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Cognitive architecture for AI-augmented software development with structured memory, ensemble validation, and closed-loop correction. FAIR-aligned artifacts, 84% cost reduction via human-in-the-loop, standards adopted by 100+ organizations.
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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