ralph-monitor — for Claude Code ralph-monitor, claude-code-base, community, for Claude Code, ide skills, ### From Local State, Monitor, report, Wiggum, status

v1.0.0

À propos de ce Skill

Scenario recommande : Ideal for AI agents that need ralph monitor skill. Resume localise : Comprehensive Claude Code project template with 164 skills, 50 commands, and full configuration # Ralph Monitor Skill Monitor and report on Ralph Wiggum loop status and progress.

Fonctionnalités

Ralph Monitor Skill
Use this skill when:
Checking Ralph loop status
Viewing iteration progress
Monitoring running loops

# Core Topics

HouseGarofalo HouseGarofalo
[1]
[0]
Updated: 3/11/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 10/11

This page remains useful for teams, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution Quality floor passed for review
Review Score
10/11
Quality Score
75
Canonical Locale
en
Detected Body Locale
en

Scenario recommande : Ideal for AI agents that need ralph monitor skill. Resume localise : Comprehensive Claude Code project template with 164 skills, 50 commands, and full configuration # Ralph Monitor Skill Monitor and report on Ralph Wiggum loop status and progress.

Pourquoi utiliser cette compétence

Recommandation : ralph-monitor helps agents ralph monitor skill. Comprehensive Claude Code project template with 164 skills, 50 commands, and full configuration # Ralph Monitor Skill Monitor and report on Ralph Wiggum

Meilleur pour

Scenario recommande : Ideal for AI agents that need ralph monitor skill.

Cas d'utilisation exploitables for ralph-monitor

Cas d'usage : Applying Ralph Monitor Skill
Cas d'usage : Applying Use this skill when:
Cas d'usage : Applying Checking Ralph loop status

! Sécurité et Limitations

  • Limitation : Requires repository-specific context from the skill documentation
  • Limitation : Works best when the underlying tools and dependencies are already configured

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Labs Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is ralph-monitor?

Scenario recommande : Ideal for AI agents that need ralph monitor skill. Resume localise : Comprehensive Claude Code project template with 164 skills, 50 commands, and full configuration # Ralph Monitor Skill Monitor and report on Ralph Wiggum loop status and progress.

How do I install ralph-monitor?

Run the command: npx killer-skills add HouseGarofalo/claude-code-base/ralph-monitor. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for ralph-monitor?

Key use cases include: Cas d'usage : Applying Ralph Monitor Skill, Cas d'usage : Applying Use this skill when:, Cas d'usage : Applying Checking Ralph loop status.

Which IDEs are compatible with ralph-monitor?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for ralph-monitor?

Limitation : Requires repository-specific context from the skill documentation. Limitation : Works best when the underlying tools and dependencies are already configured.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add HouseGarofalo/claude-code-base/ralph-monitor. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use ralph-monitor immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

ralph-monitor

Install ralph-monitor, an AI agent skill for AI agent workflows and automation. Review the use cases, limitations, and setup path before rollout.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

Ralph Monitor Skill

Monitor and report on Ralph Wiggum loop status and progress. Provides visibility into active and completed loops via Archon state.

Triggers

Use this skill when:

  • Checking Ralph loop status
  • Viewing iteration progress
  • Monitoring running loops
  • Reviewing loop history
  • Diagnosing stuck loops
  • Keywords: ralph status, loop status, ralph monitor, check loop, iteration progress, loop history

Core Mission

Query Archon and local state to provide comprehensive status reports on Ralph loops.


Status Report Formats

Active Loop Status

markdown
1## Ralph Loop Status 2 3### Loop Information 4| Property | Value | 5|----------|-------| 6| Loop ID | [LOOP_ID] | 7| Status | Running / Paused / Stopped | 8| Started | [TIMESTAMP] | 9| Duration | [HH:MM:SS] | 10 11### Progress 12| Metric | Current | Target | 13|--------|---------|--------| 14| Iteration | [N] | [MAX] | 15| Tasks | [DONE] | [TOTAL] | 16| Tests Passing | [PASS] | [TOTAL] | 17 18[====================----------] 67% complete 19 20### Current Iteration 21| Property | Value | 22|----------|-------| 23| Iteration | [N] | 24| Started | [TIME] | 25| Focus | [Current work summary] | 26 27### Recent Activity 28| Iter | Time | Summary | Files | Tests | 29|------|------|---------|-------|-------| 30| N | 5m ago | [Summary] | 3 | 15/15 | 31| N-1 | 12m ago | [Summary] | 5 | 14/15 | 32| N-2 | 20m ago | [Summary] | 2 | 10/15 | 33 34### Validation Status 35- Build: Passing 36- Tests: 15/15 passing 37- Lint: 2 warnings 38 39### Archon Integration 40- Project: [PROJECT_NAME] ([PROJECT_ID]) 41- Task: [TASK_TITLE] ([TASK_ID]) 42- Task Status: doing 43- State Doc: [DOC_ID] 44 45### Commands 46- View full log: `cat .ralph/loop.log` 47- Cancel loop: Use ralph-loop skill with cancel mode 48- View prompt: `cat .ralph/prompts/current.md`

Data Collection

From Archon

python
1# Get state document 2state_docs = find_documents( 3 project_id=PROJECT_ID, 4 query="Ralph Loop State" 5) 6 7# Get active loops 8active_loops = [ 9 doc for doc in state_docs 10 if doc["content"]["status"] == "running" 11] 12 13# Get associated tasks 14for loop in active_loops: 15 task = find_tasks(task_id=loop["content"]["task_id"]) 16 loop["task"] = task

From Local State

bash
1# Read config 2cat .ralph/config.json 3 4# Read recent log 5tail -100 .ralph/loop.log 6 7# Git status 8git --no-pager log --oneline -5 9git status --short

Report Types

Quick Status

Returns brief one-liner:

Ralph: Iteration 12/50 | 67% | Tests 15/15 | Duration 25m

Full Status

Returns complete report as shown above.

History Report

markdown
1## Ralph Loop History 2 3### Completed Loops 4| Loop ID | Task | Iterations | Duration | Status | 5|---------|------|------------|----------|--------| 6| ralph-20260122-150000 | Auth API | 12 | 45m | Complete | 7| ralph-20260121-100000 | DB Schema | 8 | 30m | Complete | 8| ralph-20260120-140000 | User Model | 25 | 1h 20m | Max reached | 9 10### Statistics 11| Metric | Value | 12|--------|-------| 13| Total Loops | 15 | 14| Completed | 12 (80%) | 15| Blocked | 2 (13%) | 16| Max Reached | 1 (7%) | 17| Avg Iterations | 14 | 18| Avg Duration | 35m |

Comparison Report

markdown
1## Loop Comparison 2 3| Metric | loop1 | loop2 | 4|--------|-------|-------| 5| Task | Auth API | User API | 6| Iterations | 12 | 18 | 7| Duration | 45m | 1h 10m | 8| Files Changed | 24 | 31 | 9| Tests Added | 15 | 22 | 10| Status | Complete | Complete |

Progress Visualization

Iteration Timeline

Iteration Progress
==================

1  #### Setup
2  ######## Basic impl
3  ############ Tests added
4  ###### Bug fix
5  ################ Feature complete
6  #### Refactor
7  ########## Edge cases
8  #################### Validation
9  ###### Polish
10 ######################## Complete

Legend: #### = Work done, length = files changed

Test Progress

Test Progress Across Iterations
===============================

Iter  1: [          ] 0/0
Iter  2: [###       ] 5/15
Iter  3: [#####     ] 8/15
Iter  4: [######    ] 10/15
Iter  5: [########  ] 12/15
Iter  6: [########  ] 12/15  <- regression
Iter  7: [##########] 15/15 PASS

Alerts and Warnings

Stuck Detection

markdown
1## Potential Issue Detected 2 3### Stuck Pattern 4The loop appears to be stuck: 5- Last 3 iterations made no test progress 6- Same files being modified repeatedly 7- Similar error messages in output 8 9### Recommendation 10Consider: 111. Reviewing the prompt for clarity 122. Breaking the task into smaller pieces 133. Adding more specific validation criteria 144. Canceling and debugging manually 15 16### Action 17- Continue monitoring 18- Cancel loop if needed 19- Review logs: `cat .ralph/loop.log | tail -500`

Resource Warning

markdown
1## Resource Warning 2 3### Issue 4- Token usage high in recent iterations 5- Approaching context limit 6 7### Recommendation 8- Consider checkpointing 9- May need to restart with fresh context 10- Current work is saved in Archon

Troubleshooting Commands

Check Configuration

markdown
1## Configuration Check 2 3### Files 4- [x] .ralph/config.json exists 5- [x] .ralph/prompts/current.md exists 6- [x] .ralph/loop-state.json exists 7 8### Archon Connection 9- [x] Project found: [PROJECT_NAME] 10- [x] Task found: [TASK_TITLE] 11- [x] State document found 12 13### Validation Commands 14- [x] Build: `npm run build` (verified) 15- [x] Test: `npm test` (verified) 16- [ ] Lint: `npm run lint` (not configured) 17 18### All checks passed

Usage Examples

Check Current Status

python
1# Quick check 2"What's the Ralph loop status?" 3 4# Full details 5"Show me detailed Ralph loop status"

View History

python
1# All loops 2"Show Ralph loop history" 3 4# Specific loop 5"Show details for ralph-20260122-150000"

Diagnose Issues

python
1# Check for problems 2"Is the Ralph loop stuck?" 3 4# View recent iterations 5"Show last 5 Ralph iterations"

Integration with Other Skills

With ralph-loop Skill

python
1# If monitoring shows issues, suggest: 2"Use ralph-loop skill to cancel or resume"

With archon-workflow Skill

python
1# For task management integration: 2"Use archon-workflow to update task status"

Best Practices

  1. Regular Monitoring: Check status periodically for long loops
  2. Watch for Patterns: Same files modified repeatedly = potential issue
  3. Test Progress: Should generally increase each iteration
  4. Duration Tracking: Unusually long iterations may indicate problems
  5. Archon Sync: Ensure state is properly saved to Archon
  6. Log Review: Check loop.log for detailed error messages

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