Back Office · office.temerarii.xyz
One published post, granular — the Social for W25 Thu, its copy, its output-policy format, and the video master it derives from. Part of the day's full output set.
postW25-Thu-social-3kindSocialweekW25dayThudate2026-06-25campaignlongform-youtubecadence5/day floor × 9 channels

Copy the published post text

Influencer
copy ready · render pending

Output-policy spec format · dimensions (asset_specs.output_policy)

formatnative cut · 9:16 · 1:1 · 16:9 dims1080×1920 · 1080×1080 · 1920×1080 cadence5/day floor × 9 channels

Channels 9 destinations

TikTokInstagramLinkedInX/TwitterFacebookThreadsPinterestBlueskyYouTube

This post a distinct social asset — its own angle, storyboard, and cuts

social-W25-Thu-3
5-distinct-social/day · 9:16 master → 1:1 / 16:9 cuts per channel

Channel-cuts this asset → 9 native captions (one master · per-platform aspect+copy)

ChannelNative caption
TiktokHow to pick influencers that actually sell (AI-assisted): ignore follower count, read the comments. Look for people asking where to buy. Let AI score the comment intent. Reach without intent sells nothing. #influencermarketing #marketing #creator
InstagramFollower count lies. Read the comments instead. Look for buying questions. #influencermarketing #creatoreconomy #marketing #socialmedia #brandpartnership
LinkedinPicking creators by follower count is a coin flip. The method: read the comments, not the count, and look for people asking where and how to buy. The takeaway: a thousand buying questions beat a million silent followers. Let a model score comment intent and surface the creators whose audiences actually act.
XPick influencers by comments, not follower count. Look for buying questions. Let AI score intent. Reach without intent sells nothing. temerarii.xyz
FacebookBig follower counts can hide dead comment sections that sell nothing. We read the comments and look for people asking where to buy, then let AI score the intent. The method is on the site.
ThreadsInfluencer tip: follower count lies. Read the comments. Look for people asking where and how to buy. A thousand buying questions beat a million silent followers. AI can score the intent.
PinterestInfluencer marketing method: pick creators by reading their comments for buying intent, not by follower count. Let AI score comment sentiment to find audiences that act. Influencer marketing tips, creator partnerships, brand strategy.
BlueskyPick influencers by comments, not counts. Look for buying questions. Reach without intent sells nothing. temerarii.xyz
YoutubePick Influencers by Comments, Not Follower Count Follower count is a coin flip. We show the method: read the comments, not the count, and look for people asking where and how to buy. A thousand buying questions beat a million silent followers. Let a model score comment intent and surface the creators whose audiences actually act.

Composition layer × scene 9 scenes · this post's OWN storyboard (distinct per asset)

#Layer (comp_id · still · tier)BeatTimecodeMotionLogoAudioVO / on-screen / caption
1s1
matches intent
shared field
signature-3d		open0–5.0sspatial-parallaxicon·color♪ bed_in
The old way picked creators by follower count and crossed its fingers.
on-screen: Picking creators used to be follower count
expected on screen: white ground · dodeca hero in the shared Signal Field · Mensor leads · node-graph · spatial-parallax · icon·color logo · caption bottom-left
spec (the prompt): comp_id shared Signal Fieldvisual node-graphshape dodecaground whitetreatment colormotion spatial-parallaxpower summoninstrument summon→Picking creators used to be follower count
2s2
matches intent
shared field
signature-3d		hook5.0–10.0skinetic-buildicon·wireframe♪ node_lock
Here is the move now: read the comments, not the follower count.
on-screen: Now we read comments, not counts
expected on screen: white ground · dodeca hero in the shared Signal Field · Mensor leads · mark · kinetic-build · icon·wireframe logo · caption bottom-left
spec (the prompt): comp_id shared Signal Fieldvisual markshape dodecaground whitetreatment wireframemotion kinetic-buildpower laser-lockinstrument laser-trace→Now we read comments, not counts
3s3
matches intent
NumberedList
template		teach10.0–15.0skinetic-buildicon·wireframe♪ node_lock
Scan a creator's comments for people asking where and how to buy.
on-screen: Scan their replies for real buyers
expected on screen: white 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 whitetreatment wireframemotion kinetic-buildpower morphinstrument morph+laser→Scan their replies for real buyerscurate items, nodes
4s4
matches intent
RankList
template		proof15.0–20.0sreceipts-counticon·wireframe♪ node_lock
A thousand buying questions beat a million bored, silent followers.
on-screen: Buying questions beat big numbers
expected on screen: white ground · a RankList panel over a dimmed Signal Field · Mensor leads · receipts · receipts-count · icon·wireframe logo · caption bottom-left
spec (the prompt): comp_id RankListvisual receiptsshape dodecaground whitetreatment wireframemotion receipts-countpower receiptsinstrument spotlight→Buying questions beat big numberscurate rows, statsLabels
5s5
matches intent
CodeWindow
template		diff20.0–25.0scrossfade-8ficon·wireframe♪ node_lock
Big reach with dead comments sells nothing, no matter how it looks.
on-screen: Big reach, dead comments, no sales
expected on screen: white ground · a CodeWindow panel over a dimmed Signal Field · Mensor leads · code · crossfade-8f · icon·wireframe logo · caption bottom-left
spec (the prompt): comp_id CodeWindowvisual codeshape dodecaground whitetreatment wireframemotion crossfade-8fpower morphinstrument morph→Big reach, dead comments, no salescurate codeLines, windowTitle
6s6
matches intent
ChecklistCard
template		teach25.0–30.2skinetic-buildicon·wireframe♪ node_lock
Let a model read the comment mood and flag accounts with real intent.
on-screen: Let AI read the comment mood
expected on screen: white 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 whitetreatment wireframemotion kinetic-buildpower morphinstrument morph+laser→Let AI read the comment moodcurate items, nodes
7s7
matches intent
StatScoreboard
template		proof30.2–35.2sreceipts-counticon·wireframe♪ node_lock
It finds the creators whose people actually act, not just scroll past.
on-screen: It finds the audience that acts
expected on screen: white ground · a StatScoreboard panel over a dimmed Signal Field · Mensor leads · receipts · receipts-count · icon·wireframe logo · caption bottom-left
spec (the prompt): comp_id StatScoreboardvisual receiptsshape dodecaground whitetreatment wireframemotion receipts-countpower receiptsinstrument spotlight→It finds the audience that actscurate pillar, stats, statsLabels
8s8
matches intent
NodeGraphCard
template		step35.2–40.400000000000006skinetic-buildicon·wireframe♪ node_lock
Read the comments, score the buying intent, then partner with the real ones.
on-screen: Read comments, score intent, then partner
expected on screen: white ground · a NodeGraphCard panel over a dimmed Signal Field · Mensor leads · pipeline · kinetic-build · icon·wireframe logo · caption bottom-left
spec (the prompt): comp_id NodeGraphCardvisual pipelineshape dodecaground whitetreatment wireframemotion kinetic-buildpower throwinstrument laser-trace→Read comments, score intent, then partnercurate hub, nodes, stages, steps
9s9
matches intent
shared field
signature-3d		resolve40.4–45.6scoalescenceicon·color♪ bed_out
The Big T-M picks creators by buying intent in the comments, not reach.
on-screen: The Big T-M picks by intent
expected on screen: white ground · dodeca hero in the shared Signal Field · Mensor leads · coalescence · coalescence · icon·color logo · caption bottom-left
spec (the prompt): comp_id shared Signal Fieldvisual coalescenceshape dodecaground whitetreatment colormotion coalescencepower coalescenceinstrument coalescence→The Big T-M picks by intent

Cross-links this post in the day's output set

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