ralph-loop

What this skill does

> **⚠️ WARNING**: This skill was deprecated in favor of a new command `ralph-loop-v2` that uses a Python orchestrator script. 
> The old `/specs:ralph-loop` command will be removed soon. Please migrate to the new command.
# 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 `/developer-kit-specs: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:**
1. Run `ralph_loop.py --action=loop`
2. Script reads `fix_plan.json` and determines current step
3. Script shows the command to execute (e.g., `/developer-kit-specs:specs.task-implementation`)
4. User executes the command in their CLI
5. User runs `ralph_loop.py --action=loop` again
6. 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 /developer-kit-specs:specs.task-implementation command             │
│    → User executes, then runs loop again                  │
│    → Next state: "review"                                │
│                                                             │
│  state: "review"                                          ││    → Show /developer-kit-specs:specs.task-implementation --action=cleanup command│},{find:                    │
│    → 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.task-implementation --action=cleanup command│
│    → Next state: "sync"                                  │
│                                                             │
│  state: "sync"                                            │
│    → Show /developer-kit-specs:specs.sync 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/
```

## Argument