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