Claude Code Ultraplan and Ultrareview: Cloud Power Tools
Claude Code ultraplan and ultrareview explained: plan big changes in the cloud, review them in your browser, then catch bugs with a multi-agent review.
Evgenii Arsentev · MD, PhDReviewed for accuracy by Evgenii Arsentev, MD, PhD · 2026-08-05
Anthropic removed the Ultraplan research preview in Claude Code v2.1.222. The /ultraplan command, the ultraplan keyword trigger, and the Refine with Ultraplan plan-approval dialog option are all gone. For planning, use plan mode in your local session (/plan), or start a Claude Code on the web session directly. Ultrareview (/code-review ultra) is unaffected and works as described below.
Ultraplan and ultrareview are Claude Code's two cloud power tools: /ultraplan drafts a plan for a big change in a Claude Code on the web session while your terminal stays free, and /code-review ultra launches a fleet of reviewer agents in a cloud sandbox to find verified bugs before you merge. One plans the work, the other checks it.
I'm Evgeny, and I'll be honest: these two features sound like they're for engineering teams, but they solve a very non-programmer problem — I can't tell if the AI's plan is good, and I can't tell if its code is safe. Ultraplan gives you a plan you can read and comment on in a browser like a Google Doc. Ultrareview gives you a bug report where every finding was independently reproduced. Both are training wheels for judgment you don't have yet.
What is ultraplan in Claude Code?
Ultraplan hands a planning task from your local CLI to a Claude Code on the web session running in plan mode. Claude researches your codebase and drafts the plan in the cloud while you keep working in the terminal. This was a research preview — it has since been removed. See the callout above for alternatives.
What is ultrareview, and how is it different from a normal review?
Ultrareview is a deep code review that runs on Claude Code on the web infrastructure: a fleet of reviewer agents explores your change in parallel inside a remote sandbox, and every reported finding is independently reproduced and verified — so you get real bugs, not style nagging. Compare that to the local /review: seconds to minutes, single pass, counts toward normal usage. Ultrareview takes roughly 5 to 10 minutes, runs in the background while you keep working, and is built for pre-merge confidence on substantial changes.
/code-review ultra
Reviews the diff between your current branch and the default branch, including uncommitted changes. To review a GitHub pull request instead: /code-review ultra 1234. The old name /ultrareview still works as an alias. Track or stop it with /tasks.
How much does ultrareview cost?
Pro and Max subscribers get 3 free runs as a one-time allotment that does not refresh, and after that each review bills to usage credits — typically $5 to $25 depending on the size of the change. Team and Enterprise plans get no free runs. A run counts once the cloud session starts, so a review you stop early still spends a free run. Before launching, Claude Code shows a confirmation with the scope, your remaining free runs, and the estimated cost — no surprise charges. Ultrareview also requires signing in with a Claude.ai account; an API key alone will not do.
For a quick is this okay while iterating, the local /review is faster and counts as normal usage. Save ultrareview for changes where being wrong is expensive — pre-merge on substantial features, or when you have no senior engineer to sanity-check the output.
The kicker: before you ship a change that makes you nervous, spend one free ultrareview run. That bug report, independently verified, is the closest a non-programmer gets to having a senior team on call.
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Selected peer-reviewed papers and preprints on the AI research behind this topic.
- [1]Espejel et al. (2023). A Comprehensive Review of State-of-The-Art Methods for Java Code Generation from Natural Language Text. arXiv:2306.06371
- [2]Luo et al. (2023). WizardCoder: Empowering Code Large Language Models with Evol-Instruct. arXiv:2306.08568
- [3]Shende et al. (2026). Perceptual Self-Reflection in Agentic Physics Simulation Code Generation. arXiv:2602.12311
- [4]Ranaldi et al. (2024). Self-Refine Instruction-Tuning for Aligning Reasoning in Language Models. arXiv:2405.00402
- [5]Sapkota et al. (2025). Object Detection with Multimodal Large Vision-Language Models: An In-depth Review. arXiv:2508.19294
- [6]Jang et al. (2026). Self-Refining Video Sampling. arXiv:2601.18577
- [7]Zucchet et al. (2025). How do language models learn facts? Dynamics, curricula and hallucinations. arXiv:2503.21676
Author
Evgenii Arsentev
MD, PhD · AI transformation executive
Reading is the blue pill
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