Agent Wiggum Cli Federiconeri

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 or /n — AI interview → feature spec
• /run or /r — Run autonomous coding loop
• /monitor 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](reference/01-ralph-loop-methodology.md)

CLI Command Reference: Full coverage of all commands with flags and options → [CLI Reference](reference/02-cli-reference.md)

Agent Mode & Backlog Automation: Autonomous GitHub backlog processing with dependency scheduling → [Agent Mode](reference/03-agent-mode.md)

Configuration & Loop Tuning: Model selection, review modes, worktree isolation, prompt templates → [Configuration](reference/04-configuration.md)

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: tangledgroup
• Source: tangledgroup/tangled-skills
• License: MIT

Install and usage instructions live in the source repository linked above.