ultrawork. Skip the study. If a task feels hard, add ultrawork (or ulw) to your prompt. The agent reads the codebase before touching a line, plans, proves each step before moving on, checks the result on the real surface, and stops when it's done.
Hundreds of agents, one keyword. Type mass ulw and the whole job becomes a graph of agents: deep research over thousands of sources, a full machine-learning pipeline, a migration nobody wants to do. Each part runs on the model that fits it, in parallel, and gets checked before anything is called done.
Kibitzer. Your agent doesn't forget you, or your work. Memory lives in a git repository of markdown files. A small, cheap model sits beside the expensive main agent, reads what you've done before, and nudges the main agent before it repeats a mistake. You stop re-explaining yourself.
Mix models without thinking about it. Use the frontier models you love together with the cheap ones. Just sign in: /login covers Claude, ChatGPT, Kimi and GLM subscriptions. Every model ships with a system prompt tuned for it, so you never pick the wrong one.
High-quality skills, only the ones you need. Your model is smart, but it still doesn't know a lot about solving real-world problems. OmO ships the missing knowledge as skills, browser use included, and loads only what the task needs.
Everything else is crafted. Independent tool calls run together in one small program, so twenty reads are one round trip. Malformed tool arguments are repaired before they run. Waiting on a build or a deploy is a subscription, not polling. Long work keeps a record of what was checked, so a restarted session picks up where it stopped. Config, skills and prompts reload live.
Last modified 03 November 2026