How RepoBird Prompts Work
📅 Published on: 2025-08-01👤 By: RepoBirdBot
prompts
AI agents
RepoBirdBot
XML structure
GitHub issues
pull requests
prompt engineering
AI context
trigger sources
code generation
Understanding RepoBird's AI Prompting
RepoBird uses carefully structured prompts to ensure our AI agents deliver consistent, high-quality code solutions. Here's how our prompting system works behind the scenes.
The RepoBirdBot Identity
Every RepoBird agent introduces itself as RepoBirdBot, a professional AI coding agent that:
- Thoroughly researches codebases before making changes
- Follows existing project patterns and conventions
- Tests solutions comprehensively
- Works in a secure Debian environment with modern development tools
Prompt Structure
RepoBird uses XML-structured prompts for clarity and consistency. Each prompt includes:
1. System Context
Establishes the agent's identity, working environment, and professional approach.
2. Task Information
Depending on how you trigger RepoBird, the prompt structure adapts:
From GitHub Issues:
- Issue title and description
- Additional context (if provided)
From Pull Request Comments:
- Your specific request
- PR title and description for context
From Dashboard UI:
- Task title and description
- Any additional context you provide
Prompt Examples
Example: PR Comment Request
<system>
RepoBirdBot introduction and approach...
</system>
<user_request>
Please add unit tests for the new authentication module
</user_request>
<pr_title>
feat: Add OAuth2 authentication support
</pr_title>
<pr_description>
This PR adds OAuth2 authentication...
</pr_description>
Example: GitHub Issue
<system>
RepoBirdBot introduction and approach...
</system>
<issue_title>
Fix email validation in login form
</issue_title>
<issue_description>
The login form is not validating email addresses correctly...
</issue_description>
<context>
Reported by multiple users. High priority bug.
</context>
How This Benefits You
- Consistency: Every run follows the same structured approach
- Context Awareness: The agent understands where requests come from
- Professional Results: Clear structure leads to better code quality
- Transparency: You know exactly what information the agent receives
Tips for Better Results
- Be Specific: Clear, detailed descriptions lead to better solutions
- Provide Context: Additional context helps the agent understand priorities