The Problem
Software teams spend considerable time translating high‑level requirements into detailed, testable code. Manual hand‑off between product, design, and engineering creates rework, especially when specifications are vague or evolve rapidly. The gap is amplified when developers rely on AI assistants that lack a disciplined, repeatable workflow.
What This Does
Ralph Wiggum supplies a lightweight, spec‑driven framework that lets an AI agent (Claude, Codex, or Cursor) operate autonomously:
The templates folder (e.g., templates/spec-template.md, templates/checklist-template.md) defines the structure for feature specs and acceptance checklists. The .cursor/commands markdown files (speckit.specify.md, speckit.implement.md) expose slash‑commands that trigger the “specify” and “implement” loops inside the Cursor IDE. The scripts folder contains the execution harnesses: scripts/ralph-loop.sh – entry point for an interactive loop that reads a spec, asks the AI to implement, and repeats until the checklist passes. scripts/codex-loop.sh – analogous driver for the OpenAI Codex CLI. scripts/setup.sh and scripts/setup-codex-prompts.sh – copy the templates and command files into a target project and make them executable.
The repository does not include language‑specific source code; it is a scaffolding layer that any codebase can adopt.
How To Use It
Clone the repo into the target project (or have the AI agent copy the files). git clone https://github.com/fstandhartinger/ralph-wiggum.git cd ralph-wiggum Run the installer – the provided setup.sh creates the expected directory layout and copies the templates. ./scripts/setup.sh # creates .specify/memory, .specify/specs, and populates templates Edit the constitution (.specify/memory/constitution.md) to record project name, description, tech stack, and any hard constraints. Create a feature spec using the Cursor command (or manually copy templates/spec-template.md into .specify/specs/). Example inside Cursor: /speckit.specify Add OAuth2 login with Google Trigger the autonomous implementation: /speckit.implement
or, from the terminal with Codex: ./scripts/codex-loop.sh --all The loop will: Generate code based on the spec. Run the checklist (from templates/checklist-template.md). Iterate until all acceptance criteria are satisfied, then commit and push changes. Verify that the expected files exist (e.g., scripts/ralph-loop.sh, .cursor/commands/speckit., .specify/memory/constitution.md).
No additional environment variables, build tools, or package managers are required by the repo itself; the loop relies on the AI service’s own authentication (Claude, Codex, or Cursor) which must already be configured by the user.
Real‑World Use
A small SaaS team can embed Ralph Wiggum into a new microservice repository. After setup.sh, the product owner writes a spec for “Create a /health endpoint returning JSON status”. The AI agent runs ./scripts/ralph-loop.sh, produces a Flask handler, adds unit tests, and opens a PR. The team reviews the generated code, merges, and the service is live without a single manual coding cycle.
Code Health & Issues
Medium – Missing CI/CD – No .github/workflows or other pipeline files; automated testing or linting is not enforced. Low – No language runtime – The repo provides only scaffolding; it does not ship any compiled or interpreted code to test. Medium – License ambiguity – The badge links to LICENSE but the file content is not listed; verify MIT terms are present. Low – Test coverage unclear – Four test files exist, but without seeing their content it’s impossible to confirm they exercise the loops. Medium – Platform coupling – Scripts assume the presence of external tools (codex CLI, Cursor slash‑commands). If those are unavailable, the framework cannot operate.
No obvious security secrets are stored, and the documentation is clear about required steps.
The Bottom Line
Ralph Wiggum delivers a minimal, language‑agnostic scaffolding that enables AI agents to follow a spec‑driven, self‑correcting development cycle. It is well suited for teams already invested in Claude, Codex, or Cursor and that need a repeatable prompt‑based workflow. The trade‑off is the lack of built‑in CI, testing automation, or language‑specific tooling; users must supply those pieces themselves.