name: error-fixer description: | Fix JavaScript and HTTP errors flagged for AI in the admin dashboard. Queries the database for errors with ai_status='flagged_for_ai', analyzes them, and attempts to fix them.

Use when:

• User says "fix errors" or "fix flagged errors"
• User says "check error queue"
• User mentions AI error queue or AI-flagged errors
• After flagging errors in admin dashboard

Triggers: "fix errors", "error queue", "ai errors", "flagged errors", "fix ai errors", "/fix-errors"

Error Fixer

Fetch and fix errors flagged for AI correction in the admin dashboard. Supports both JS errors (error_reports) and HTTP errors (http_error_logs).

Workflow

1. FETCH    → Get AI-flagged errors from both tables
2. ANALYZE  → Understand each error (stack trace, context, URL, status)
3. FIX      → Apply fixes using systematic debugging
4. MARK     → Mark errors as ai_fixed with notes
5. REPORT   → Summarize what was fixed

Step 1: Fetch Flagged Errors

Query both error tables using Supabase MCP:

JS Errors:

SELECT id, error_type, error_message, stack_trace, context, user_action, ai_prompt, created_at
FROM error_reports WHERE ai_status = 'flagged_for_ai'

HTTP Errors:

SELECT id, method, url, status_code, response_body, request_context, navigation_path, ai_prompt, created_at
FROM http_error_logs WHERE ai_status = 'flagged_for_ai'

Or use RPC functions: get_errors_for_ai() and get_http_errors_for_ai().

Important: The ai_prompt field contains specific instructions from the admin.

Step 2: Analyze Each Error

JS Errors

1. Read the ai_prompt - Admin's instruction
2. Parse stack trace - File path, line number, call chain
3. Check context - route, action, other data
4. Read affected file(s)

HTTP Errors

1. Read the ai_prompt - Admin's instruction
2. Check URL and status code - What endpoint failed and why
3. Review response_body - Error message from server
4. Check navigation_path - User's journey to this error
5. Find the code - Locate fetch/API call that made this request

Step 3: Fix Using Systematic Debugging

DO NOT GUESS. Follow systematic-debugging:

1. Understand root cause - Don't patch symptoms
2. Find the actual source - Trace back through stack/code
3. Make minimal fix - One change at a time
4. Verify locally - Run build/tests after fix

Common JS Error Patterns

Common HTTP Error Patterns

Step 4: Mark as Fixed

JS Errors: Call mark_error_ai_fixed(p_id, p_fix_notes)

HTTP Errors: Call mark_http_error_ai_fixed(p_id, p_fix_notes)

Good fix notes examples:

• "Added null check for user object in ProfileCard.tsx:45"
• "Fixed RLS policy for authenticated users on topics table"
• "Added missing CORS header to edge function"

Step 5: Report Summary

## Error Fix Summary

### JS Errors
#### Fixed (X)
- [error_type] in file.tsx:line - Brief description

#### Could Not Fix (X)
- [error_type] - Reason

### HTTP Errors
#### Fixed (X)
- [status_code] [method] /path - Brief description

#### Could Not Fix (X)
- [status_code] [method] /path - Reason

Database Schema Reference

-- error_reports (JS errors)
ai_status: 'pending' | 'flagged_for_ai' | 'ai_fixed' | 'verified'
ai_prompt: text
ai_fixed_at: timestamp
ai_fix_notes: text

-- http_error_logs (HTTP errors)
ai_status: 'pending' | 'flagged_for_ai' | 'ai_fixed' | 'verified'
ai_prompt: text
ai_fixed_at: timestamp
ai_fix_notes: text

-- RPC functions
get_errors_for_ai()              -- JS errors flagged for AI
get_http_errors_for_ai()         -- HTTP errors flagged for AI
mark_error_ai_fixed(p_id, p_fix_notes)       -- Mark JS error fixed
mark_http_error_ai_fixed(p_id, p_fix_notes)  -- Mark HTTP error fixed

Best Practices

1. Always read ai_prompt first - Admin may have specific instructions
2. Don't fix what you don't understand - Mark as "could not fix"
3. Run build after fixes - Verify no new errors
4. Write clear fix notes - Help admin understand what changed
5. Group related errors - Multiple errors may have same root cause
6. Check Edge Function logs - For HTTP 500 errors, use Supabase MCP get_logs