con un clic
setup-ralph
// Set up and configure Geoffrey Huntley's original Ralph Wiggum autonomous coding loop in any directory with proper structure, prompts, and backpressure.
// Set up and configure Geoffrey Huntley's original Ralph Wiggum autonomous coding loop in any directory with proper structure, prompts, and backpressure.
| name | setup-ralph |
| description | Set up and configure Geoffrey Huntley's original Ralph Wiggum autonomous coding loop in any directory with proper structure, prompts, and backpressure. |
<essential_principles>
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.
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.
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.
Remote Backup: The loop automatically creates a private GitHub repo and pushes after each commit. This protects against accidental data loss from autonomous operations. Requires gh CLI authenticated. Disable with RALPH_BACKUP=false.
Safety Rules: PROMPT_build.md includes critical safety rules prohibiting dangerous operations like rm -rf on project directories. Tests must run in isolated temp directories.
</essential_principles>
Wait for response before proceeding.
| Response | Workflow | |----------|----------| | 1, "set up", "setup", "new", "initialize", "create" | `workflows/setup-new-loop.md` | | 2, "understand", "learn", "concepts", "explain", "how" | `workflows/understand-ralph.md` | | 3, "customize", "modify", "change", "update", "edit" | `workflows/customize-loop.md` | | 4, "troubleshoot", "debug", "fix", "problem", "issue" | `workflows/troubleshoot-loop.md` | | Other | Clarify intent, then select appropriate workflow |After reading the workflow, follow it exactly.
<reference_index>
All in references/:
Core Concepts: ralph-fundamentals.md - Three phases, two prompts, one loop Structure: project-structure.md - Required files and directory layout Prompts: prompt-design.md - Planning vs building mode instructions Backpressure: validation-strategy.md - Tests, lints, builds as steering Best Practices: operational-learnings.md - AGENTS.md guidance and evolution </reference_index>
<workflows_index>
| Workflow | Purpose |
|---|---|
| setup-new-loop.md | Initialize Ralph structure in a directory |
| understand-ralph.md | Learn Ralph concepts and philosophy |
| customize-loop.md | Modify prompts or loop configuration |
| troubleshoot-loop.md | Debug loop issues and improve performance |
| </workflows_index> |
<success_criteria> Skill is successful when:
Search The Pirate Bay for torrents and extract magnet links via the apibay.org JSON API. Use when asked to "find a torrent", "search pirate bay", "get a magnet link", "download torrent", "find seeders", "top torrents", or any torrent search task. Can operate via CLI tool or direct API calls.
Create Model Context Protocol (MCP) servers that expose tools, resources, and prompts to Claude. Use when building custom integrations, APIs, data sources, or any server that Claude should interact with via the MCP protocol. Supports both TypeScript and Python implementations.
Create hierarchical project plans optimized for solo agentic development. Use when planning projects, phases, or tasks that Claude will execute. Produces Claude-executable plans with verification criteria, not enterprise documentation. Handles briefs, roadmaps, phase plans, and context handoffs.
Expert guidance for creating, writing, building, and refining Claude Code Skills. Use when working with SKILL.md files, authoring new skills, improving existing skills, or understanding skill structure and best practices.
Create optimized prompts for Claude-to-Claude pipelines with research, planning, and execution stages. Use when building prompts that produce outputs for other prompts to consume, or when running multi-stage workflows (research -> plan -> implement).
Deep analysis debugging mode for complex issues. Activates methodical investigation protocol with evidence gathering, hypothesis testing, and rigorous verification. Use when standard troubleshooting fails or when issues require systematic root cause analysis.