agent-loop-ext
Crash-resilient external agent loop with state persistence and CI/CD integration
Resume an interrupted agent loop from last checkpoint
$ npx -y skills add jmagly/aiwg --skill ralph-resume --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/ralph-resumeContext preview
The summary Claude sees to decide when to auto-load this skill.
Resume an interrupted agent loop from last checkpoint
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).
Resume a paused or interrupted agent loop.
/ralph-resume # Resume with existing settings /ralph-resume --max-iterations 20 # Resume with higher iteration limit /ralph-resume --timeout 120 # Resume with longer timeout
Override the maximum iterations limit. Useful when loop stopped at limit but was making progress.
Override the timeout in minutes. Useful when loop timed out but task is close to completion.
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"Apply any parameter overrides:
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
Same as `ralph` - generate completion report on success or limit.
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.**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
Previous loop stopped at iteration 10:
/ralph-resume --max-iterations 20
Continues with 10 more iterations available.
Previous loop timed out at 60 minutes:
/ralph-resume --timeout 120
Continues with fresh 120-minute timeout.
Loop interrupted (network, restart, etc.):
/ralph-resume
Continues from last checkpoint with original settings.
Reusable project context and specialist workflows for the AI tools you already use. Plan software, coordinate specialist reviews, prepare campaigns, investigate incidents, organize research, curate media, and maintain operational knowledge.
Repo: jmagly/aiwg
Crash-resilient external agent loop with state persistence and CI/CD integration
Detect requests for iterative autonomous agent loops and route to the appropriate loop executor
Automatically execute tests when code-generating agents modify source files, enforcing the execute-before-return pattern
Enable agent loops to learn from similar past tasks and share patterns across loops
Query and manage the executable feedback debug memory
Execute tests on generated code and iterate until passing