios.

1. Default Mode: Efficient Problem Solving

This is the mode most people are accustomed to, designed for efficiently completing software engineering tasks. In this mode, Claude focuses on quickly providing usable code solutions, being concise and direct. Suitable for:

• Emergency bug fixes
• Rapid prototype development
• When you’re familiar with the technology and just need specific implementation

2. Explanatory Mode: Guidance Like a Senior Engineer

In this mode, Claude will insert “Insights” comments in the code while helping you complete tasks, explaining its coding decisions and design trade-offs, helping you understand implementation choices and codebase patterns.

For example:

// Insight: Using Map instead of Object here because Map can use any type as keys,
// and has better performance when frequently adding/removing key-value pairs.
const cache = new Map();

Suitable scenarios:

• Learning new frameworks or languages
• Understanding complex system design
• Code reviews where you need to understand design reasoning

3. Learning Mode: Socratic Teaching

This is the most collaborative “learn-by-doing” mode. Claude won’t give complete code directly, but will:

1. Share “Insights” to explain concepts.
2. Provide code frameworks with TODO(human) markers inserted.
3. Guide you to personally complete 5-10 lines of critical code.

For example, handling array deduplication:

function removeDuplicates(arr) {
  // TODO(human): Use the Set data structure to remove duplicate elements.
  // Hint: You can first new Set(arr), then convert it back to an array.

  return result;
}

This approach is particularly suitable for:

• Learning algorithms and data structures
• Practicing new programming paradigms
• Avoiding the mental laziness brought by “copy-paste learning”

How to Use?

Switching Modes

You can easily switch modes with the following commands:

# Open interactive menu for selection
/output-style

# Switch directly to specified mode
/output-style explanatory
/output-style learning

Settings Persistence

The selected mode applies at the project level and is automatically saved in the project’s .claude/settings.local.json file, automatically applying the next time you open that project.

Custom Modes: Create Your Personal Assistant

If the built-in modes don’t meet your needs, you can also create custom Output Styles:

# Claude will guide you to create a new style
/output-style:new I want a code review expert mode...

Custom Styles are saved as Markdown files, stored in the user directory ~/.claude/output-styles (available to all projects) or project directory .claude/output-styles (current project only). You can share these files among team members to unify collaboration styles.

For example, you can create a “Code Review Expert” mode that focuses on code quality, security, and maintainability, providing specific refactoring suggestions.

Differences from Related Features

Understanding the differences between Output Styles and other similar features is important:

• Output Styles vs. CLAUDE.md: Output Styles completely replace the default system instructions related to software engineering in Claude Code. While CLAUDE.md content is added as a user message after the system prompt.
• Output Styles vs. Agents: Output Styles only affect the main Agent’s system prompt. Agents (sub-agents) are called to handle specific tasks and can contain more complex configurations, such as which model to use, which tools are available, etc.

Why This Feature Matters?

Many developers may encounter “AI dependency syndrome”: becoming accustomed to directly copying AI-generated code, which over time may diminish their independent thinking abilities. Anthropic believes the Learning mode is one way to address this problem.

Output Styles, particularly Learning mode, encourage:

• Active thinking: Instead of giving standard answers directly, it guides you to think.
• Hands-on practice: Forces your participation in coding through TODO(human) markers.
• Interactive learning: Maintains interactive dialogue with AI, avoiding passive acceptance.

This approach may seem slightly slower in the short term, but long-term it helps you truly master skills rather than just becoming a “prompt engineer.”

Dual AI Collaboration: Claude Code + Gemini CLI (Advanced Extension)

An advanced use of Output Styles is combining it with other AI tools to form multi-AI collaborative workflows. Here’s an example of combining Claude Code with Gemini CLI for code reviews.

1. Create “Gemini Code Reviewer” Custom Style

First, create a dedicated custom style that makes Claude Code call Gemini CLI for code analysis:

/output-style:new I want a Gemini code reviewer style that first calls Gemini CLI to analyze code, then provides optimization suggestions based on the analysis results.

In the custom Style instructions, explicitly require Claude to use Gemini CLI.

2. Dual AI Collaboration Workflow

Phase 1: Gemini Analysis When code review is needed, Claude (under the new custom Style) will automatically call Gemini CLI for analysis.

# Claude will automatically run commands similar to the following
gemini -p "Please perform a code review for this file, focusing on code quality, security, and best practices: @$FILE_PATH"

Gemini will analyze from multiple dimensions including code quality, performance, security, and best practices.

Phase 2: Claude Optimization After obtaining Gemini’s analysis results, Claude will:

1. Interpret and categorize: Classify discovered issues by severity.
2. Determine priority: Recommend prioritizing the most important issues.
3. Provide code implementation: Give specific fix code or refactoring solutions.
4. Suggest testing: Recommend corresponding test cases to verify fixes.

Why Dual AI Collaboration is Effective?

Different AI models have their respective strengths. Through this approach, you can combine Gemini’s powerful capabilities in code analysis and pattern recognition with Claude’s advantages in understanding context and generating solutions, thus:

• Improving problem discovery accuracy.
• Reducing cognitive biases of single AI.
• Obtaining more comprehensive code review results.

Claude Code’s Output Styles is not just a feature update; it represents a new mindset: AI assistants shouldn’t make us lazy, but should help us become stronger. The shift from “AI writes code for me” to “AI teaches me to write code” is worth serious consideration by every developer.

References

• Output styles - Anthropic Official Docs
• Blog article reference