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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