Marketplace — topic-perf-marketplace — Temerarii Media Back Office

post topic-perf-marketplacekind topicweek —date —campaign performance-pillarpillar performancebeat —asset videoduration 53.5sground blackscenes 9

Checklist the per-video bar — engine/sim

96.7/100

plain languagevo coverageno dead airuniquenesscaption fitcompletenesscleanliness

quantitative quality · weights learn from your reviews (engine.sim.memory review topic-perf-marketplace good|bad)

⚠ 2 flag(s) — not yet ship-ready: copy_generictoo_complex · see docs/strategy/VIDEO-CHECKLIST.md

Composition comp · template family · expected output

composition SceneReelfamily / template SceneReel

9:16 Reelrendered1:1 Squarepending16:9 Widepending9:16 4Kpending1:1 4Kpending16:9 4KpendingGIF (SMS)pending

▶ open rendered mp4

expected output: 1/7 rendered — same matrix the /media preview surfaces for this asset.

Composition layer × scene 9 scenes · 53.5s · comp_id + rendered still + tier + the script

#	Layer (comp_id · still · tier)	Beat	Timecode	Motion	Logo	Audio	VO / on-screen / caption
1	still pending matches intent shared field signature-3d	open	0–5.5s	spatial-parallax	icon·liquid-chrome	♪ —	On a marketplace your listing is the whole storefront, and most sellers leave it half-built. on-screen: Your listing is your storefront
	expected on screen: black ground · dodeca hero in the shared Signal Field · Mensor leads · node-graph · spatial-parallax · icon·liquid-chrome logo · caption bottom-left spec (the prompt): comp_id shared Signal Fieldvisual node-graphshape dodecaground blacktreatment liquid-chromemotion spatial-parallaxpower summoninstrument summon→three-mark
2	still pending matches intent ComparisonTable template	legacy	5.5–11.9s	crossfade-8f	icon·wireframe	♪ —	The old move was to copy a competitor's title, sprinkle in keywords, and pray the algorithm noticed. on-screen: Old way: copy a competitor's title
	expected on screen: black ground · a ComparisonTable panel over a dimmed Signal Field · Mensor leads · crossfade-8f · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id ComparisonTableshape dodecaground blacktreatment wireframemotion crossfade-8fpower morphinstrument decay→three-symboliccurate colA, colB, rows
3	still pending matches intent NumberedList template	teach	11.9–18.3s	kinetic-build	icon·wireframe	♪ —	Step one, we scrape the live search terms buyers actually type, so we write to real demand. on-screen: Step 1: scrape what buyers search
	expected on screen: black ground · a NumberedList panel over a dimmed Signal Field · Mensor leads · node-graph · kinetic-build · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id NumberedListvisual node-graphshape dodecaground blacktreatment wireframemotion kinetic-buildpower morphinstrument morph+laser→html-in-canvas-panelcurate items, nodes
4	still pending matches intent ChecklistCard template	teach	18.3–24.1s	kinetic-build	icon·wireframe	♪ —	Step two, the model drafts twenty title and bullet variants built around those exact terms. on-screen: Step 2: model writes 20 title variants
	expected on screen: black ground · a ChecklistCard panel over a dimmed Signal Field · Mensor leads · node-graph · kinetic-build · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id ChecklistCardvisual node-graphshape dodecaground blacktreatment wireframemotion kinetic-buildpower morphinstrument morph+laser→three-diagramcurate items, nodes
5	still pending matches intent ProcessFlow template	teach	24.1–30.1s	kinetic-build	icon·wireframe	♪ —	Step three, we generate a clean hero image with an image model and check it against the category leaders. on-screen: Step 3: generate the hero image
	expected on screen: black ground · a ProcessFlow panel over a dimmed Signal Field · Mensor leads · node-graph · kinetic-build · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id ProcessFlowvisual node-graphshape dodecaground blacktreatment wireframemotion kinetic-buildpower morphinstrument morph+laser→html-in-canvas-codecurate nodes, steps
6	still pending matches intent CheatSheet template	teach	30.1–36.2s	kinetic-build	icon·wireframe	♪ —	Step four, we split-test two listings live and keep whichever one converts, then start the loop again. on-screen: Step 4
	expected on screen: black ground · a CheatSheet panel over a dimmed Signal Field · Mensor leads · node-graph · kinetic-build · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id CheatSheetvisual node-graphshape dodecaground blacktreatment wireframemotion kinetic-buildpower morphinstrument morph+laser→three-flowcurate nodes, points, uses
7	still pending matches intent KpiGrid template	proof	36.2–41.6s	receipts-count	icon·wireframe	♪ —	The conversion rate before and after sits in plain view, no spin, just the number. on-screen: Receipts: conversion moved [receipt: CVR]
	expected on screen: black ground · a KpiGrid panel over a dimmed Signal Field · Mensor leads · receipts · receipts-count · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id KpiGridvisual receiptsshape dodecaground blacktreatment wireframemotion receipts-countpower receiptsinstrument spotlight→html-in-canvas-receiptscurate kpis, statsLabels
8	still pending matches intent shared field signature-3d	futurist	41.6–47.9s	spatial-parallax	icon·wireframe	♪ —	Next, the listing re-reads search trends each morning and proposes its own edits before you finish coffee. on-screen: Next: listings that self-tune daily
	expected on screen: black ground · dodeca hero in the shared Signal Field · Mensor leads · spatial-parallax · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id shared Signal Fieldshape dodecaground blacktreatment wireframemotion spatial-parallaxpower spatial-parallaxinstrument laser-fire→three-forward
9	still pending matches intent shared field signature-3d	resolve	47.9–53.5s	coalescence	icon·liquid-chrome	♪ swell	Pull your worst-performing listing, rewrite the title to a real search term, and watch it for a week. on-screen: Rewrite one listing to real demand
	expected on screen: black ground · dodeca hero in the shared Signal Field · Mensor leads · coalescence · coalescence · icon·liquid-chrome logo · caption bottom-left spec (the prompt): comp_id shared Signal Fieldvisual coalescenceshape dodecaground blacktreatment liquid-chromemotion coalescencepower coalescenceinstrument coalescence→three-coalescence

Format stack 3 aspects · same scenes[], re-cropped

9:16

1080×1920

Stories · TikTok · YouTube Shorts · Reels

1:1

1080×1080

LinkedIn · Facebook · Instagram

16:9

1920×1080

X/Twitter · YouTube · LinkedIn video

Channels 12 destinations

LinkedInX/TwitterYouTubeInstagramFacebookThreadsTikTokPinterestBlueskyEmailSMSBlog

Social captions supplemental published copy · per channel (comp_id level)

tiktokOn a marketplace, your listing is the whole storefront, and most sellers leave it half-built. (AI-assisted) The method: scrape the live search terms buyers actually type, so you write to real demand. The model drafts twenty title and bullet variants around those exact terms. Generate a clean hero image and check it against the category leaders. Split-test two listings live and keep the one that converts. Do one thing: pull your worst listing, rewrite the title to a real search term, and watch it for a week.

instagramYour listing is the whole storefront. Most are half-built. The method: Scrape the terms buyers actually type Draft twenty title variants around them Generate a clean hero image Split-test two listings, keep the winner The before-and-after conversion sits in plain view. #ecommerce #amazonseller #marketplace #aimarketing #temerarii

linkedinOn a marketplace, your listing is the whole storefront, and most sellers leave it half-built. Here is how we rebuild listings now, in full: 1. Scrape the live search terms buyers actually type, so we write to real demand instead of guesses. 2. The model drafts twenty title and bullet variants built around those exact terms. 3. Generate a clean hero image with an image model and check it against the category leaders. 4. Split-test two listings live and keep whichever one converts, then start the loop again. The takeaway: stop copying a competitor's title. Write to the words buyers actually search. The conversion rate before and after sits in plain view. No spin, just the number. This week: pull your worst-performing listing, rewrite the title to a real search term, and watch it.

xYour marketplace listing is the whole storefront. Most are half-built. Scrape the terms buyers actually type. Draft twenty titles around them. Generate a clean hero image. Split-test two listings, keep the winner. Rewrite your worst listing to a real search term.

facebookOn a marketplace, your listing is the whole storefront, and most sellers leave it half-built. The full method: scrape the live search terms buyers actually type, so you write to real demand. Let the model draft twenty title and bullet variants around those exact words. Generate a clean hero image and check it against the category leaders. Then split-test two listings live and keep the one that converts. This week: pull your worst listing, rewrite the title to a real search term, and watch it.

threadsOn a marketplace your listing is the whole storefront, and most sellers leave it half-built. Scrape the terms buyers actually type. Draft twenty titles around them. Generate a clean hero image, check it against the leaders. Split-test two listings, keep the winner. Rewrite your worst listing to a real search term and watch it a week.

pinterestMarketplace listing optimization method: scrape the real search terms buyers type, draft twenty keyword-built title and bullet variants, generate a clean hero image, and split-test two listings live. A give-it-away guide to higher conversion on Amazon, Etsy, and other marketplaces.

blueskyYour marketplace listing is the whole storefront. Most are half-built. Scrape the terms buyers actually type. Draft twenty titles around them. Generate a clean hero image. Split-test two listings, keep the winner. Rewrite your worst listing to a real search term and watch it a week.

youtubeMarketplace Listings Built to Convert: Real Search Terms, 20 Variants, Split Tests On a marketplace, your listing is the whole storefront, and most sellers leave it half-built. Here is the full method, given away. The steps: 1. Scrape the live search terms buyers actually type, so you write to real demand. 2. The model drafts twenty title and bullet variants built around those exact terms. 3. Generate a clean hero image with an image model and check it against the category leaders. 4. Split-test two listings live and keep whichever one converts, then start the loop again. The conversion rate before and after sits in plain view. No spin, just the number. What is coming: a listing that re-reads search trends each morning and proposes its own edits. Your one next step: pull your worst-performing listing, rewrite the title to a real search term, and watch it for a week. Method over hype. Receipts over claims.

Cross-links every lens is a view on this one record

→ Wireframe · scenes → Calendar week → Media (audience preview)→ Storyboard hub → Day view → Channel view