ralph-runner

📁 linuxlewis/agent-skills 📅 10 days ago
1
总安装量
1
周安装量
#46261
全站排名
安装命令
npx skills add https://github.com/linuxlewis/agent-skills --skill ralph-runner

Agent 安装分布

openclaw 1
claude-code 1

Skill 文档

Ralph Runner

Run autonomous AI coding loops that implement features from a PRD (Product Requirements Document) iteratively.

Prerequisites

  • Claude Codeclaude --version
  • ralph-cliralph --version

Quick Start

# Check status of a PRD
ralph status prd.json

# Run the loop
ralph run prd.json

# With context files
ralph run prd.json --context AGENTS.md --context ./docs

# More iterations
ralph run prd.json --iterations 20

Workflow

1. Create PRD

Create a prd.json file with user stories:

{
  "project": "MyApp",
  "branchName": "ralph/feature-name",
  "description": "Feature description",
  "userStories": [
    {
      "id": "US-001",
      "title": "Add login form",
      "priority": 1,
      "passes": false
    }
  ]
}

2. Run Ralph

ralph run prd.json --context AGENTS.md

3. Monitor Progress

ralph status prd.json

How It Works

Each iteration:

  1. Reads prd.json, picks highest priority story where passes: false
  2. Spawns fresh Claude Code instance
  3. Claude implements that ONE story
  4. Runs quality checks (typecheck, lint, tests)
  5. If passing: commits, updates prd.json, appends to progress.txt
  6. Outputs <promise>COMPLETE</promise> when all done

Memory persists via:

  • Git history (commits)
  • progress.txt (learnings)
  • prd.json (status)

CLI Reference

ralph run <prd>

ralph run prd.json [options]

Options:
  -i, --iterations <n>    Max iterations (default: 10)
  -c, --context <paths>   Context files/dirs to include
  -p, --prompt <path>     Custom prompt template
  --headed                Show Claude output live
  --dry-run               Preview without running

ralph status [prd]

ralph status prd.json

Shows progress bar and story status.

PRD Format

See references/prd-format.md for complete format specification.

Best Practices

  1. Keep stories small – Each should complete in one context window
  2. Good acceptance criteria – Be specific about “done”
  3. Include typecheck – Add “Typecheck passes” to criteria
  4. Use context – Pass in AGENTS.md and relevant docs
  5. Check progress.txt – Contains learnings from previous iterations