Does this skill affect the actual code Claude writes?

It primarily affects the explanation style and length of the surrounding dialogue, ensuring you get the right amount of context for your code.

What is the difference between Technical and Humanized modes?

Technical mode provides deep code details and implementation specifics, while Humanized mode uses plain language and minimal bullets for maximum brevity.

Can I change my response style after the initial setup?

Yes, the skill is designed to allow you to update or change your preferred style at any time during your interaction.

Why is this useful for quantitative research?

In complex environments like AERA, it helps researchers toggle between dense algorithmic explanations and quick status updates.

Where are my response preferences saved?

Your choices are stored locally in your project directory at .agent-docs/memory/USER_PREFERENCES.md, ensuring they persist across sessions.

Response Style Selector

Name: Response Style Selector
Author: CheekyCodexConjurer

byCheekyCodexConjurer

Productivity & Workflow

Customizes and persists the AI's interaction style between technical and humanized modes to streamline developer communication.

About

The Response Style Selector is a utility for Claude Code that allows developers to define exactly how they want information presented during their coding sessions. By offering a choice between deep technical detail and concise, 'humanized' summaries, the skill ensures that the AI's output aligns with the user's current cognitive load and project requirements. It automatically remembers preferences in local documentation, reducing repetitive formatting requests and creating a more efficient, personalized development workflow within the quant-lab ecosystem.

Key Features

Interactive selection between Technical and Humanized modes
On-the-fly style switching for dynamic workflow needs
Persistent preference storage in local project memory
Concise output optimization to minimize information noise
Automated response adaptation based on saved settings
0 GitHub stars

Use Cases

Enabling Technical mode for deep-dive debugging and detailed implementation patterns
Standardizing AI communication styles across local quantitative research environments
Switching to Humanized mode for high-level project overviews with minimal code clutter

About

Key Features

Interactive selection between Technical and Humanized modes
On-the-fly style switching for dynamic workflow needs
Persistent preference storage in local project memory
Concise output optimization to minimize information noise
Automated response adaptation based on saved settings
0 GitHub stars

Use Cases

Enabling Technical mode for deep-dive debugging and detailed implementation patterns
Standardizing AI communication styles across local quantitative research environments
Switching to Humanized mode for high-level project overviews with minimal code clutter