ralph-wiggum-loop

name	ralph-wiggum-loop
description	Autonomous task completion loop that continuously processes tasks from /Needs_Action without human intervention

Ralph Wiggum Loop

The autonomous task completion loop - the heart of Gold tier autonomy.

What It Does

Continuously monitors /Needs_Action folder and autonomously:

1. Reads pending tasks
2. Analyzes task requirements
3. Creates execution plans
4. Executes safe actions automatically
5. Requests approval for sensitive actions
6. Logs all activities
7. Self-heals on errors

Features

• Autonomous Operation: Runs every 10 minutes
• Smart Decision Making: Knows what needs approval vs auto-execute
• Self-Healing: Recovers from errors automatically
• Audit Trail: Complete logging of all actions
• Safety First: Sensitive actions always require approval

Usage

Start the loop (recommended)

Use Claude Code's /loop command to run continuously:

/loop 10m python .claude/skills/ralph-wiggum-loop/scripts/ralph_loop.py

This will run the loop every 10 minutes automatically. Claude Code manages scheduling and restarts.

Run once (test mode)

python .claude/skills/ralph-wiggum-loop/scripts/ralph_loop.py

Stop the loop

Stop the /loop command in Claude Code interface.

How It Works

Every 10 minutes:
  1. Check /Needs_Action for tasks
  2. For each task:
     - Read and analyze
     - Determine if safe to auto-execute
     - If safe: Execute and move to /Done
     - If sensitive: Create approval request in /Pending_Approval
     - If error: Retry with backoff, then move to /Needs_Action with error note
  3. Check /Approved for approved actions
  4. Execute approved actions
  5. Update dashboard with metrics
  6. Log all activities

Auto-Execute vs Approval

Auto-Execute (Safe):

• Reading emails
• Creating drafts
• Organizing files
• Updating dashboard
• Generating reports
• Creating invoices (draft mode)

Requires Approval (Sensitive):

• Sending emails to new contacts
• Posting on social media
• Making payments
• Deleting files
• Finalizing invoices

Safety Features

• Dry-run mode for testing
• Rate limiting (max 10 actions per cycle)
• Error recovery with exponential backoff
• Dead letter queue for failed tasks
• Health monitoring
• Emergency stop capability

Monitoring

Check logs:

tail -f AI_Employee_Vault/Logs/ralph_loop.log

View metrics:

cat AI_Employee_Vault/Dashboard.md

Configuration

Edit ralph_loop_config.json:

{
  "interval_minutes": 10,
  "max_actions_per_cycle": 10,
  "auto_approve_safe_actions": true,
  "enable_self_healing": true,
  "max_retries": 3
}