decocms
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- ▌ Cms Friendly Props · decocmsUse when creating a section, adding or changing a field on an existing section's Props, shaping an editorial array (tabs, cards, links, banners, slides), picking a widget for a field (image, video, color, rich text, HTML, date, secret), or reviewing whether a section is editable enough for a non-technical merchant in the Studio. Covers the JSDoc annotations the schema generator recognises, the formats the Studio form actually renders, and the prop shapes that break the editor in ways code review cannot see. Not about how the component looks — about the shape of the data the editor fills in.
- ▌ Deco Bot Rendering · decocmsUse when debugging what crawlers or bots see on a deco site, verifying if SSR is active for bots, or diagnosing firstByteThresholdMS behavior.
- ▌ Deduplicate Loaders · decocmsFinds and consolidates duplicate loaders across page JSONs to reduce redundant API calls.
- ▌ Vtex Login Headless · decocmsDiagnose and fix headless login issues on VTEX storefronts — covering authorized domain setup and simulating an authenticated session by copying VtexIdclientAutCookie cookies.
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- ▌ Client JS Budget · decocmsUse when a storefront scores badly on Total Blocking Time or INP, when an interaction (opening filters, adding to cart, switching a tab, opening the menu) feels slow, when deciding whether a component needs JavaScript at all, or when a third-party tag (GTM, pixels, chat, anti-fraud) is suspected of eating the main thread. Covers how to attribute blocking time before changing anything, the CSS-first ladder that removes JS instead of deferring it, and the gating patterns that move a script off the critical path without losing the data it collects.
- ▌ HTML Size Optimizer · decocmsShrink first-byte HTML and embedded payloads: trim framework state scripts (Fresh/Next), event data attributes, JSON-LD bloat, and duplicate responsive markup so the client receives less markup and JSON.
- ▌ Pagespeed Score Model · decocmsUse when acting on a PageSpeed Insights or Lighthouse score rather than on one metric — deciding which of LCP, TBT, CLS, FCP or Speed Index is actually worth the next change, building a byte budget for LCP, explaining why a fix that improved one metric lowered the score, judging whether a target score is reachable at all, or telling a real win apart from machine noise. Covers the mobile score weights, the Lantern simulation model that makes LCP predictable from bytes, the attribution procedure that separates first-party from third-party cost, and the dead ends that were measured and rejected.