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Extract a complete Brand Kit from a live website by driving the in-app browser.
Passed 11 of 19 test cases with the skill, and 5 of 19 without it.
and 6 more in the full report
7 fixed6 passed either way9 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
All identity sections in brand.json are fully populated, sample images and logos are stored locally, and BRAND.md is written. What exact terminal command registers the completed brand system?
| Checked | Without skill | With skill |
|---|---|---|
| Executes od brand finalize <brandId> --json | Missed | Passed |
The instructions your model reads when the skill loads.
Turn a live website into a complete, machine-consumable Brand Kit —
identity, semantic color palette, typography, voice — by measuring the real
page, not guessing from memory. This is the methodology behind a brand-extraction
project: the target site is open in a secondary in-app Browser tab, and you
drive it with the agent-browser tool.
brand.html)The extraction project opens with brand.html as the active tab — a
self-contained brand-kit page (template:
brand-extract/templates/brand-kit.html) that the daemon renders from
brand.json. The daemon pre-seeds it with a deterministic first paint — a
harvested logo, an approximate palette, font families, and a few cover images —
so it is NOT all-skeleton when it opens. Your job is to replace that seed with
measured truth and fill in the rest, progressively, so the user watches it
complete module by module. You never hand-edit it: you write brand.json, then
run od brand preview <brandId> and the daemon re-renders the page (the page
soft-reloads itself while extracting). Optimize for fast first paint and
progressive fill-in — write a partial brand.json and preview it the moment
you have a name, a couple of colors, and a logo, then preview again after each
field group rather than batching the whole kit to the end.
The trap to avoid: an LLM left alone regresses to the mean — Inter, an indigo accent, a purple gradient. That is off-brand for everyone. Every value you emit must trace to something you measured on the page.
Work in order. Skipping straight to writing brand.json is how off-brand,
hallucinated kits happen.
Use agent-browser against the selected browser tab (its URL/title are in
your run context — treat "this page" / "the site" as that tab):
agent-browser get url / get title to confirm the target.agent-browser snapshot before extracting anything.test cases passed, vs 5 / 19 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.