Co-Pilot / 辅助式
更新于 25 days ago

ralphie

Sskylarbarrera
0.0k
skylarbarrera/ralphie
84
Agent 评分

💡 摘要

Ralphie通过结构化的AI驱动迭代和git集成来自动化编码任务。

🎯 适合人群

寻求自动化编码帮助的软件开发人员监督开发工作流程的项目经理旨在提高代码质量和效率的技术团队探索自主编码解决方案的AI爱好者需要快速原型开发的初创公司

🤖 AI 吐槽:看起来很能打,但别让配置把人劝退。

安全分析中风险

风险:Medium。建议检查:是否执行 shell/命令行指令;是否发起外网请求(SSRF/数据外发);文件读写范围与路径穿越风险;依赖锁定与供应链风险。以最小权限运行,并在生产环境启用前审计代码与依赖。

Ralphie

Autonomous AI coding loops.

Based on the Ralph Wiggum technique: describe what you want → AI builds it task by task → each task gets committed → come back to working code.

ralphie spec "Todo app with auth" # Creates spec ralphie run --all # Builds until done

Quick Start

1. Install Ralphie

npm install -g ralphie

2. Set up your AI provider

# Claude (default) curl -fsSL https://anthropic.com/install-claude.sh | sh # Or Codex npm install -g @openai/codex && export OPENAI_API_KEY=sk-... # Or OpenCode npm install -g opencode-ai && opencode auth login

3. Build something

# Create a spec ralphie spec "REST API with JWT auth" # Run the loop ralphie run --all git log --oneline # See what was built

What happens next? Ralphie generates a structured spec with research and analysis, then executes task-by-task with fresh context each iteration. Progress lives in git commits—the AI can fail, the loop restarts clean.

How It Works

Each iteration:

  1. Fresh context (no accumulated confusion)
  2. Reads spec → picks next pending task
  3. Implements, tests, commits
  4. Exits → loop restarts clean

The insight: Progress lives in git, not the LLM's context. The AI can fail—next iteration starts fresh and sees only committed work.

What makes Ralphie different: Structured specs with task IDs, status tracking, size budgeting, and verify commands. The AI knows exactly what to build, how to check it worked, and when it's done. No ambiguity, no drift.

Key Features

Compound Engineering - Each failure makes the system better:

  • Research phase: Fetches framework-specific best practices from skills.sh (React, Next.js, Expo, etc.) and web research
  • Dynamic tool selection: Discovers best-in-class libraries for your stack (not hardcoded recommendations)
  • Multi-agent review: Security, performance, architecture checks before implementation
  • Learnings system: Captures failure→fix transitions as reusable knowledge
  • Quality enforcement: >80% test coverage mandatory, typed interfaces required, security by default
  • Debug logs: Full audit trail in .ralphie/logs/ viewable with ralphie logs

Senior Engineer Output - Code quality built-in:

  • Research recommends current best tools (Zod, bcrypt, expo-auth-session)
  • Specs include explicit quality requirements (tests, security, architecture)
  • Test validator blocks task completion without >80% coverage
  • Clean, maintainable code with proper separation of concerns
  • See Code Quality Standards for details

Inspired by EveryInc/compound-engineering-plugin. See Architecture docs for details.

Commands

| Command | Description | |---------|-------------| | ralphie spec "desc" | Generate spec autonomously with research + analysis | | ralphie spec --skip-research | Skip deep research phase | | ralphie spec --skip-analyze | Skip SpecFlow analysis phase | | ralphie run | Run one iteration | | ralphie run -n 5 | Run 5 iterations | | ralphie run --all | Run until spec complete | | ralphie run --review | Run multi-agent review before iteration | | ralphie run --force | Override P1 blocking (use with --review) | | ralphie run --greedy | Multiple tasks per iteration | | ralphie run --headless | JSON output for CI/CD | | ralphie init | Add to existing project | | ralphie validate | Check spec format | | ralphie status | Show progress of active spec | | ralphie spec-list | List active and completed specs | | ralphie logs | View iteration logs (with --tail, --filter) | | ralphie archive | Move completed spec to archive |

Spec Format

Ralphie works from structured specs in .ralphie/specs/active/:

# My Project Goal: Build a REST API with authentication ## Tasks ### T001: Set up Express with TypeScript - Status: pending - Size: M **Deliverables:** - Initialize npm project with TypeScript - Configure Express server - Add basic health check endpoint **Verify:** `npm run build && npm test` --- ### T002: Create User model - Status: pending - Size: S **Deliverables:** - Define User interface - Add bcrypt password hashing **Verify:** `npm test`

Tasks transition from pendingin_progresspassed/failed. See Spec Guide for best practices.

Project Structure

After ralphie init, you'll have:

  • .ralphie/specs/active/ - Generated specs with task tracking
  • .ralphie/logs/ - Timestamped logs (research, spec generation, iterations)
  • .ralphie/learnings/ - Captured failure→fix knowledge
  • .ralphie/state.txt - Iteration progress log

See Architecture docs for complete structure and file formats.

Troubleshooting

| Problem | Solution | |---------|----------| | command not found: ralphie | npm install -g ralphie | | command not found: claude | export PATH="$HOME/.local/bin:$PATH" | | Missing ANTHROPIC_API_KEY | export ANTHROPIC_API_KEY=sk-ant-... (add to .zshrc) | | Missing OPENAI_API_KEY | export OPENAI_API_KEY=sk-... (add to .zshrc) | | Stuck on same task | Check task status. Run ralphie validate | | No spec found | ralphie spec "description" to create one |

Documentation

Requirements

  • Node.js 18+
  • Claude Code CLI, OpenAI Codex CLI, or OpenCode CLI
  • Git

License

MIT

五维分析
清晰度9/10
创新性8/10
实用性8/10
完整性9/10
可维护性8/10
优缺点分析

优点

  • 有效地自动化编码任务
  • 与git良好集成以进行版本控制
  • 通过测试要求确保高代码质量
  • 支持多个AI提供商以实现灵活性

缺点

  • 依赖外部AI提供商
  • 在复杂任务中可能会造成困惑
  • 需要初始设置和配置
  • 新用户的学习曲线

相关技能

pytorch

S
toolCode Lib / 代码库
92/ 100

“它是深度学习的瑞士军刀,但祝你好运能从47种安装方法里找到那个不会搞崩你系统的那一个。”

agno

S
toolCode Lib / 代码库
90/ 100

“它承诺成为智能体领域的Kubernetes,但得看开发者有没有耐心学习又一个编排层。”

nuxt-skills

S
toolCo-Pilot / 辅助式
90/ 100

“这本质上是一份组织良好的小抄,能把你的 AI 助手变成一只 Nuxt 框架的复读机。”

免责声明:本内容来源于 GitHub 开源项目,仅供展示和评分分析使用。

版权归原作者所有 skylarbarrera.