Setup Ralph
Set up and configure Geoffrey Huntley's original Ralph Wiggum autonomous coding loop in...
Install
npx skills add glittercowboy/taches-cc-resources --skill setup-ralph -g -a claude-code -y- Repository
- glittercowboy/taches-cc-resources2k
- Category
- Engineering
- Updated
- 7mo ago
- License
- MIT
- Context cost
- Lightabout 1,052 tokens
- Runs scripts
- Yes, review before use
What it does
Set up and configure Geoffrey Huntley's original Ralph Wiggum autonomous coding loop in any directory with proper structure, prompts, and backpressure.
<essential_principles>
What is Ralph?
Ralph is Geoffrey Huntley's autonomous AI coding methodology that uses iterative loops with task selection, execution, and validation. In its purest form, it's a Bash loop:
while :; do cat PROMPT.md | claude ; done
The loop feeds a prompt file to Claude, the agent completes one task, updates the implementation plan, commits changes, then exits. The loop restarts immediately with fresh context.
Core Philosophy
The Ralph Wiggum Technique is deterministically bad in an undeterministic world. Ralph solves context accumulation by starting each iteration with fresh context, the core insight behind Geoffrey's approach.
Three Phases, Two Prompts, One Loop
- Planning Phase: Gap analysis (specs vs code) outputs prioritized TODO list, no implementation, no commits
- Building Phase: Picks tasks from plan, implements, runs tests (backpressure), commits
- Observation Phase: You sit on the loop, not in it, engineer the setup and environment that allows Ralph to succeed
Key Principles
Your Role: Ralph does all the work, including deciding which planned work to implement next and how to implement it. Your job is to engineer the environment.
Backpressure: Create backpressure via tests, typechecks, lints, builds that reject invalid/unacceptable work.
Observation: Watch, especially early on. Prompts evolve through observed failure patterns.
Context Efficiency: With ~176K usable tokens from 200K window, allocating 40-60% to "smart zone" means tight tasks with one task per loop achieves maximum context utilization.
File I/O as State: The plan file persists between isolated loop executions, serving as deterministic shared state, no sophisticated orchestration needed.
