Ralph Loop — Python Orchestrator

⚠️ IMPORTANT: This skill uses a Python orchestrator script. Do NOT execute arbitrary bash commands. Use

Bash

ONLY to run

ralph_loop.py

. All task commands (like

/specs:task-implementation

) are shown to the user to execute manually.

Overview

The Ralph Loop applies Geoffrey Huntley's "Ralph Wiggum as a Software Engineer" technique to specification-driven development. It uses a Python orchestrator script that manages a state machine: one invocation = one step, state persisted in

fix_plan.json

Key insight: Implementing + reviewing + syncing in one invocation explodes the context window. Solution: each loop iteration does exactly one step, saves state to

fix_plan.json

, and stops. The next iteration resumes from saved state.

Key improvement: The Python script

ralph_loop.py

handles all state management, task selection, and command generation. It does NOT execute task commands directly — it shows you the correct command to execute in your CLI.

When to Use

User runs
```
/loop
```
command for recurring automation
User asks to "automate implementation" or "run tasks in loop"
User wants to "iterate through tasks step-by-step" or "run workflow automation"
User needs "context window management" across multiple SDD commands
User wants to "process task range" from TASK-N to TASK-M
User needs multi-agent support (different CLIs for different tasks)

Architecture

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│   ralph_loop.py │────▶│   fix_plan.json │────▶│  User executes  │
│   (orchestrator)│     │   (state file)  │     │  command in CLI │
└─────────────────┘     └─────────────────┘     └─────────────────┘
         │                                               │
         │                                               ▼
         │                                      ┌─────────────────┐
         └──────────────────────────────────────│   Task result   │
                                                │   (success/     │
                                                │   failure)      │
                                                └─────────────────┘

One Step Flow:

Run
```
ralph_loop.py --action=loop
```
Script reads
```
fix_plan.json
```
and determines current step
Script shows the command to execute (e.g.,
```
/specs:task-implementation
```
)
User executes the command in their CLI
User runs
```
ralph_loop.py --action=loop
```
again
Script updates state based on result and shows next command

State Machine

fix_plan.json state machine:
┌─────────────────────────────────────────────────────────────┐
│  state: "init"                                            │
│    → --action=start: Initialize fix_plan.json              │
│    → Load tasks from tasks/TASK-*.md files                │
│    → Apply task_range filter                              │
│                                                             │
│  state: "choose_task"                                      │
│    → Pick next pending task (within range, deps satisfied)│
│    → No tasks in range → state: "complete"               │
│    → Task found → state: "implementation"                │
│                                                             │
│  state: "implementation"                                  │
│    → Show /specs:task-implementation command             │
│    → User executes, then runs loop again                  │
│    → Next state: "review"                                │
│                                                             │
│  state: "review"                                          │
│    → Show /specs:task-review command                     │
│    → User reviews results, then runs loop again          │
│    → Issues found → state: "fix" (retry ≤ 3)             │
│    → Clean → state: "cleanup"                            │
│                                                             │
│  state: "fix"                                             │
│    → Show commands to fix issues                         │
│    → User applies fixes, then runs loop again            │
│    → Next state: "review"                                │
│                                                             │
│  state: "cleanup"                                         │
│    → Show /developer-kit-specs:specs-code-cleanup command│
│    → Next state: "sync"                                  │
│                                                             │
│  state: "sync"                                            │
│    → Show /specs:spec-sync-with-code command             │
│    → Next state: "update_done"                           │
│                                                             │
│  state: "update_done"                                     │
│    → Mark task done, commit git changes                  │
│    → Re-evaluate dependencies                            │
│    → state: "choose_task"                                │
│                                                             │
│  state: "complete" | "failed"                            │
│    → Print result, stop                                   │
└─────────────────────────────────────────────────────────────┘

File Location Requirements

⚠️ CRITICAL: The

fix_plan.json

file MUST ALWAYS be located in:

docs/specs/[ID-feature]/_ralph_loop/fix_plan.json

This is enforced by the script to prevent LLMs from creating files in wrong locations.

Migration: If you have an old

fix_plan.json

in the root of your spec folder, the script will automatically migrate it to

_ralph_loop/

on first run.

Instructions

Phase 1: Initialize

Run the Python script with

--action=start

to scan task files and create

fix_plan.json

in the correct location:

bash

python3 plugins/developer-kit-specs/skills/ralph-loop/scripts/ralph_loop.py \
  --action=start \
  --spec=docs/specs/001-feature/ \
  --from-task=TASK-036 \
  --to-task=TASK-041

Phase 2: Execute Loop Steps

Run the script with

--action=loop

to get the current state and the command to execute:

bash

python3 plugins/developer-kit-specs/skills/ralph-loop/scripts/ralph_loop.py \
  --action=loop \
  --spec=docs/specs/001-feature/

The script will show you the exact command to execute for the current step. Execute it in your CLI, then run the loop command again.

Phase 3: Advance State (Manual)

After executing the shown command, manually advance to the next step:

bash

python3 plugins/developer-kit-specs/skills/ralph-loop/scripts/ralph_loop.py \
  --action=next \
  --spec=docs/specs/001-feature/

This updates

fix_plan.json

to the next state (e.g.,

implementation

→

review

Phase 4: Monitor Progress

Check status anytime with

--action=status

bash

python3 plugins/developer-kit-specs/skills/ralph-loop/scripts/ralph_loop.py \
  --action=status \
  --spec=docs/specs/001-feature/

Quick Start

1. Initialize

bash

python3 plugins/developer-kit-specs/skills/ralph-loop/scripts/ralph_loop.py \
  --action=start \
  --spec=docs/specs/001-feature/ \
  --from-task=TASK-036 \
  --to-task=TASK-041 \
  --agent=claude

2. Run Loop

bash

python3 plugins/developer-kit-specs/skills/ralph-loop/scripts/ralph_loop.py \
  --action=loop \
  --spec=docs/specs/001-feature/

The script will show you the command to execute. Run it, then run the loop again.

3. Check Status

bash

python3 plugins/developer-kit-specs/skills/ralph-loop/scripts/ralph_loop.py \
  --action=status \
  --spec=docs/specs/001-feature/

Arguments

Argument	Description
`--action`	`start` (init), `loop` (run one step), `status` , `resume` , `next` (advance step)
`--spec`	Spec folder path (e.g. `docs/specs/001-feature/` )
`--from-task`	Start of task range (e.g. `TASK-036` )
`--to-task`	End of task range (e.g. `TASK-041` )
`--agent`	Default agent: `claude` , `codex` , `copilot` , `kimi` , `gemini` , `glm4` , `minimax`
`--no-commit`	Skip git commits (for testing)

Step Details

Step 1: Initialize (

--action=start

)

The script:

Scans
```
tasks/TASK-*.md
```
files in the spec folder
Extracts metadata from YAML frontmatter (id, title, status, lang, dependencies, agent)
Applies
```
--from-task
```
and
```
--to-task
```
filters
Creates
```
fix_plan.json
```
with full state

Step 2: Choose Task (

choose_task

)

The script:

Finds pending tasks within range
Checks dependencies are satisfied
Selects next task
Updates
```
fix_plan.json
```
with
```
current_task
```
Shows command to execute

Step 3: Implementation (

implementation

)

The script shows:

→ Implementation: TASK-037

Execute:
  /specs:task-implementation --task=TASK-037

After execution, update state:
  python3 ralph_loop.py --action=loop --spec=docs/specs/001-feature/

Step 4: Review (

review

)

The script shows:

→ Review: TASK-037 | Retry: 0/3

Execute:
  /specs:task-review --task=TASK-037

Review the generated review report, then update state:
  python3 ralph_loop.py --action=loop --spec=docs/specs/001-feature/

Step 5: Fix (

fix

) - If Review Failed

If issues found, script shows fix instructions. After fixes, user runs loop again.

Step 6: Cleanup (

cleanup

)

The script shows:

→ Cleanup: TASK-037

Execute:
  /developer-kit-specs:specs-code-cleanup --task=TASK-037

Step 7: Sync (

sync

)

The script shows:

→ Sync: TASK-037

Execute:
  /specs:spec-sync-with-code docs/specs/001-feature/ --after-task=TASK-037

Step 8: Update Done (

update_done

)

The script:

Marks task as completed in
```
fix_plan.json
```
Commits git changes (unless
```
--no-commit
```
)
Updates iteration count
Returns to
```
choose_task
```

Multi-Agent Support

Default Agent for All Tasks

bash

python3 ralph_loop.py --action=start --spec=... --agent=codex

Per-Task Agent

Specify agent in task file YAML frontmatter:

yaml

---
id: TASK-036
title: Refactor user service
status: pending
lang: java
agent: codex
---

Supported agents:

claude

codex

copilot

kimi

gemini

glm4

minimax

Using with /loop (Claude Code)

For automatic scheduling every 5 minutes:

bash

/loop 5m python3 plugins/developer-kit-specs/skills/ralph-loop/scripts/ralph_loop.py \
  --action=loop \
  --spec=docs/specs/001-feature/

This will repeatedly run the loop, showing you the next command each time.

Note: The Ralph Loop is now managed directly through the Python script. The deprecated

/specs:ralph-loop

command has been removed.

Task File Format

Each task should be a separate file:

tasks/TASK-XXX.md

markdown

---
id: TASK-036
title: Implement user authentication
status: pending
lang: java
dependencies: []
complexity: medium
agent: claude
---

## Description

Implement JWT-based authentication for the API.

## Acceptance Criteria

- [ ] Login endpoint returns JWT token
- [ ] Token validation middleware
- [ ] Refresh token mechanism

Examples

Example 1: Basic Usage

bash

# Initialize
python3 ralph_loop.py --action=start \
  --spec=docs/specs/001-feature/ \
  --from-task=TASK-001 \
  --to-task=TASK-005

# Loop until complete
while true; do
  python3 ralph_loop.py --action=loop --spec=docs/specs/001-feature/
  # Execute the shown command manually
  # Then continue loop
done

Example 2: With Claude Code /loop

bash

# Start with specific range
/loop 5m python3 plugins/developer-kit-specs/skills/ralph-loop/scripts/ralph_loop.py \
  --action=loop \
  --spec=docs/specs/002-tdd-command \
  --from-task=TASK-001 \
  --to-task=TASK-010

Example 3: Multi-Agent Setup

bash

# Initialize with Claude as default
python3 ralph_loop.py --action=start \
  --spec=docs/specs/001-feature/ \
  --agent=claude

# Some tasks have "agent: codex" in their frontmatter
# Those will show Codex-formatted commands

Best Practices

One step per invocation: Execute exactly one step, save state, stop
Trust the state: Read from
```
fix_plan.json
```
, write to
```
fix_plan.json
```
No context accumulation: State lives in the file, not in context
Manual command execution: The script shows commands; you execute them in your CLI
Retry on review failure: Max 3 retries before failing
Range filtering: Always filter by
```
task_range
```
Dependencies first: Only pick tasks where all dependencies are done
Git commits: The script auto-commits after each completed task

Constraints and Warnings

Context explosion: Do NOT implement + review + sync in one invocation — context will overflow
Max retries: Review failures retry up to 3 times, then fail
Git state: Ensure clean git state before starting
Test infrastructure: Loop requires tests to pass — without tests, backpressure is ineffective

Strict state validation: Valid

state.step

values are ONLY:

init

choose_task

implementation

review

fix

cleanup

sync

update_done

complete

failed

NO automatic command execution: The script shows commands but does NOT execute them — you must run them in your CLI

Troubleshooting

"fix_plan.json not found"

Run

--action=start

first:

bash

python3 ralph_loop.py --action=start --spec=docs/specs/001-feature/

The script will create

fix_plan.json

in the correct location:

docs/specs/001-feature/_ralph_loop/fix_plan.json

"fix_plan.json in wrong location"

If you see a warning about the file being in the wrong location, the script will guide you through migration:

bash

# Manual migration if needed
mkdir -p docs/specs/001-feature/_ralph_loop
mv docs/specs/001-feature/fix_plan.json docs/specs/001-feature/_ralph_loop/fix_plan.json

The script will automatically migrate old files on first run.

"Invalid spec folder"

Run

--action=start

first:

bash

python3 ralph_loop.py --action=start --spec=docs/specs/001-feature/

Task files not found

Ensure tasks are in

tasks/TASK-XXX.md

format with YAML frontmatter.

Wrong agent commands

Check

--agent

parameter or task

agent:

frontmatter field.

References

```
references/state-machine.md
```
- Complete state machine documentation
```
references/multi-cli-integration.md
```
- Multi-CLI setup guide
```
references/loop-prompt-template.md
```
- Prompt template for shell loops

ralph-loop

NPX Install

Tags

SKILL.md Content

Ralph Loop — Python Orchestrator

Overview

When to Use

Architecture

State Machine

File Location Requirements

Instructions

Phase 1: Initialize

Phase 2: Execute Loop Steps

Phase 3: Advance State (Manual)

Phase 4: Monitor Progress

Quick Start

1. Initialize

2. Run Loop

3. Check Status

Arguments

Step Details

Step 1: Initialize (--action=start)

Step 2: Choose Task (choose_task)

Step 3: Implementation (implementation)

Step 4: Review (review)

Step 5: Fix (fix) - If Review Failed

Step 6: Cleanup (cleanup)

Step 7: Sync (sync)

Step 8: Update Done (update_done)

Multi-Agent Support

Default Agent for All Tasks

Per-Task Agent

Using with /loop (Claude Code)

Task File Format

Examples

Example 1: Basic Usage

Example 2: With Claude Code /loop

Example 3: Multi-Agent Setup

Best Practices

Constraints and Warnings

Troubleshooting

"fix_plan.json not found"

"fix_plan.json in wrong location"

"Invalid spec folder"

Task files not found

Wrong agent commands

References

Step 1: Initialize (
`--action=start`
)

Step 2: Choose Task (
`choose_task`
)

Step 3: Implementation (
`implementation`
)

Step 4: Review (
`review`
)

Step 5: Fix (
`fix`
) - If Review Failed

Step 6: Cleanup (
`cleanup`
)

Step 7: Sync (
`sync`
)

Step 8: Update Done (
`update_done`
)