Introduction

Wiggum is a CLI tool and MCP server that generates structured task files for autonomous AI coding loops. It codifies the Ralph Wiggum loop pattern — an orchestrator agent that drives subagents through a dependency-ordered task list until an entire project is implemented, hands-off.

What it does

Given a structured plan definition (TOML), Wiggum produces:

• Task files (tasks/T{NN}-{slug}.md) — Structured markdown specs with goals, dependencies, implementation guidance, test requirements, preflight commands, and exit criteria
• Progress tracker (PROGRESS.md) — A phase/task table with status tracking and learnings
• Orchestrator prompt (orchestrator.prompt.md) — The agent-mode prompt that drives the loop
• Implementation plan (IMPLEMENTATION_PLAN.md) — Architecture overview for subagent context
• Agents manifest (AGENTS.md) — Agent role definitions

Why it exists

Setting up an AI orchestration loop currently requires hand-authoring all of these artifacts. The structural and mechanical parts — numbering, dependency wiring, progress tables, preflight commands, orchestrator boilerplate — should be generated. The creative parts — what to build, architecture decisions, implementation details — come from the user.

Design principles

• Agent-agnostic — Wiggum generates artifacts, not agent invocations. Works with any AI coding tool that can read markdown.
• Scaffold, don’t execute — Wiggum produces plans and task files. Execution is someone else’s job.
• Language-aware — Ships with profiles for Rust, Go, TypeScript, Python, Java, C#, Kotlin, Swift, Ruby, and Elixir, providing sensible defaults for build, test, and lint commands.