<!-- .claude/commands/blog.md -->
You are a product manager tasked with writing a blog post about a given topic.

The topic for your blog post is:<topic>$ARGUMENTS</topic>

Look through the existing project structure for relevant information.

To gather additional information, use the web research agent.

When writing, follow this structure:
1. Introduction: Hook the reader
2. Main body: 2-4 sections exploring different aspects
3. Conclusion: Summarize and provide call to action

Maintain a conversational and engaging tone. Use examples from the actual
codebase where relevant.
When finished, save to the blog/ directory.

---
This is your LinkedIn profile:
<linkedin>[Your or your colleague's linkedin profile.]</linkedin>
---

<example_post>[Example posts for tone and style follow...]<

<!-- .claude/commands/issues.md -->
You are a GitHub issues manager for the Kasava project.
  
Task: $ARGUMENTS
  
Available operations:
- Create new issues with proper labels and assignees
- Search existing issues for duplicates
- Link related issues
- Create issue templates
- Generate issue reports
  
Always check for duplicates before creating new issues.Use our label taxonomy:
- bug: Something isn't working
- enhancement: New feature or request
- documentation: Improvements or additions to documentation
- performance: Performance improvements
- tech-debt: Code refactoring or cleanup

Format issues with:
  
1. Clear, searchable ttles
2. Reproduction steps (for bugs)
3. Expected vs actual behavior
4. Environment details
5. Related PR/issue links

<!-- .claude/commands/think.md -->
Approach this problem methodically:
1. Understand: Restate the problem in your own words
2. Analyze: Break down the components
3. Research: Look through the codebase for similar patterns
4. Design: Propose 2-3 different approaches
5. Evaluate: Compare trade-offs of each approach
6. Recommend: Choose the best approach with justification
7. Plan: Create step-by-step implementation plan
   
Problem: $ARGUMENTS

Take your time. Think through edge cases. Consider performance implications.
Reference existing code patterns where applicable

<!-- .claude/commands/style.md -->
You are tasked with creating a comprehensive style guide for writing code
that future AI prompts should follow.
  
Carefully examine the code in the following directory:
  
<relevant_directory>$ARGUMENTS</relevant_directory>
  
Analyze the code patterns, design choices, and conventions used throughout
the project. Pay attention to:

- Naming conventions (variables, functions, classes)
- Code organization and structure
- Commenting and documentation practices
- Indentation and formatting
- Error handling and logging
- Use of design patterns or architectural styles
- Testing approaches (if visible in the code)
  
Create a document called STYLE_GUIDE.md that outlines the best practices 
and conventions for writing code in this project

<!-- .claude/commands/update.md -->

You are tasked with updating all existing CLAUDE.md files in a project directory
with the current information of the project.

Look at the CLAUDE_FILES_INDEX.md file and identify each file path that has a
updated_at value of more than 7 days ago. Create a subagent for each file.

For each subagent:
  - Analyze the content of the corresponding CLAUDE.md file
  - Compare the existing information with the updated project information
  - Identify any outdated, incorrect, or missing information
  - Update the file content with current project information
  - Maintain the original structure and formatting
  
After all subagents have completed their updates, 
update the relevant files in CLAUDE_FILES_INDEX.md with the current

<!-- .claude/commands/visualize.md -->
Analyzes code and architecture to generate explanatory diagrams.
  
<architecture_location>
  $ARGUMENTS
</architecture_location>
  
**Default Output Location**: `/docs/diagrams/` directory
  
This command transforms complex code, logic, and system architectures
into clear, visual diagrams. 

Generates:
- Code flow visualization (flowcharts)
- Database schema visualization (ERDs)
- System architecture diagrams
- API interaction sequences
- Class and module relationships
- Network and infrastructure diagrams
  
Output formats: Mermaid (default), PlantUML, Graphviz DOT

<!-- .claude/commands/work.md -->
You are an AI assistant tasked with managing and working on GitHub issues.

1. Connect to the preconfigured MCP server for Github
2. List all open issues on the repository (or work on specific issue if provided)
3. Select one to work on (oldest or highest priority)
4. Create a new branch for your changes
5. Work on the issue with clear, tested code
6. Update documentation as needed
7. Commit changes with descriptive messages
8. Push branch and create pull request
9. Clean up workspace when done

Your goal is to look for open issues, select one to work on, and
create a pull request when the work is

<!-- .claude/commands/upgrade.md -->
Analyze the requested upgrade and delegate to the appropriate specialist:

Task: $ARGUMENTS

If this involves:
  - Cloudflare services → Use cloudflare-backend-architect agent
  - Mastra workflows → Use mastra-workflow-architect agent
  - Search/embeddings → Use vector-search-architect agent
  - Frontend React → Use nextjs-frontend-expert agent
  - Chrome extension → Use browser-extension-developer agent
    
Approach:
  1. Identify the domain
  2. Engage the specialist agent
  3. Review their recommendations
  4. Implement with proper testing
  5. Update relevant CLAUDE.md files

<!-- .claude/commands/test.md -->
Run tests for the specified component or feature:
<target>$ARGUMENTS</target>
If no target specified, run all tests.If target is a file path, 
run tests for that file.If target is a feature name, run related test suites