Getting Started

This guide takes you from zero to running your first AI-assisted task in under five minutes.

Prerequisites

Neovim 0.10+

Dwight uses modern Neovim APIs (vim.uv, vim.health, native LSP). Check with :version.

A CLI Agent Backend

Dwight delegates agentic tasks to a CLI tool. You need at least one installed and authenticated:

Claude Code (default, recommended):

npm install -g @anthropic-ai/claude-code
claude login

OpenAI Codex:

npm install -g @openai/codex
# Set OPENAI_API_KEY in your environment

Gemini CLI:

npm install -g @google/gemini-cli
# Authenticate via gcloud or set GOOGLE_API_KEY

Git

Dwight uses git for checkpoints during multi-step tasks and for diff review. Your project must be a git repository.

API Key (optional)

An API key is only needed for single-shot commands like :DwightGenSkill and :DwightRefactor. If you only use :DwightAuto and :DwightAgent, the CLI backend handles authentication.

Set one of: ANTHROPIC_API_KEY, OPENAI_API_KEY, or GOOGLE_API_KEY.

Installation

lazy.nvim

{
  "otaleghani/dwight.nvim",
  dependencies = {
    "nvim-lua/plenary.nvim",
    "nvim-treesitter/nvim-treesitter",
  },
  config = function()
    require("dwight").setup({
      backend = "claude_code",
    })
  end,
}

packer.nvim

use {
  "otaleghani/dwight.nvim",
  requires = {
    "nvim-lua/plenary.nvim",
    "nvim-treesitter/nvim-treesitter",
  },
  config = function()
    require("dwight").setup({
      backend = "claude_code",
    })
  end,
}

Optional Dependencies

These aren't required but significantly improve the experience:

  • telescope.nvim — better pickers for skills, features, sessions (falls back to vim.ui.select)
  • nvim-treesitter — signature extraction for feature context (highly recommended)

Verify Your Setup

After installing, run the health check:

:checkhealth dwight

This validates your backend, API keys, git status, project initialization, and test command detection. Fix any errors before proceeding.

Initialize Your Project

Navigate to your project root and run:

:DwightInit

This creates the .dwight/ directory with built-in coding skills. Add .dwight/ to your .gitignore (Dwight creates one automatically inside .dwight/).

Bootstrap Pragmas

Next, let Dwight analyze your codebase and add feature pragmas:

:DwightBootstrap

Choose Agentic mode (recommended) — it reads your source code to understand the architecture and adds meaningful @feature: comments. Quick mode uses directory scanning and is faster but shallower.

After bootstrapping, your files will have pragma comments like:

// HTTP server and routing for the web interface. @feature:web-server
package web

# Database models and ORM configuration. @feature:database
from sqlalchemy import create_engine

Verify Features

Check that pragmas were added correctly:

:DwightFeatures    " List all detected features
:DwightCoverage    " See how many files are tagged

Your First Task

Single Agent Task

Try a focused task on one feature:

:DwightAgent Add input validation to the user creation endpoint

The agent will read relevant files, make changes, and verify the result. You'll see a live status buffer with tool calls and progress.

Multi-Step Auto Task

For larger work, use Auto mode:

:DwightAuto Create a REST API for user management with CRUD endpoints

Dwight will:

  1. Break this into sub-tasks (e.g., "Create user model", "Add list endpoint", "Write tests")
  2. Execute each sub-task through an agent
  3. Run your test command after each step
  4. Git commit after each passing step
  5. Show a summary when done

Review What Changed

After a task completes:

:DwightDiffReview   " See the full diff in a split buffer
:DwightReplay       " Step through the session event by event
:copen              " QuickFix list of all changed files

Next Steps