Enhance AI Agent Detection: Aider, Cline, Windsurf, Crush

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Enhance AI Agent Detection: Aider, Cline, Windsurf, Crush

Hey guys! Today we're diving into an important update for ai-aligned-gh to boost its AI agent detection capabilities. Currently, ai-aligned-gh doesn't recognize a few key AI coding agents that other AI Ecoverse tools already support. Let's get into the details and see how we can improve this!

Summary

Our gap analysis reveals that ai-aligned-gh is missing detection for several AI coding agents that are already supported by other AI Ecoverse tools. Addressing this will bring more consistency and transparency to our AI workflows.

Current State

Currently, ai-aligned-gh can detect the following AI agents:

  • Amp
  • Codex
  • Claude
  • Gemini
  • Qwen
  • Droid
  • OpenCode
  • Cursor
  • Kimi
  • Copilot

But, as you can see, some important players are missing from the list. Let's highlight which agents need to be added and why.

Missing Agents

1. Aider

  • Status: Supported by YOLO
  • Impact: Without proper detection, Aider's GitHub CLI operations won't have the correct bot attribution. This means actions performed by Aider might not be properly identified, causing confusion and hindering accountability.
  • Detection: We can detect Aider by checking for the environment variable AIDER_CLI or by matching the process name.

The importance of proper attribution cannot be overstated. When Aider makes changes, we want it clearly marked as "Aider [bot] on behalf of @user." This level of detail helps teams understand who initiated specific actions and maintains a clear audit trail. By implementing this detection, we ensure that all Aider-driven GitHub CLI operations are correctly attributed, thus avoiding any ambiguity. Furthermore, accurate attribution supports better collaboration and transparency within development teams. When everyone knows which AI tool performed a given action, it fosters trust and allows for more effective monitoring and management of AI-driven processes. The goal is to create an environment where AI tools are seen as valuable assistants, working transparently and accountably alongside human developers. Implementing this simple yet crucial detection mechanism brings us one step closer to achieving that vision. Consider the scenario where Aider is used to apply a complex code refactoring across multiple files. Without proper attribution, it might be challenging to trace the origin of these changes back to the AI tool. This can lead to unnecessary confusion and time spent trying to understand the modifications. However, with the correct attribution, anyone can immediately see that Aider performed the refactoring under the direction of a specific user, providing clarity and context. Additionally, this improved visibility allows for better evaluation of Aider's performance, helping teams fine-tune their AI workflows and maximize efficiency. The benefits extend beyond individual tasks. By consistently attributing AI actions, organizations can build a comprehensive understanding of how AI tools are being used across their projects. This data can be used to identify best practices, optimize resource allocation, and make informed decisions about future AI investments. The overarching objective is to integrate AI seamlessly into the development process, ensuring that it enhances rather than complicates the workflow.

2. Cline

  • Status: Detected by ai-aligned-git
  • Impact: Without detection, Cline's GitHub operations will appear to come directly from the user, which obscures the fact that an AI tool is being used.
  • Detection: Look for the environment variable CLINE_CLI or match the process name.

Transparency is key when integrating AI tools into our workflows. When Cline makes changes to our codebase, it’s vital that we know it. If Cline's GitHub operations appear to come directly from a user, it undermines the transparency we aim for. With proper detection, we ensure that all of Cline's actions are correctly attributed, maintaining a clear audit trail. This prevents confusion and supports more effective team collaboration. Imagine a scenario where Cline is used to automatically update documentation based on recent code changes. If these updates appear to come directly from a user, other team members might be unaware that an AI tool was involved. This could lead to questions about the accuracy and reliability of the documentation. However, with proper detection, everyone can see that Cline performed the updates, allowing them to assess the changes with the appropriate context. Moreover, this transparency fosters trust in the AI tool. When team members understand how Cline is being used and can see its impact on their work, they are more likely to embrace it as a valuable assistant. This can lead to increased adoption of AI tools across the organization, driving further innovation and efficiency. The goal is to create an environment where AI and humans work together seamlessly, with each party's contributions clearly recognized and understood. Detecting Cline also helps in managing permissions and access control. By knowing that Cline is performing certain actions, we can ensure that it has the necessary permissions to access the required resources. This prevents potential security vulnerabilities and ensures that the AI tool is operating within the defined boundaries. The overall objective is to integrate AI tools safely and responsibly, maintaining full visibility and control over their activities.

3. Windsurf

  • Status: Detected by vibe-coded-badge-action
  • Impact: Without detection, Windsurf's GitHub operations lack proper bot attribution, leading to inconsistency in how AI-driven actions are displayed.
  • Detection: Check for the environment variable WINDSURF_AI or match the process name.

Consistency across our AI Ecoverse tools is essential for creating a seamless and reliable experience. vibe-coded-badge-action already detects Windsurf, and we need to ensure that ai-aligned-gh does too. This consistency means that regardless of which tool you're using, Windsurf's actions will always be properly attributed. When Windsurf performs GitHub operations without proper attribution, it can disrupt the flow of information and make it harder to track changes. By detecting Windsurf and attributing its actions correctly, we maintain a clear and consistent view of our AI-driven workflows. This enhances transparency and makes it easier for teams to collaborate effectively. Consider the situation where Windsurf is used to automatically generate code snippets based on natural language descriptions. If these snippets are committed to the repository without proper attribution, it might be unclear who or what created them. This can lead to confusion and hinder the ability to review and understand the code. However, with the correct attribution, it's immediately apparent that Windsurf generated the snippets, providing valuable context. Furthermore, consistency in attribution helps in identifying potential issues and debugging problems. If an AI tool is consistently misattributing its actions, it's easier to spot the problem and take corrective measures. This ensures that our AI tools are working reliably and that their actions are properly documented. The overall objective is to create an AI Ecoverse where all tools work together harmoniously, providing a consistent and transparent experience for users. By ensuring that ai-aligned-gh detects Windsurf, we take a step towards achieving that goal.

4. Crush

  • Status: Detected by ai-aligned-git
  • Impact: Without detection, Crush's GitHub operations won't use bot token exchange, potentially leading to authentication issues and reduced security.
  • Detection: Look for the environment variable CRUSH_CLI or match the process name.

Security is paramount when dealing with AI tools that interact with our repositories. Crush uses bot token exchange for authentication, and without proper detection, this mechanism won't be utilized. This could expose our repositories to potential security vulnerabilities and compromise the integrity of our workflows. By detecting Crush and ensuring that it uses bot token exchange, we enhance the security of our AI-driven operations. This prevents unauthorized access and ensures that only authorized actions are performed. Imagine a scenario where Crush is used to automatically merge pull requests based on predefined criteria. Without proper detection and bot token exchange, there's a risk that unauthorized users could potentially trigger these merges. However, with the correct detection, we can ensure that only authorized actions are performed, preventing any malicious activity. Furthermore, proper authentication helps in tracking and auditing AI-driven actions. By knowing that Crush is using bot token exchange, we can monitor its activities and identify any potential security breaches. This provides an additional layer of security and helps in maintaining the integrity of our repositories. The overall objective is to integrate AI tools securely and responsibly, ensuring that they don't compromise the security of our development workflows. By ensuring that ai-aligned-gh detects Crush and utilizes bot token exchange, we take a significant step towards achieving that goal.

Benefits of Adding Detection

  1. Proper Attribution: All AI tool actions will show as "as-a-bot[bot] on behalf of @user." This makes it clear who or what initiated each action.
  2. Transparency: Users and teams can easily see which AI tool performed specific GitHub actions, fostering trust and understanding.
  3. Ecosystem Consistency: Matches agent support across all AI Ecoverse tools, providing a unified experience.

Suggested Implementation

To add detection, you can modify the detect_ai_tool_from_env() function:

# Aider detection
if [ -n "$AIDER_CLI" ]; then
    detected="$detected aider"
fi

# Cline detection
if [ -n "$CLINE_CLI" ]; then
    detected="$detected cline"
fi

# Windsurf detection
if [ -n "$WINDSURF_AI" ]; then
    detected="$detected windsurf"
fi

# Crush detection
if [ -n "$CRUSH_CLI" ]; then
    detected="$detected crush"
fi

Also, remember to add to process tree detection and update the priority order accordingly.

Context

This is part of the AI Ecoverse consistency initiative. GitHub CLI operations by these agents should have proper bot attribution through the as-a-bot GitHub App. By implementing these changes, we ensure that all AI tool actions are properly attributed, enhancing transparency and security within our AI workflows. Let's make it happen, team!