richginsberg

Ralph Mode - Autonomous Development Loops

AIエージェントv1.2.0
作者:richginsberg更新日 2026-05-11全 3 バージョン · 最新版の公開日 2026-02-12ralph-mode@clawhub_richginsberg/ralph-mode
タスク自動化ワークフロー編成

AlphaMoatによると、2026-09時点でRalph Mode - Autonomous Development Loopsのダウンロード数は4,659、スター数は6で、AIスキル全体5,078件中2,047位、AIエージェントでは969件中385位です。

インストール数
231
ダウンロード数
4659
スター数
6
総合順位
#2,047
全体の上位 41% · AIエージェントの上位 40%
プラットフォームインストール数ダウンロード数スター数コメント
ClawHub ↗15346416-
SkillHub ↗23146596—

プラットフォームごとに集計方法と集計時点が異なります。

このスキルでできること

OpenClaw プロジェクト向けの構造化された自律開発フローを提供します。要件と受け入れ条件を定義し、計画を作成した後、検証ゲートを通しながらタスクを順に実装します。反復的な開発・テスト・進捗管理を伴う継続的なコーディングに適しています。ループは広範囲のコード変更を生む可能性があるため、目標・制限・検証条件を明確にし、変更内容を確認してください。

資料公開日 2026-10-27

できること

  • 複雑な機能開発を段階的な作業計画にする
  • 継続的なコーディングで受け入れ条件と検証結果を追跡する

使う場面

  • 反復しながら実装と確認を進めるとき
  • 自律開発プロセスに明確な完了条件を設けるとき

プロンプト例

スキルのドキュメントをもとに生成した例であり、実際の実行結果ではありません。

この開発タスクを検証可能な段階に分け、順に進めて。

月次の推移

ダウンロード数4659
3184397647682026-03 · ダウンロード数 32932026-05 · ダウンロード数 39022026-06 · ダウンロード数 40732026-07 · ダウンロード数 43802026-08 · ダウンロード数 45172026-09 · ダウンロード数 465926-0326-09
スター数6
5672026-03 · スター数 62026-05 · スター数 62026-06 · スター数 62026-07 · スター数 62026-08 · スター数 62026-09 · スター数 626-0326-09

2026-09 から集計方法を変更し、同名スキルを作者ごとに分けて集計し、全スキルを対象にしました。破線は変更前の月で、変更後のデータとは直接比較せず、変更をまたぐ増減も計算していません。

月ダウンロード数前月比スター数
2026-094659—6
2026-084517+3.1%6
2026-074380+7.5%6
2026-064073+4.4%6
2026-053902—6
2026-033293—6

SKILL.md 原文

作者が公開した元のファイルをテキストのまま表示しています。AlphaMoat による編集や審査はしていません。リンクはクリックできません。コマンドは内容を確認してから実行してください。取得日 2026-10-23。

15 KB・ClawHub v1.2.0
---
name: ralph-mode
description: Autonomous development loops with iteration, backpressure gates, and completion criteria. Use for sustained coding sessions that require multiple iterations, test validation, and structured progress tracking. Supports Next.js, Python, FastAPI, and GPU workloads with Ralph Wiggum methodology adapted for OpenClaw.
---

# Ralph Mode - Autonomous Development Loops

Ralph Mode implements the Ralph Wiggum technique adapted for OpenClaw: autonomous task completion through continuous iteration with backpressure gates, completion criteria, and structured planning.

## When to Use
すべて表示(全 552 行)

Use Ralph Mode when:
- Building features that require multiple iterations and refinement
- Working on complex projects with acceptance criteria to validate
- Need automated testing, linting, or typecheck gates
- Want to track progress across many iterations systematically
- Prefer autonomous loops over manual turn-by-turn guidance

## Core Principles

### Three-Phase Workflow

**Phase 1: Requirements Definition**
- Document specs in `specs/` (one file per topic of concern)
- Define acceptance criteria (observable, verifiable outcomes)
- Create implementation plan with prioritized tasks

**Phase 2: Planning**
- Gap analysis: compare specs against existing code
- Generate `IMPLEMENTATION_PLAN.md` with prioritized tasks
- No implementation during this phase

**Phase 3: Building (Iterative)**
- Pick one task from plan per iteration
- Implement, validate, update plan, commit
- Continue until all tasks complete or criteria met

### Backpressure Gates

Reject incomplete work automatically through validation:

**Programmatic Gates (Always use these):**
- Tests: `[test command]` - Must pass before committing
- Typecheck: `[typecheck command]` - Catch type errors early
- Lint: `[lint command]` - Enforce code quality
- Build: `[build command]` - Verify integration

**Subjective Gates (Use for UX, design, quality):**
- LLM-as-judge reviews for tone, aesthetics, usability
- Binary pass/fail - converges through iteration
- Only add after programmatic gates work reliably

### Context Efficiency

- One task per iteration = fresh context each time
- Spawn sub-agents for exploration, not main context
- Lean prompts = smart zone (~40-60% utilization)
- Plans are disposable - regenerate cheap vs. salvage

## File Structure

Create this structure for each Ralph Mode project:

```
project-root/
├── IMPLEMENTATION_PLAN.md     # Shared state, updated each iteration
├── AGENTS.md                  # Build/test/lint commands (~60 lines)
├── specs/                     # Requirements (one file per topic)
│   ├── topic-a.md
│   └── topic-b.md
├── src/                        # Application code
└── src/lib/                    # Shared utilities
```

### IMPLEMENTATION_PLAN.md

Priority task list - single source of truth. Format:

```markdown
# Implementation Plan

## In Progress
- [ ] Task name (iteration N)
  - Notes: discoveries, bugs, blockers

## Completed
- [x] Task name (iteration N)

## Backlog
- [ ] Future task
```

### Topic Scope Test

Can you describe the topic in one sentence without "and"?
- ✅ "User authentication with JWT and session management"
- ❌ "Auth, profiles, and billing" → 3 topics

### AGENTS.md - Operational Guide

Succinct guide for running the project. Keep under 60 lines:

```markdown
# Project Operations

## Build Commands
npm run dev      # Development server
npm run build     # Production build

## Validation
npm run test      # All tests
npm run lint      # ESLint
npm run typecheck  # TypeScript
npm run e2e       # E2E tests

## Operational Notes
- Tests must pass before committing
- Typecheck failures block commits
- Use existing utilities from src/lib over ad-hoc copies
```

## Hats (Personas)

Specialized roles for different tasks:

**Hat: Architect** (`@architect`)
- High-level design, data modeling, API contracts
- Focus: patterns, scalability, maintainability

**Hat: Implementer** (`@implementer`)
- Write code, implement features, fix bugs
- Focus: correctness, performance, test coverage

**Hat: Tester** (`@tester`)
- Test authoring, validation, edge cases
- Focus: coverage, reliability, reproducibility

**Hat: Reviewer** (`@reviewer`)
- Code reviews, PR feedback, quality assessment
- Focus: style, readability, adherence to specs

**Usage:**
```
"Spawn a sub-agent with @architect hat to design the data model"
```

## Loop Mechanics

### Outer Loop (You coordinate)

Your job as main agent: engineer setup, observe, course-correct.

1. **Don't allocate work to main context** - Spawn sub-agents
2. **Let Ralph Ralph** - LLM will self-identify, self-correct
3. **Use protection** - Sandbox is your security boundary
4. **Plan is disposable** - Regenerate when wrong/stale
5. **Move outside the loop** - Sit and watch, don't micromanage

### Inner Loop (Sub-agent executes)

Each sub-agent iteration:
1. **Study** - Read plan, specs, relevant code
2. **Select** - Pick most important uncompleted task
3. **Implement** - Write code, one task only
4. **Validate** - Run tests, lint, typecheck (backpressure)
5. **Update** - Mark task done, note discoveries, commit
6. **Exit** - Next iteration starts fresh

### Stopping Conditions

Loop ends when:
- ✅ All IMPLEMENTATION_PLAN.md tasks completed
- ✅ All acceptance criteria met
- ✅ Tests passing, no blocking issues
- ⚠️ Max iterations reached (configure limit)
- 🛑 Manual stop (Ctrl+C)

## Completion Criteria

Define success upfront - avoid "seems done" ambiguity.

### Programmatic (Measurable)
- All tests pass: `[test_command]` returns 0
- Typecheck passes: No TypeScript errors
- Build succeeds: Production bundle created
- Coverage threshold: e.g., 80%+

### Subjective (LLM-as-Judge)
For quality criteria that resist automation:

```markdown
## Completion Check - UX Quality
Criteria: Navigation is intuitive, primary actions are discoverable
Test: User can complete core flow without confusion

## Completion Check - Design Quality
Criteria: Visual hierarchy is clear, brand consistency maintained
Test: Layout follows established patterns
```

Run LLM-as-judge sub-agent for binary pass/fail.

## Technology-Specific Patterns

### Next.js Full Stack

```
specs/
├── authentication.md
├── database.md
└── api-routes.md

src/
├── app/                    # App Router
├── components/              # React components
├── lib/                    # Utilities (db, auth, helpers)
└── types/                   # TypeScript types

AGENTS.md:
  Build: npm run dev
  Test: npm run test
  Typecheck: npx tsc --noEmit
  Lint: npm run lint
```

### Python (Scripts/Notebooks/FastAPI)

```
specs/
├── data-pipeline.md
├── model-training.md
└── api-endpoints.md

src/
├── pipeline.py
├── models/
├── api/
└── tests/

AGENTS.md:
  Build: python -m src.main
  Test: pytest
  Typecheck: mypy src/
  Lint: ruff check src/
```

### GPU Workloads

```
specs/
├── model-architecture.md
├── training-data.md
└── inference-pipeline.md

src/
├── models/
├── training/
├── inference/
└── utils/

AGENTS.md:
  Train: python train.py
  Test: pytest tests/
  Lint: ruff check src/
  GPU Check: nvidia-smi
```

## Quick Start Command

Start a Ralph Mode session:

```
"Start Ralph Mode for my project at ~/projects/my-app. I want to implement user authentication with JWT.
```

I will:
1. Create IMPLEMENTATION_PLAN.md with prioritized tasks
2. Spawn sub-agents for iterative implementation
3. Apply backpressure gates (test, lint, typecheck)
4. Track progress and announce completion

## Operational Learnings

When Ralph patterns emerge, update AGENTS.md:

```markdown
## Discovered Patterns

- When adding API routes, also add to OpenAPI spec
- Use existing db utilities from src/lib/db over direct calls
- Test files must be co-located with implementation
```

## Escape Hatches

When trajectory goes wrong:
- **Ctrl+C** - Stop loop immediately
- **Regenerate plan** - "Discard IMPLEMENTATION_PLAN.md and re-plan"
- **Reset** - "Git reset to last known good state"
- **Scope down** - Create smaller scoped plan for specific work

## Advanced: LLM-as-Judge Fixture

For subjective criteria (tone, aesthetics, UX):

Create `src/lib/llm-review.ts`:

```typescript
interface ReviewResult {
  pass: boolean;
  feedback?: string;
}

async function createReview(config: {
  criteria: string;
  artifact: string; // text or screenshot path
}): Promise<ReviewResult>;
```

Sub-agents discover and use this pattern for binary pass/fail checks.

## Critical Operational Requirements

Based on empirical usage, enforce these practices to avoid silent failures:

### 1. Mandatory Progress Logging

**Ralph MUST write to PROGRESS.md after EVERY iteration.** This is non-negotiable.

Create `PROGRESS.md` in project root at start:

```markdown
# Ralph: [Task Name]

## Iteration [N] - [Timestamp]

### Status
- [ ] In Progress | [ ] Blocked | [ ] Complete

### What Was Done
- [Item 1]
- [Item 2]

### Blockers
- None | [Description]

### Next Step
[Specific next task from IMPLEMENTATION_PLAN.md]

### Files Changed
- `path/to/file.ts` - [brief description]
```

**Why:** External observers (parent agents, crons, humans) can tail one file instead of scanning directories or inferring state from session logs.

### 2. Session Isolation & Cleanup

Before spawning a new Ralph session:
- Check for existing Ralph sub-agents via `sessions_list`
- Kill or verify completion of previous sessions
- Do NOT spawn overlapping Ralph sessions on same codebase

**Anti-pattern:** Spawning Ralph v2 while v1 is still running = file conflicts, race conditions, lost work.

### 3. Explicit Path Verification

Never assume directory structure. At start of each iteration:

```typescript
// Verify current working directory
const cwd = process.cwd();
console.log(`Working in: ${cwd}`);

// Verify expected paths exist
if (!fs.existsSync('./src/app')) {
  console.error('Expected ./src/app, found:', fs.readdirSync('.'));
  // Adapt or fail explicitly
}
```

**Why:** Ralph may be spawned from different contexts with different working directories.

### 4. Completion Signal Protocol

When done, Ralph MUST:

1. Write final `PROGRESS.md` with "## Status: COMPLETE"
2. List all created/modified files
3. Exit cleanly (no hanging processes)

Example completion PROGRESS.md:

```markdown
# Ralph: Influencer Detail Page

## Status: COMPLETE ✅

**Finished:** [ISO timestamp]

### Final Verification
- [x] TypeScript: Pass
- [x] Tests: Pass  
- [x] Build: Pass

### Files Created
- `src/app/feature/page.tsx`
- `src/app/api/feature/route.ts`

### Testing Instructions
1. Run: `npm run dev`
2. Visit: `http://localhost:3000/feature`
3. Verify: [specific checks]
```

### 5. Error Handling Requirements

If Ralph encounters unrecoverable errors:

1. Log to PROGRESS.md with "## Status: BLOCKED"
2. Describe blocker in detail
3. List attempted solutions
4. Exit cleanly (don't hang)

**Do not silently fail.** A Ralph that stops iterating with no progress log is indistinguishable from one still working.

### 6. Iteration Time Limits

Set explicit iteration timeouts:

```markdown
## Operational Parameters
- Max iteration time: 10 minutes
- Total session timeout: 60 minutes
- If iteration exceeds limit: Log blocker, exit
```

**Why:** Prevents infinite loops on stuck tasks, allows parent agent to intervene.

## Memory Updates

After each Ralph Mode session, document:

```markdown
## [Date] Ralph Mode Session

**Project:** [project-name]
**Duration:** [iterations]
**Outcome:** success / partial / blocked
**Learnings:**
- What worked well
- What needs adjustment
- Patterns to add to AGENTS.md
```

## Appendix: Hall of Failures

Common anti-patterns observed:

| Anti-Pattern | Consequence | Prevention |
|--------------|-------------|------------|
| No progress logging | Parent agent cannot determine status | Mandatory PROGRESS.md |
| Silent failure | Work lost, time wasted | Explicit error logging |
| Overlapping sessions | File conflicts, corrupt state | Check/cleanup before spawn |
| Path assumptions | Wrong directory, wrong files | Explicit verification |
| No completion signal | Parent waits indefinitely | Clear COMPLETE status |
| Infinite iteration | Resource waste, no progress | Time limits + blockers |
| Complex initial prompts | Sub-agent never starts (empty session logs) | SIMPLIFY instructions |

## NEW: Session Initialization Best Practices (2025-02-07)

### Problem: Sub-agents spawn but don't execute
**Evidence:** Empty session logs (2 bytes), no tool calls, 0 tokens used

### Root Causes
1. **Instructions too complex** - Overwhelms isolated session initialization
2. **No clear execution trigger** - Agent doesn't know to start
3. **Branching logic** - "If X do Y, if Z do W" confuses task selection
4. **Multiple files mentioned** - Can't decide which to start with

### Fix: SIMPLIFIED Ralph Task Template

```markdown
## Task: [ONE specific thing]

**File:** exact/path/to/file.ts
**What:** Exact description of change
**Validate:** Exact command to run
**Then:** Update PROGRESS.md and exit

## Rules
1. Do NOT look at other files
2. Do NOT "check first"
3. Make the change, validate, exit
```

### BEFORE (Bad - causes stalls):
```
Fix all TypeScript errors across these files:
- lib/db.ts has 2 errors
- lib/proposal-service.ts has 5 errors
- route.ts has errors
Check which ones to fix first, then...
```

### AFTER (Good - executes):
```
Fix lib/db.ts line 27:
Change: PoolClient to pg.PoolClient
Validate: npm run typecheck
Exit immediately after
```

### CRITICAL: Single File Rule
Each Ralph iteration gets ONE file. Not "all errors", not "check then decide". ONE file, ONE change, validate, exit.

### CRITICAL: Update PROGRESS.md
**MANDATORY:** After EVERY iteration, update PROGRESS.md with:
```markdown
## Iteration [N] - [Timestamp]

### Status: Complete ✅ | Blocked ⛔ | Failed ❌

### What Was Done
- [Specific changes made]

### Validation
- [Test/lint/typecheck results]

### Next Step
- [What should happen next]
```

**Why this matters:** Cron job reads PROGRESS.md for status updates. If not updated, status appears stale/repetitive.

### Debugging Ralph Stalls
If Ralph stalls:
1. Check session logs (should show tool calls within 60s)
2. If empty after spawn → instructions too complex
3. Reduce: ONE file, ONE line number, ONE change
4. Shorter timeout forces smaller tasks (300s not 600s)

### Fixing Stale Status Reports
If cron reports same status repeatedly:
1. Check PROGRESS.md was updated by sub-agent
2. If not updated → sub-agent skipped documentation step
3. Update skill: Add "MANDATORY PROGRESS.md update" to prompt
4. Manual fix: Update PROGRESS.md to reflect actual state

## Summary
Ralph works when: Single file focus + explicit change + validate + exit
Ralph stalls when: Complex decisions + multiple files + conditional logic

AIエージェントの人気スキル

データ月:2026-09 · ダウンロード数、スター数、インストール数は公開スキルレジストリ(ClawHub、SkillHub)から月単位で集計しています。詳しくはデータの方法論をご覧ください。