gui-agent-dev

Automated development workflow for GUI automation agents - analyzes task, explores codebase, drafts solutions, implements, reviews, and generates test plan

Use this agent when adding new action types to the GUI agent, extending InputController capabilities, or designing new automation actions. Examples: <example> Context: User wants to add drag-and-drop support user: "I need to add a drag action for drag-and-drop operations" assistant: "I'll use the action-designer agent to help design the drag action with proper schema and implementation." <commentary> Adding new action types requires careful schema design and InputController integration. </commentary> </example> <example> Context: User wants to handle a specific UI pattern user: "How can I make the agent handle dropdown menus better?" assistant: "Let me use the action-designer agent to analyze if we need new actions or can improve existing ones for dropdown handling." <commentary> UI pattern handling may require new actions or combinations of existing ones. </commentary> </example> <example> Context: User is extending the ActionType enum user: "I'm adding a new action type, what do I need to consider?" assistant: "I'll help you design the complete action with the action-designer agent - schema, implementation, and prompt updates." <commentary> New action types need coordinated changes across multiple components. </commentary> </example>

Designs feature architectures by analyzing existing codebase patterns and conventions, then providing 2-3 solution options with implementation blueprints for user selection

Deeply analyzes existing codebase features by tracing execution paths, mapping architecture layers, understanding patterns and abstractions, and documenting dependencies to inform new development

Reviews code for bugs, logic errors, security vulnerabilities, code quality issues, and adherence to project conventions, using confidence-based filtering to report only high-priority issues

Use this agent when working on context management, optimizing token usage, improving history compression, or debugging KV-cache efficiency. Examples: <example> Context: User is concerned about token costs or context window limits user: "The agent is using too many tokens, tasks are getting expensive" assistant: "I'll use the context-optimizer agent to analyze your token usage and suggest optimizations." <commentary> Token optimization is a core concern for long-running GUI automation tasks. </commentary> </example> <example> Context: User is working on context_manager.py user: "How can I make the compression more efficient?" assistant: "Let me analyze your context management with the context-optimizer agent to identify compression improvements." <commentary> Direct work on context management code benefits from specialized optimization analysis. </commentary> </example> <example> Context: User notices the agent forgetting earlier actions user: "The agent seems to forget what it did earlier in long tasks" assistant: "I'll use the context-optimizer agent to analyze how history is being managed and compressed." <commentary> Memory/history issues are context management problems requiring specialized analysis. </commentary> </example>

This skill should be used when the user asks to "review code", "code review", "check my changes", "review this PR", "review my implementation", "PR review", "look at my code", or when reviewing GUI automation code, vision agent code, or PyAutoGUI-based automation. Provides domain-specific review criteria covering prompts, debugging, context management, action design, and coordinate systems.