Autonomous AI coding with spec-driven development. Implements Geoffrey Huntley's iterative bash loop methodology where agents work through specs one at a time, outputting a completion signal only when acceptance criteria are 100% met.

Ralph Wiggum Autonomous AI coding with spec driven development What is Ralph Wiggum? Ralph Wiggum combines Geoffrey Huntley's iterative bash loop with spec driven development for fully autonomous AI assisted software development. The key insight: Fresh context each iteration . Each loop starts a new agent process with a clean context window, preventing context overflow and degradation. When to Use This Skill Use Ralph Wiggum when: You have multiple specifications/features to implement You want the AI to work autonomously through tasks You need consistent, verifiable completion of acceptance criteria You want to avoid context window problems in long sessions How It Works Installation Quick Install (via Skill Installers) Full Setup (Recommended) For full Ralph Wiggum setup with constitution and interview: The agent will guide you through a lightweight, pleasant setup : 1. Quick Setup (~1 min) — Create directories, download scripts 2. Project Interview — Focus on your vision and goals (not tech details) 3. Constitution — Create a guiding document for all sessions 4. Next Steps — Clear guidance on creating specs and starting Ralph For existing projects, the agent detects your tech stack automatically. The interview prioritizes understanding what you're building and why . Core Concepts 1. Fresh Context Each Loop Each iteration of the Ralph loop starts a new AI agent process. This means: No context window overflow No degradation over time Clean slate for each task 2. Shared State on Disk State persists between loops via files: specs/ — Feature specifications with acceptance criteria ralph history.txt — Log of breakthroughs, blockers, learnings IMPLEMENTATION PLAN.md — Optional detailed task breakdown 3. Completion Signal The agent outputs <promise DONE</promise ONLY when: All acceptance criteria are verified Tests pass Changes are committed and pushed The bash loop checks for this phrase. If not found, it retries. 4. Backpressure via Tests Tests, lints, and builds act as guardrails. The agent must fix issues before outputting the completion signal. Usage Creating Specifications The key to success: Each spec needs clear, testable acceptance criteria . This is what tells Ralph when a task is truly "done." Good criteria: "User can log in with Google and session persists" Bad criteria: "Auth works correctly" The more specific your acceptance criteria, the better Ralph performs. Running the Loop Logging (All Output Captured) Every loop run writes all output to log files in logs/ : Session log: logs/ralph session YYYYMMDD HHMMSS.log (entire run, including CLI output) Iteration logs: logs/ralph iter N YYYYMMDD HHMMSS.log (per iteration CLI output) Codex last message: logs/ralph codex output iter N .txt Two Modes Mode Purpose Command build (default) Pick spec, implement, test, commit ./scripts/ralph loop.sh plan (optional) Create detailed task breakdown ./scripts/ralph loop.sh plan Key Principles Let Ralph Ralph Trust the AI to self identify, self correct, and self improve. Observe patterns and adjust prompts. YOLO Mode For Ralph to work effectively, enable full autonomy: Claude Code: dangerously skip permissions Codex: dangerously bypass approvals and sandbox ⚠️ Use at your own risk. Only in sandboxed environments. Links GitHub: https://github.com/fstandhartinger/ralph wiggum Website: https://ralph wiggum.ai Original methodology: [Geoffrey Huntley's how to ralph wiggum](https://github.com/ghuntley/how to ralph wiggum)

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