What’s up, developers? Still playing the basic “Q&A” game with your AI?
That’s old news.
Today, I’m going to show you a “nuclear weapon” that can revolutionize your programming workflow: the new Output Styles feature in Claude Code. In simple terms, it lets you inject a “soul” into your AI, transforming it from a mere tool into any “character” you desire.
Imagine your AI is no longer just a “tool” that mechanically spits out code. It can instantly become a “Senior Architect” to help you untangle legacy code, and the next moment, it’s a “TDD fanatic” guiding you to write the most robust test cases.
Feeling the excitement? This isn’t just about programming assistance anymore; it’s about building your own “AI dream team.”
1. Output Styles: Installing a “Personality” in Your AI
First, let’s break down what this magical “Output Styles” feature is all about.
Simply put, it’s an “AI personality switcher.”
With a traditional AI, you give it a prompt, and it gives you an answer, like a fixed-function calculator. Output Styles, however, allows you to directly modify Claude Code’s “System Prompt,” fundamentally changing its behavior patterns and tone of voice.
It’s like you’re not just giving commands to your Gundam, but directly modifying its cockpit operating system. You can keep its core combat power (like writing code and manipulating files) but completely change its fighting style.
2. The Official Trio: Three Preset Personas
Claude provides three “standard personas” to get you started quickly:
1. Default Persona This is the familiar, concise “programming special agent.” Not much talk, just high-quality work. Designed for efficient collaboration, it gets straight to the point with no fluff.
2. Explanatory Persona This one is like that experienced and patient tech lead you know. While completing tasks, it intersperses deep insights on “design philosophy” and “technology choices,” helping you understand not just the “what” but also the “why.”
3. Learning Persona
This is a godsend for newcomers. It acts like a pair-programming coach, teaching as it goes. It might even intentionally leave TODO(human) in the code for you to complete, then provide feedback. It truly creates an immersive “learn-by-doing” experience.
3. How to Summon and Switch Personas
Switching between these personas is as easy as changing skins in a game.
To see the available options, just type in your terminal:
/output-style
Want to summon a specific “master”? For instance, to call upon the “Explanatory Persona”:
/output-style explanatory
Or switch to the “Learning Persona”:
/output-style learning
These settings are saved locally in your project’s .claude/settings.local.json file. Set it once, and it applies to the entire project—super convenient.
4. The Ultimate Move: Create Your Own “AI Deity”
Bored with the presets? Here comes the main event: creating your own AI personality!
This is where Output Styles becomes truly powerful. You can use natural language, like writing a character bio for a novel, to define a brand-new AI style.
For example, if you want to create a “Code Security Auditor”:
/output-style:new I want a strict security audit style: first threat modeling, then static/dependency/config audit, outputting CWE mapping, a draft PR for fixes, and a local script.
With this command, Claude will generate a new AI persona for you. This “personality” file is saved as a Markdown file in the ~/.claude/output-styles directory. You can even place it in the project-level .claude/output-styles directory to share your created “AI expert” with the entire team.
5. In Action! Let the AI “Flex” in Real Scenarios
Talk is cheap. Let’s look at some real-world examples you can apply directly to your work.
Case A: Dropped into a “Code Hell” Project? Let the “Mentor” Draw You a Map
Just took over a five-year-old legacy codebase and have no idea where to start? Don’t panic.
Summon Persona:
/output-style explanatory
Give the Command:
“Please perform a systematic review of the
services/order/directory. Output key points for an architecture diagram, common anti-patterns, and give me 3 refactoring roadmaps.”
AI Output: It will analyze the code structure for you while interspersing “Insights” to explain the design rationale and the technical trade-offs behind it. This isn’t just an AI; it’s like hiring an architect to do a code consultation for you.
Case B: New Team Member Onboarding? Let the “Coach” Help Them Soar
A new developer joined the team, but you don’t have time to mentor them personally? Let them pair-program with the AI.
Summon Persona:
/output-style learning
Give the Command:
“We need to add a bulk import feature to
UserService. Let’s do it TDD-style: please write the failing test case first, then leave aTODO(human)in the implementation for me to complete the key logic.”
AI Output: It will strictly follow the TDD process, first giving you a failing test (red), then setting up the framework in the business logic, leaving the core part for you to fill in. After the newcomer finishes, the AI will automatically review and provide suggestions for improvement. With this workflow, growth is almost guaranteed.
Case C: Product Manager Rushing a Feature? Let the “PM” Handle Them
Sometimes, you need an AI that understands product management to help you evaluate requirements.
Create Persona:
/output-style:new I want a PM-oriented style: emphasize user stories, impact assessment, success metrics, edge cases, and rollback plans. Produce a concise review conclusion.
Give the Command:
“Review this refactoring PR: add user stories, metrics, risks, and a canary release strategy. Provide a go/no-go conclusion.”
AI Output: It will act like a seasoned product manager, generating a structured review report from multiple perspectives—user value, data metrics, risk control, etc.—that you can paste directly into the PR comments. Professional!
6. Integrate AI Output into Your Automation Pipelines
Even better, you can integrate these powerful capabilities into your CI/CD or other scripts.
Want the AI’s analysis results in plain text?
claude -p 'summarize this data' --output-format text > summary.txt
Want the AI’s code analysis report directly in JSON format?
claude -p 'analyze this code for bugs' --output-format json > analysis.json
This opens up a world of possibilities for implementing more complex AI Agent workflows.
Conclusion: This Isn’t Just a Feature, It’s a Revolution
Claude Code’s Output Styles may seem like just another new feature, but it represents a significant trend: we are moving from “using AI” to “shaping AI.”
We are no longer content with the answers AI gives us; we are starting to actively define “who” the AI should be.
This transforms AI from a cold, standardized tool into personalized, virtual team members that we can “inject a soul” into. This is the true future of AI collaboration.
Don’t hesitate. Go try it now and create your very first “AI master.”
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