agent-wiggum-cli-federiconeri

name	agent-wiggum-cli-federiconeri
description	Autonomous coding agent that scans codebases, generates feature specs via AI interviews, and runs Ralph loops via Claude Code, Codex CLI, or any CLI-based agent. Reads GitHub backlog, runs loops, and auto-merges PRs. Use when automating feature development end-to-end, running autonomous coding loops, managing GitHub backlogs with AI, or implementing the Ralph loop methodology.

Wiggum CLI v0.18.3

Overview

Wiggum is an AI agent CLI by Federico Neri that plugs into any codebase and ships features autonomously. It works in two phases. First, Wiggum itself is the agent: it scans your project, detects your stack (80+ technologies), and runs an AI-guided interview to produce detailed specs, prompts, and scripts tailored to your codebase. Then it delegates coding loops to Claude Code or Codex CLI, running implement → test → fix cycles until completion.

Built on the Ralph loop technique pioneered by Geoffrey Huntley, Wiggum provides structured phase isolation (plan, implement, test, verify, PR) rather than undifferentiated retry loops. Specs are agent-agnostic markdown — they work with any CLI-based coding agent.

         Wiggum (agent)                    Coding Agent
  ┌────────────────────────────┐    ┌────────────────────┐
  │                            │    │                    │
  │  Scan ──▶ Interview ──▶ Spec ──▶  Run loops           │
  │  detect      AI-guided   .ralph/   implement         │
  │  80+ tech    questions   specs     test + fix        │
  │  plug&play   prompts     guides    until done        │
  │                            │    │                    │
  └────────────────────────────┘    └────────────────────┘
       runs in your terminal          Claude Code / Codex CLI

When to Use

• Automating feature development from spec to merged PR without manual coding
• Running autonomous coding loops against existing codebases (any language, any framework)
• Generating implementation-ready specs through AI-guided interviews grounded in codebase context
• Processing GitHub backlogs autonomously with wiggum agent (priority scheduling, dependency ordering, auto-merge)
• Implementing the Ralph loop methodology with phase-level checkpoints instead of bash-script retry loops
• Setting up CI pipelines for autonomous feature delivery with headless mode

Core Workflow: Three Commands

npm install -g wiggum-cli      # install (or use npx wiggum-cli)

wiggum init                    # Phase 1: scan codebase, detect stack, generate context
wiggum new user-auth           # Phase 2: AI interview → detailed feature spec
wiggum run user-auth           # Phase 3: autonomous Ralph loop execution

Interactive Mode (TUI)

Running wiggum with no arguments opens the terminal UI — the recommended way to use Wiggum:

• /init or /i — Scan project, configure AI provider
• /new <feature> or /n — AI interview → feature spec
• /run <feature> or /r — Run autonomous coding loop
• /monitor <feature> or /m — Monitor a running feature in real-time
• /issue [query] — Browse GitHub issues and start a spec from issue context
• /agent [flags] or /a — Run autonomous backlog executor
• /sync or /s — Re-scan project, update context
• /config [...] or /cfg — Manage API keys and loop settings

Headless Mode

For CI pipelines, cron jobs, or integration with other agents:

wiggum new --auto --goals "add rate limiting to API" --issue #42
wiggum sync
wiggum agent --stream --max-items 5

Generated Files

After wiggum init, a .ralph/ directory is created:

.ralph/
├── ralph.config.cjs          # Stack detection results + loop config
├── prompts/
│   ├── PROMPT.md             # Implementation prompt
│   ├── PROMPT_feature.md     # Feature planning
│   ├── PROMPT_e2e.md         # E2E testing
│   ├── PROMPT_verify.md      # Verification
│   ├── PROMPT_review_manual.md  # PR review (stop at PR)
│   ├── PROMPT_review_auto.md    # PR review (review, no merge)
│   └── PROMPT_review_merge.md   # PR review (review + auto-merge)
├── guides/
│   ├── AGENTS.md             # Agent instructions
│   ├── FRONTEND.md           # Frontend patterns
│   ├── SECURITY.md           # Security guidelines
│   └── PERFORMANCE.md        # Performance patterns
├── scripts/
│   └── feature-loop.sh       # Main loop script
├── specs/
│   └── _example.md           # Example spec template
└── LEARNINGS.md              # Accumulated project learnings

Requirements

• Node.js >= 18.0.0
• Git (for worktree features)
• GitHub CLI (gh) for /issue browsing and backlog agent operations
• An AI provider API key (Anthropic, OpenAI, or OpenRouter)
• A supported coding CLI: Claude Code and/or Codex CLI

AI Providers

Provider	Environment Variable
Anthropic	ANTHROPIC_API_KEY
OpenAI	OPENAI_API_KEY
OpenRouter	OPENROUTER_API_KEY

Optional services:

• TAVILY_API_KEY — Web search for current best practices
• CONTEXT7_API_KEY — Up-to-date documentation lookup

Keys are stored in .ralph/.env.local and never leave your machine.

Advanced Topics

The Ralph Loop Methodology: How the loop really works — phase isolation, checkpoints, error recovery → Ralph Loop Deep Dive

CLI Command Reference: Full coverage of all commands with flags and options → CLI Reference

Agent Mode & Backlog Automation: Autonomous GitHub backlog processing with dependency scheduling → Agent Mode

Configuration & Loop Tuning: Model selection, review modes, worktree isolation, prompt templates → Configuration