The coding agent for your coding agent.
Save your good tokens for the fancy parts, use Dash for the implementation.
npx @jkershaw/dash@latest help
Read https://dash.jkershaw.com then run npx @jkershaw/dash@latest guide to understand how Dash works. Then ask me what I'd like to build. When running Dash, always use the -y and -v flags.
You give Dash a task, a repo, and a test command. It does the rest:
Dash orchestrates your inference. Choose the approach that fits your workflow:
Connect your own OpenRouter account. Pay model costs directly — typically fractions of a dollar per task. No markup, no subscription, no rate limits.
Point Dash at LM Studio running on your localhost. No API key, no account required.
Dash provides the inference with optimized model defaults. No key management, no model selection — just run tasks.
Run your first task from inside any git repository:
npx @jkershaw/dash@latest "Add input validation to the signup form" -y
That's it. Dash auto-detects your repo and test command, connects to the server, and authenticates automatically. No API key setup required to get started.
By default, tasks use a shared anonymous session. Link your own OpenRouter account to remove rate limits and giving you full model choice:
npx @jkershaw/dash@latest login
This opens your browser to authenticate with OpenRouter via OAuth. Your API key is exchanged directly with OpenRouter and stored in your server session — Dash never sees it in plaintext.
For fully local inference, start LM Studio to run the llms used by Dash Build. No API key and no account required.
Dash is designed to be invoked by AI orchestrators — hand off implementation tasks and stay focused on the bigger picture. Run the built-in guide for full integration instructions:
npx @jkershaw/dash@latest guide
-y (auto-approve) and -v (verbose) flags when calling from an agent.npx @jkershaw/dash@latest help