What Is Wiggum CLI? The Autonomous Coding Agent That Ships Features While You Sleep

The short version

Wiggum CLI is an open-source command-line tool that turns feature requests into shipped code — autonomously. You describe what you want in plain English, Wiggum interviews you to nail down the details, generates an implementation-ready spec, then hands it to a coding agent (Claude Code, Codex, or any CLI-based agent) to execute in an autonomous loop.

According to the 2025 Stack Overflow Developer Survey, 84% of developers use or plan to use AI coding tools — up from 76% the year before. But most of those tools — autocomplete, inline suggestions, chat-based code generation — still require you to sit at the keyboard. Wiggum is designed for the next step: fully autonomous feature development.

Install it in one line:

Why another AI coding tool?

Most AI coding tools help you write code faster while you’re sitting at the keyboard. Copilot autocompletes your lines. Cursor helps you refactor. They’re great at accelerating your workflow.

Wiggum operates at a different level. Instead of helping you write code, it helps you specify what needs to be built — then builds it without you.

The difference is like giving someone driving directions turn-by-turn versus handing them a detailed itinerary and letting them navigate. Wiggum generates the itinerary, and your coding agent drives.

How it works

Wiggum has three commands that form a complete pipeline:

1. Scan with wiggum init

Point Wiggum at any project and it auto-detects your tech stack — frameworks, languages, file structure, conventions. Zero config. This context feeds into every spec and loop to ensure generated code actually fits your project.

wiggum init

It produces a structured context file that captures how your project works, not just what files exist.

2. Interview with wiggum new

Describe what you want to build. Wiggum doesn’t just take your description at face value — it interviews you using AI that understands your codebase. It asks clarifying questions about edge cases, design decisions, and tradeoffs you might not have considered.

The output is a detailed, implementation-ready spec in markdown — structured so any coding agent can execute it consistently.

3. Execute with wiggum run

This is where the Ralph loop kicks in. Wiggum hands your spec to a coding agent and runs autonomous plan-implement-test-verify-PR cycles. Each phase has checkpoints. The agent plans the implementation, writes the code, runs tests, verifies everything works, and opens a pull request.

You can monitor progress in the TUI, background the process, and come back later to review the results.

What makes Wiggum different

Spec-first architecture. Most autonomous coding tools skip the specification step. They take a vague description and start writing code immediately. Wiggum forces a structured specification process — the AI interview — before any code gets written. GitClear’s 2025 analysis of 211 million lines of code changes found that AI-assisted development has driven copy-pasted code from 8.3% to 12.3% of all changes, while refactoring dropped from 25% to under 10%. Structured specifications reduce this pattern by giving the agent clear, unambiguous instructions upfront.

Codebase-aware context. The init scan means Wiggum understands your project’s patterns, conventions, and dependencies. Generated specs reference your actual file structure and coding style, not generic best practices.

Agent-agnostic execution. Wiggum generates specs that work with any CLI-based coding agent. It’s been tested with Claude Code and Codex, but the specs are just markdown — any agent that can read a file and execute code can use them.

The Ralph loop. Named after the Ralph loop technique by Geoffrey Huntley, this is the autonomous execution engine. It’s not just “run the agent until it’s done” — it’s a structured multi-phase loop with plan, implement, test, verify, and PR review stages.

Who it’s for

Wiggum is for developers who want to ship features faster by delegating implementation to AI agents — without sacrificing code quality. Sonar’s 2026 State of Code survey found that 42% of committed code is now AI-generated, yet 96% of developers don’t fully trust AI output and only 48% always verify it before committing. Wiggum addresses this verification gap — instead of line-by-line assistance you have to manually check, it generates structured specs and runs autonomous loops with built-in verification phases.

It works best when you know what you want to build but want to automate the how.

Getting started

Then, in your project directory:

wiggum init
wiggum new
wiggum run

That’s it. Three commands from zero to pull request.

The CLI is free and open source. You bring your own API keys. Pro plans add managed keys, a web dashboard, and push notifications — but the core tool is yours to use without limits.

Check out the GitHub repository for full documentation, or see the roadmap and pricing.