javi-dots

Developer workstation setup — AI CLIs, SDD, memory, and code review in one command

Quick Start

npx javi-dots

That's it. An interactive TUI walks you through selecting AI CLIs, then installs everything: skills, configs, orchestrators, persistent memory, SDD workflow, and optionally code review.

What It Does

javi-dots is the single entry point for setting up an AI-powered developer workstation. It orchestrates multiple tools so you don't have to install them one by one.

flowchart LR
    A["npx javi-dots"] --> B["Select AI CLIs"]
    B --> C["javi-ai install"]
    C --> D["agent-teams-lite<br/>(SDD workflow)"]
    D --> E["engram<br/>(persistent memory)"]
    E --> F{"ghagga?"}
    F -- yes --> G["ghagga init<br/>(code review)"]
    F -- no --> H["Done ✓"]
    G --> H

Setup Steps

| Step | Component | Required | Description | |------|-----------|----------|-------------| | 1 | javi-ai | ✅ | Installs skills, configs, and orchestrators for selected CLIs | | 2 | agent-teams-lite | ✅ | Clones and configures the SDD (Spec-Driven Development) framework | | 3 | engram | ✅ | Installs persistent AI memory via Homebrew, configures per CLI | | 4 | ghagga | ❌ | Optional multi-agent code review system |

Presets

| Preset | CLIs | ghagga | TUI | |--------|------|--------|-----| | full | All 6 (Claude, OpenCode, Gemini, Qwen, Codex, Copilot) | ✅ | Skipped | | minimal | Claude only | ❌ | Skipped | | custom | Interactive selection | Interactive | Full TUI |

# Full preset — everything, no prompts
npx javi-dots --preset full

# Minimal — Claude only
npx javi-dots --preset minimal

# Custom — interactive TUI (default)
npx javi-dots

Commands

| Command | Description | |---------|-------------| | setup | Set up developer workstation (default) | | doctor | Show health report of current installation | | update | Re-run setup for previously configured CLIs | | uninstall | Remove javi-dots managed files and manifests |

npx javi-dots setup        # same as just: npx javi-dots
npx javi-dots doctor       # check installation health
npx javi-dots update       # re-apply with same config
npx javi-dots uninstall    # clean removal

CLI Flags

| Flag | Type | Default | Description | |------|------|---------|-------------| | --dry-run | boolean | false | Preview changes without writing anything | | --preset | string | custom | Preset: full, minimal, custom | | --cli | string | — | Comma-separated CLIs: claude,opencode,gemini,qwen,codex,copilot | | --ghagga | boolean | — | Enable ghagga code review | | --no-ghagga | boolean | — | Disable ghagga code review |

Flags override presets. If you pass both --cli and --ghagga/--no-ghagga, the TUI is skipped entirely:

npx javi-dots setup --cli claude,opencode --ghagga --dry-run

What Gets Installed

javi-ai

The AI development layer installer. Deploys skills, configs, orchestrators, hooks, and plugins to ~/.claude/, ~/.config/opencode/, ~/.gemini/, etc. depending on which CLIs you selected.

👉 See javi-ai

agent-teams-lite (SDD)

Spec-Driven Development framework. Cloned to ~/.javidots/agent-teams-lite/ and configured for each selected CLI. Provides structured planning workflows: proposal → spec → design → tasks → apply → verify.

engram

Persistent AI memory system. Installed via Homebrew (brew install gentleman-programming/tap/engram). Each CLI gets its own engram configuration so conversations persist across sessions.

ghagga (optional)

Multi-agent code review system. When enabled, runs ghagga init to configure consensus-based AI code review in your projects.

👉 See ghagga

Manifest

Configuration is saved to ~/.javidots/manifest.json. The update and uninstall commands read this manifest to know what was installed.

Requirements

• Node.js ≥ 18
• git — required for cloning agent-teams-lite
• brew — required for installing engram (macOS/Linux)

Ecosystem

| Package | Description | |---------|-------------| | javi-dots | Workstation setup (this package) | | javi-ai | AI development layer — skills, configs, orchestrators | | javi-forge | Project scaffolding — CI, templates, AI bootstrap |

License

MIT