/ralph-resume
Resume an interrupted agent loop from last checkpoint
$ npx -y skills add jmagly/aiwg --skill ralph-resume --agent claude-codeHow it fires
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- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/ralph-resume
Context preview
The summary Claude sees to decide when to auto-load this skill.
Resume an interrupted agent loop from last checkpoint
SKILL.md
ralph-resume.SKILL.mdnamespace: aiwg
name: ralph-resume
platforms: [all]
description: Resume an interrupted agent loop from last checkpoint
commandHint:
argumentHint: '[--max-iterations N] [--timeout M --interactive --guidance "text"]'
allowedTools: "Task, Read, Write, Bash, Glob, Grep, TodoWrite, Edit"
model: haiku
category: automation
orchestration: true
modelRole: efficiency
modelTier: economy
<!-- AIWG-SKILL-CALLOUT --> > **Skill access pattern (post-kernel-pivot, 2026.5+)** > > Skill names referenced in this document are AIWG skills, **not slash commands**. Most are not kernel-listed and cannot be invoked as `/skill-name` by the platform. Reach them via: > > ```bash > aiwg discover "<capability>" > aiwg show skill <name> > ``` > > Only kernel-listed skills (`aiwg-doctor`, `aiwg-refresh`, `aiwg-status`, `aiwg-help`, `use`, `steward`) are directly invokable as slash commands. See [skill-discovery rule](../../../addons/aiwg-utils/rules/skill-discovery.md).
Al Resume
Resume a paused or interrupted agent 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 agent 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 agent loop pattern:
1. Display resume status:
Resuming Agent 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 agent 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:
## Agent 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 agent 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 agent loop was aborted and cannot be resumed.
To start fresh with the same task:
/ralph "{original task}" --completion "{original completion}"**State corrupted**:
Agent 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
References
- @$AIWG_ROOT/agentic/code/addons/ralph/README.md — Ralph addon overview and loop executor documentation
- @$AIWG_ROOT/agentic/code/addons/aiwg-utils/rules/vague-discretion.md — Loop termination and iteration limit rules
- @$AIWG_ROOT/docs/cli-reference.md — CLI reference for ralph-resume and related commands
- @$AIWG_ROOT/agentic/code/addons/aiwg-utils/rules/instruction-comprehension.md — Re-reading original task instructions on resume
Read more
namespace: aiwg name: ralph-resume platforms: [all] description: Resume an interrupted agent loop from last checkpoint commandHint: argumentHint: '[--max-iterations N] [--timeout M --interactive --guidance "text"]' allowedTools: "Task, Read, Write, Bash, Glob, Grep, TodoWrite, Edit" model: haiku category: automation orchestration: true modelRole: efficiency modelTier: economy
<!-- AIWG-SKILL-CALLOUT --> > **Skill access pattern (post-kernel-pivot, 2026.5+)** > > Skill names referenced in this document are AIWG skills, **not slash commands**. Most are not kernel-listed and cannot be invoked as `/skill-name` by the platform. Reach them via: > > ```bash > aiwg discover "<capability>" > aiwg show skill <name> > ``` > > Only kernel-listed skills (`aiwg-doctor`, `aiwg-refresh`, `aiwg-status`, `aiwg-help`, `use`, `steward`) are directly invokable as slash commands. See [skill-discovery rule](../../../addons/aiwg-utils/rules/skill-discovery.md).
Al Resume
Resume a paused or interrupted agent 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 agent 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 agent loop pattern:
1. Display resume status:
Resuming Agent 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 agent 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:
## Agent 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 agent 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 agent loop was aborted and cannot be resumed.
To start fresh with the same task:
/ralph "{original task}" --completion "{original completion}"**State corrupted**:
Agent 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
References
- @$AIWG_ROOT/agentic/code/addons/ralph/README.md — Ralph addon overview and loop executor documentation
- @$AIWG_ROOT/agentic/code/addons/aiwg-utils/rules/vague-discretion.md — Loop termination and iteration limit rules
- @$AIWG_ROOT/docs/cli-reference.md — CLI reference for ralph-resume and related commands
- @$AIWG_ROOT/agentic/code/addons/aiwg-utils/rules/instruction-comprehension.md — Re-reading original task instructions on resume
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Repo: jmagly/aiwg
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