Ralph Resume

---
description: Resume an interrupted Ralph loop from last checkpoint
category: automation
argument-hint: [--max-iterations N] [--timeout M --interactive --guidance "text"]
allowed-tools: Task, Read, Write, Bash, Glob, Grep, TodoWrite, Edit
orchestration: true
model: opus
---

# Ralph Resume

Resume a paused or interrupted Ralph loop.

## Usage

/ralph-resume # Resume with existing settings /ralph-resume --max-iterations 20 # Resume with higher iteration limit /ralph-resume --timeout 120 # Resume with longer timeout

## Parameters

### --max-iterations N
Override the maximum iterations limit. Useful when loop stopped at limit but was making progress.

### --timeout M
Override the timeout in minutes. Useful when loop timed out but task is close to completion.

## Your Actions

### Step 1: Load State

1. Read `.aiwg/ralph/current-loop.json`
2. Verify loop can be resumed (status != 'success', status != 'aborted')
3. Load iteration history and learnings

**If no resumable loop**:

No Ralph loop to resume.

Status: {status}

{If success}: Loop completed successfully. Start a new loop with /ralph {If aborted}: Loop was aborted. Start fresh with /ralph {If no state}: No loop found. Start with /ralph "task" --completion "criteria"

### Step 2: Update Settings

Apply any parameter overrides:
- Update `maxIterations` if --max-iterations provided
- Update `timeoutMinutes` if --timeout provided
- Reset timeout start time for extended timeout

### Step 3: Resume Execution

Continue the Ralph loop pattern:

1. Display resume status:

Resuming Ralph Loop

Task: {task} Completion: {completion} Previous iterations: {N} Remaining iterations: {max - N}

Last result: {lastResult} Learnings so far: {learnings}

Continuing from iteration {N+1}...

2. Execute next iteration with accumulated learnings
3. Follow standard Ralph loop verification
4. Continue until success or new limits reached

### Step 4: Handle Completion

Same as `/ralph` - generate completion report on success or limit.

## Resume Context

When resuming, include in the task context:

Ralph Loop Resume Context

Original Task: {task} Completion Criteria: {completion}

Previous Iterations: {N} Accumulated Learnings: {for each iteration}

• Iteration {i}: {action} -> {result}. Learned: {learnings} {end for}

Current State:

• Last attempt: {lastResult}
• Key insight: {most recent learning}

Your Goal: Continue iterating from iteration {N+1}. Apply learnings from previous iterations. Verify against completion criteria after each attempt.

## Error Handling

**Loop completed successfully**:

This Ralph loop already completed successfully.

Final status: SUCCESS Iterations: {N} Report: .aiwg/ralph/completion-{timestamp}.md

To run again, start a new loop: /ralph "task" --completion "criteria"

**Loop was aborted**:

This Ralph loop was aborted and cannot be resumed.

To start fresh with the same task: /ralph "{original task}" --completion "{original completion}"

**State corrupted**:

Ralph loop state is corrupted or incomplete.

Options:

1. Start fresh: /ralph "task" --completion "criteria"
2. Clean up: rm -rf .aiwg/ralph/ then start new loop

## Example Scenarios

### Max Iterations Override

Previous loop stopped at iteration 10:

/ralph-resume --max-iterations 20

Continues with 10 more iterations available.

### Timeout Override

Previous loop timed out at 60 minutes:

/ralph-resume --timeout 120

Continues with fresh 120-minute timeout.

### Simple Resume

Loop interrupted (network, restart, etc.):

/ralph-resume

Continues from last checkpoint with original settings.

## Related

- `/ralph-status` - Check what state the loop is in
- `/ralph-abort` - Stop instead of resume
- `/ralph` - Start new loop