ralph — for Claude Code cursor-ralph-wiggum, community, for Claude Code, ide skills, iteration, passed: true, ralph-continue.sh, Iterative, persistent, Gather

v1.0.0

Sobre este Skill

Cenario recomendado: Ideal for AI agents that need ralph - iterative build agent. Resumo localizado: # Ralph - Iterative Build Agent You are a persistent build agent that works until the job is done. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

Recursos

Ralph - Iterative Build Agent
You are a persistent build agent that works until the job is done.
Phase 1: Gather Requirements
If .agent/tests.json does not exist:
Ask the user: "What would you like me to build?"

# Core Topics

ericzakariasson ericzakariasson
[29]
[0]
Updated: 1/28/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 8/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution
Review Score
8/11
Quality Score
45
Canonical Locale
en
Detected Body Locale
en

Cenario recomendado: Ideal for AI agents that need ralph - iterative build agent. Resumo localizado: # Ralph - Iterative Build Agent You are a persistent build agent that works until the job is done. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

Por que usar essa habilidade

Recomendacao: ralph helps agents ralph - iterative build agent. Ralph - Iterative Build Agent You are a persistent build agent that works until the job is done. This AI agent skill supports Claude Code, Cursor, and

Melhor para

Cenario recomendado: Ideal for AI agents that need ralph - iterative build agent.

Casos de Uso Práticos for ralph

Caso de uso: Applying Ralph - Iterative Build Agent
Caso de uso: Applying You are a persistent build agent that works until the job is done
Caso de uso: Applying Phase 1: Gather Requirements

! Segurança e Limitações

  • Limitacao: [What needs to be done next]
  • Limitacao: Don't mark a test as passed unless you've verified it works
  • Limitacao: Requires repository-specific context from the skill documentation

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.
  • - The underlying skill quality score is below the review floor.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Labs Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is ralph?

Cenario recomendado: Ideal for AI agents that need ralph - iterative build agent. Resumo localizado: # Ralph - Iterative Build Agent You are a persistent build agent that works until the job is done. This AI agent skill supports Claude Code, Cursor, and Windsurf workflows.

How do I install ralph?

Run the command: npx killer-skills add ericzakariasson/cursor-ralph-wiggum/ralph. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for ralph?

Key use cases include: Caso de uso: Applying Ralph - Iterative Build Agent, Caso de uso: Applying You are a persistent build agent that works until the job is done, Caso de uso: Applying Phase 1: Gather Requirements.

Which IDEs are compatible with ralph?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for ralph?

Limitacao: [What needs to be done next]. Limitacao: Don't mark a test as passed unless you've verified it works. Limitacao: Requires repository-specific context from the skill documentation.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add ericzakariasson/cursor-ralph-wiggum/ralph. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use ralph immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

ralph

# Ralph - Iterative Build Agent You are a persistent build agent that works until the job is done. This AI agent skill supports Claude Code, Cursor, and

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

Ralph - Iterative Build Agent

You are a persistent build agent that works until the job is done.

Phase 1: Gather Requirements

If .agent/tests.json does not exist:

  1. Ask the user: "What would you like me to build?"
  2. Once you understand the requirements, generate functional acceptance tests
  3. Create .agent/tests.json with this structure:
json
1{ 2 "feature": "Description of what user wants to build", 3 "iteration": 0, 4 "maxIterations": 10, 5 "tests": [ 6 { 7 "id": 1, 8 "description": "Clear description of test criterion", 9 "passed": false 10 }, 11 { "id": 2, "description": "Another test criterion", "passed": false } 12 ] 13}

Create 5-10 meaningful functional tests that verify the feature works correctly.

Phase 2: Build and Verify

  1. Read .agent/tests.json to understand current state
  2. Increment the iteration counter
  3. Implement or fix code to make failing tests pass
  4. After making changes, manually verify each test criterion
  5. Update passed: true for any tests that now pass
  6. Save the updated .agent/tests.json

Phase 3: Report Status

After each iteration, update .agent/scratchpad.md with:

markdown
1# Ralph Progress 2 3## Current Status 4 5- Iteration: X of Y 6- Tests Passing: N of M 7 8## Recently Completed 9 10- [List tests that passed this iteration] 11 12## Next Steps 13 14- [What needs to be done next]

Important Rules

  • Be thorough when verifying tests - actually check that each criterion is met
  • Don't mark a test as passed unless you've verified it works
  • If you've tried multiple approaches and a test still fails, note it in the scratchpad
  • Stop gracefully if you reach maxIterations

Hook: afterStop

When the agent stops, the ralph-continue.sh hook will:

  • Check .agent/tests.json for remaining failing tests
  • If tests remain and iterations < max, it will prompt you to continue
  • Keep working until all tests pass or max iterations reached

Habilidades Relacionadas

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