AI Tools
PyBreeze integrates several AI-powered tools for code review, prompt engineering, and LLM interaction. All AI tools are accessible from the Tools menu and can be opened as either tabs or dock widgets.
AI Code-Review Client
Menu: Tools > AI Code-Review Tab / AI Code-Review Dock
A client for sending code to an AI API endpoint for automated code review.
Interface Layout
URL Input – Enter the API endpoint URL
Method Selector – Choose HTTP method (GET, POST, PUT, DELETE)
Code Input (left panel) – Paste or write code to be reviewed
Response Display (right panel, read-only) – Shows the AI review response
Send Request button – Sends the code to the API endpoint
Features
Tracks accept/reject statistics for AI responses
Saves URL history to
.pybreeze/urls.txtStores response statistics in
.pybreeze/response_stats.txt
Usage
Enter your AI API endpoint URL in the URL input field
Select the HTTP method (typically POST)
Paste the code you want reviewed in the left panel
Click Send Request
Review the AI’s response in the right panel
CoT Code Review GUI
Menu: Tools > AI Code-Review Tab / Dock
An advanced code review tool using Chain-of-Thought (CoT) prompting for more structured and detailed reviews.
Interface Layout
API URL Input – Enter the API endpoint URL
Code Area – Paste code for review
Response Selector (ComboBox) – Browse through multiple review responses
Response Viewer (read-only) – Displays the selected review response
Send Button – Sends code for review
Features
Supports reviewing multiple files at once via
SenderThreadBackground threading prevents UI freezing during API calls
Multiple responses can be stored and browsed
CoT Prompt Editor
Menu: Tools > CoT Prompt Editor Tab / CoT Prompt Editor Dock
A template-based editor for creating and managing Chain-of-Thought prompt templates.
Interface Layout
File Selector (ComboBox) – Select from available prompt template files
Edit Panel (QTextEdit) – Edit the selected prompt template
Create button – Creates a new prompt template file
Save button – Saves changes to the current template
Reload button – Reloads the template from disk
Features
Template-based file management with
COT_TEMPLATE_RELATIONmappingFile system watcher for detecting external changes
Auto-reloads templates when modified outside the editor
Pre-configured templates for common CoT review patterns
Usage
Select a template from the dropdown or create a new one
Edit the prompt template in the text area
Click Save to persist your changes
The template can then be used in the CoT Code Review GUI
Skill Prompt Editor
Menu: Tools > Skill Prompt Editor Tab / Skill Prompt Editor Dock
Similar to the CoT Prompt Editor, but specialized for skill-based prompt templates such as code review and code explanation prompts.
Interface Layout
File Selector (ComboBox) – Select from available skill prompt templates
Edit Panel (QTextEdit) – Edit the selected skill prompt
Create button – Creates a new skill prompt template
Save button – Saves changes
Reload button – Reloads from disk
Pre-built Skill Templates
Code Review prompts
Code Explanation prompts
Skills Send GUI
Menu: Tools > Skill Send GUI Tab / Skill Prompt Dock
An interface for sending skill-based prompts to an LLM API and viewing responses.
Interface Layout
API URL Input – Enter the LLM API endpoint URL
Prompt Template Selector (ComboBox) – Choose a pre-defined skill prompt template
Prompt Text Area – Edit or customize the prompt before sending
Send Button – Sends the prompt to the API (runs in background thread)
Response Display (read-only) – Shows the LLM response
Features
Background threading via
RequestThreadprevents UI freezingError handling with specific HTTP status code messages
Prompt templates are loaded from the Skill Prompt Editor’s template files
Usage
Enter your LLM API endpoint URL
Select a prompt template from the dropdown
Customize the prompt text if needed (e.g., paste code to review)
Click Send
Wait for the response to appear in the response display area
Note
All AI tools require a compatible API endpoint. Configure your API URL to point to your LLM service (e.g., OpenAI-compatible API, local LLM server, etc.).