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ego-jev uses one typed System One decision per DOM step to choose a bounded browser operation and target; ego-browser carries it out. The skill is an inner loop for ego lite, not a standalone browser or a replacement for the planner. That division can make action selection more structured, but the project’s speed and cost figures are maintainer-reported—not independently verified benchmarks.
What ego-jev does in the browser loop
The project describes a repeating sequence: ego-browser observes the page, ego-jev receives a fresh snapshot and a numbered list of interactive elements, and Jev selects an operation and its target together. Local execution code maps that choice to a browser action, then the page is observed again. The model’s choice is bounded by the available operations and page targets rather than being a free-form browser command.
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The README says Jev does not write text or view screenshots. It is a decision layer within ego lite’s browser workflow; it does not replace the browser runtime, the planner, or other capabilities needed to complete a task. The implementation-oriented starting points are the project’s README, skill instructions, loop script, and the project reference material for options and thresholds.
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The README lists click, fill, and select operations, as well as scroll, wait, done, escalate, and blocked outcomes. It describes operation-specific speculative targets: although candidate targets may be considered, only the target associated with the chosen operation can be executed. This design makes the selector’s result usable by an executor with a known action vocabulary.
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That structure is useful when the next step is choosing among visible, supported controls. It does not mean Jev can handle every interaction. In particular, the README says it does not generate the text a form may need, interpret screenshot-only interfaces, or take over broader task reasoning.
What gets escalated, and who verifies completion?
The maintainer documents escalation for login, payment, free-text entry, canvas work, and content reading. The README also describes guarded actions—including pay, delete, upload, and confirm—as escalation cases, and says low confidence or repeated actions can trigger escalation as well. These are project-described policy safeguards, not independent proof that every risky action will always be prevented.
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A done choice is not evidence by itself that the user’s task succeeded. The README assigns that check to a caller-provided verify step. In practice, the planner or calling workflow still needs to decide whether the requested outcome is actually present and what to do if verification fails.
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The project lists three prerequisites for live use: ego lite installed and onboarded, the ego-browser agent skill, and an API credential. It documents direct TypeSafe access with TYPESAFE_API_KEY and a Vercel AI Gateway route with AI_GATEWAY_API_KEY. Installation choices in the README include the Skills CLI, GitHub CLI, and a Claude Code plugin:
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npx skills add ZephyrDeng/ego-jev- Install through GitHub CLI, following the README’s current command and instructions.
- Use the documented Claude Code plugin option if that is your environment.
Follow the README for the exact install and configuration steps for your environment, then use its documented offline mock-decider self-test to exercise the loop without a live model key. That mock test can check the local decision flow; it does not measure live model latency or establish that a real site task will succeed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the speed and cost figures do—and do not—show
The ego-jev README reports approximately 300–550 ms per direct TypeSafe decision for pages with 20–120 elements, and about 20 ms for snapshotting plus DOM traversal. It also reports approximately 11K input tokens and $0.0005 per decision, with output described as free. These are project-reported figures, not independently reproduced measurements; the README excerpt does not establish that they apply across websites, network conditions, backends, or task types.
Those per-decision numbers are not an end-to-end comparison with a conventional browser agent. A meaningful comparison would use the same pages and conditions and account for the full task: bounded typed selection versus free-form reasoning, text generation, browser execution, escalation, verification, completion rate, latency, and cost. The project material describes the design and reports figures, but does not provide controlled comparative results for ego-jev.
A separate ecosystem-catalog entry discusses a browser-use/jev-ultrafast example and an author-reported Google Flights result. That is a different example, not an ego-jev benchmark, and the catalog says its inclusion is not a performance or safety endorsement. It should not be used to infer ego-jev’s speed or reliability.
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