Back Office · office.temerarii.xyz
One asset, all the way in — composition, the wireframe + storyboard, the output format stack, and the template, all read from the SAME content-index record. The expected output matches what /media surfaces for this post.
post topic-sr-partnershipskind topicweek —date —campaign strategic-relations-pillarpillar strategic_relationsbeat —asset videoduration 90.1sground blackscenes 14

Checklist the per-video bar — engine/sim

96.1/100
plain languagevo coverageno dead airuniquenesscaption fitcompletenesscleanliness
quantitative quality · weights learn from your reviews (engine.sim.memory review topic-sr-partnerships 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 14 scenes · 90.1s · comp_id + rendered still + tier + the script

#Layer (comp_id · still · tier)BeatTimecodeMotionLogoAudioVO / on-screen / caption
1s1
matches intent
shared field
signature-3d		open0–5.8sspatial-parallaxicon·white-knockout♪ —
The right partner opens doors that ad spend never will, so finding the fit is the whole game.
on-screen: The right partner changes everything
expected on screen: black ground · tetra hero in the shared Signal Field · Nexus leads · node-graph · spatial-parallax · icon·white-knockout logo · caption bottom-left
spec (the prompt): comp_id shared Signal Fieldvisual node-graphshape tetraground blacktreatment white-knockoutmotion spatial-parallaxpower summoninstrument summon→three-mark
2s2
matches intent
ListCard
template		legacy5.8–11.399999999999999scrossfade-8ficon·wireframe♪ —
The old way chased whoever you happened to know, then hoped the audiences somehow lined up.
on-screen: Old way: chase whoever you already know
expected on screen: black ground · a ListCard panel over a dimmed Signal Field · Nexus leads · crossfade-8f · icon·wireframe logo · caption bottom-left
spec (the prompt): comp_id ListCardshape tetraground blacktreatment wireframemotion crossfade-8fpower morphinstrument decay→three-symboliccurate items
3s3
matches intent
NumberedList
template		teach11.4–18.5skinetic-buildicon·wireframe♪ —
Step one, we define fit on paper first, the audience overlap, the values, the non-competing offer.
on-screen: Step 1
expected on screen: black ground · a NumberedList panel over a dimmed Signal Field · Nexus leads · node-graph · kinetic-build · icon·wireframe logo · caption bottom-left
spec (the prompt): comp_id NumberedListvisual node-graphshape tetraground blacktreatment wireframemotion kinetic-buildpower morphinstrument morph+laser→html-in-canvas-panelcurate items, nodes
4s4
matches intent
ChecklistCard
template		teach18.5–25.7skinetic-buildicon·wireframe♪ —
Step two, we scan the market and let the model surface companies that match that definition, not just famous names.
on-screen: Step 2: scan the market for matches
expected on screen: black ground · a ChecklistCard panel over a dimmed Signal Field · Nexus leads · node-graph · kinetic-build · icon·wireframe logo · caption bottom-left
spec (the prompt): comp_id ChecklistCardvisual node-graphshape tetraground blacktreatment wireframemotion kinetic-buildpower morphinstrument morph+laser→three-diagramcurate items, nodes
5s5
matches intent
FlowSchematic
template		teach25.7–32.3skinetic-buildicon·wireframe♪ —
Step three, we check the real audience overlap, because two big brands with no shared audience help nobody.
on-screen: Step 3: check the audience overlap
expected on screen: black ground · a FlowSchematic panel over a dimmed Signal Field · Nexus leads · node-graph · kinetic-build · icon·wireframe logo · caption bottom-left
spec (the prompt): comp_id FlowSchematicvisual node-graphshape tetraground blacktreatment wireframemotion kinetic-buildpower morphinstrument morph+laser→html-in-canvas-codecurate nodes, stages
6s6
matches intent
BuildLog
template		teach32.3–39.0skinetic-buildicon·wireframe♪ —
Step four, the model scores each candidate on fit and effort, so you chase the few that are truly worth it.
on-screen: Step 4: score each match honestly
expected on screen: black ground · a BuildLog panel over a dimmed Signal Field · Nexus leads · node-graph · kinetic-build · icon·wireframe logo · caption bottom-left
spec (the prompt): comp_id BuildLogvisual node-graphshape tetraground blacktreatment wireframemotion kinetic-buildpower morphinstrument morph+laser→three-flowcurate lines, nodes
7s7
matches intent
WireframeMock
template		teach39.0–45.4skinetic-buildicon·wireframe♪ —
Step five, one prompt tailors the opening note to each partner's own goals, not a copy-paste ask.
on-screen: Step 5: draft the outreach per partner
expected on screen: black ground · a WireframeMock panel over a dimmed Signal Field · Nexus leads · node-graph · kinetic-build · icon·wireframe logo · caption bottom-left
spec (the prompt): comp_id WireframeMockvisual node-graphshape tetraground blacktreatment wireframemotion kinetic-buildpower morphinstrument morph+laser→html-in-canvas-panelcurate nodes
8s8
matches intent
DiffCard
template		teach45.4–52.699999999999996skinetic-buildicon·wireframe♪ —
Step six, every conversation lives in structured data, so the partnership pipeline is visible, not in your head.
on-screen: Step 6: track the pipeline as data
expected on screen: black ground · a DiffCard panel over a dimmed Signal Field · Nexus leads · node-graph · kinetic-build · icon·wireframe logo · caption bottom-left
spec (the prompt): comp_id DiffCardvisual node-graphshape tetraground blacktreatment wireframemotion kinetic-buildpower morphinstrument morph+laser→three-diagramcurate lines, nodes
9s9
matches intent
LogStream
template		teach52.7–59.0skinetic-buildicon·wireframe♪ —
Step seven, the model finds and ranks, but a person builds the trust, because partnerships are still human.
on-screen: Step 7: a human builds the relationship
expected on screen: black ground · a LogStream panel over a dimmed Signal Field · Nexus leads · node-graph · kinetic-build · icon·wireframe logo · caption bottom-left
spec (the prompt): comp_id LogStreamvisual node-graphshape tetraground blacktreatment wireframemotion kinetic-buildpower morphinstrument morph+laser→html-in-canvas-codecurate nodes, rows
10s10
matches intent
JsonDiff
template		teach59.0–65.7skinetic-buildicon·wireframe♪ —
Step eight, we tie each partnership to a tracked result, so we know which ones to renew and which to drop.
on-screen: Step 8: measure what each deal returns
expected on screen: black ground · a JsonDiff panel over a dimmed Signal Field · Nexus leads · node-graph · kinetic-build · icon·wireframe logo · caption bottom-left
spec (the prompt): comp_id JsonDiffvisual node-graphshape tetraground blacktreatment wireframemotion kinetic-buildpower morphinstrument morph+laser→three-flowcurate fileName, lines, nodes
11s11
matches intent
PipelineMap
template		teach65.7–72.10000000000001skinetic-buildicon·wireframe♪ —
Step nine, the deals that worked sharpen the fit criteria, so the next search is smarter than the last.
on-screen: Step 9
expected on screen: black ground · a PipelineMap panel over a dimmed Signal Field · Nexus leads · node-graph · kinetic-build · icon·wireframe logo · caption bottom-left
spec (the prompt): comp_id PipelineMapvisual node-graphshape tetraground blacktreatment wireframemotion kinetic-buildpower morphinstrument morph+laser→html-in-canvas-panelcurate nodes, stages
12s12
matches intent
ComparisonTable
template		proof72.1–77.8sreceipts-counticon·wireframe♪ —
We show the shortlist and the fit scores in the open, so the picks are judged on logic, not
on-screen: Receipts: a vetted shortlist, openly
expected on screen: black ground · a ComparisonTable panel over a dimmed Signal Field · Nexus leads · receipts · receipts-count · icon·wireframe logo · caption bottom-left
spec (the prompt): comp_id ComparisonTablevisual receiptsshape tetraground blacktreatment wireframemotion receipts-countpower receiptsinstrument spotlight→html-in-canvas-receiptscurate colA, colB, rows, statsLabels
13s13
matches intent
shared field
signature-3d		futurist77.8–83.39999999999999sspatial-parallaxicon·wireframe♪ —
Soon you state the goal and the system returns a ranked, vetted list of partners to call this week.
on-screen: Next: type your goal, get the partners
expected on screen: black ground · tetra hero in the shared Signal Field · Nexus leads · spatial-parallax · icon·wireframe logo · caption bottom-left
spec (the prompt): comp_id shared Signal Fieldshape tetraground blacktreatment wireframemotion spatial-parallaxpower spatial-parallaxinstrument laser-fire→three-forward
14s14
matches intent
shared field
signature-3d		resolve83.4–90.10000000000001scoalescenceicon·white-knockout♪ swell
This week, write your fit criteria and score three possible partners against it. Start the pipeline there.
on-screen: Score three potential partners by fit
expected on screen: black ground · tetra hero in the shared Signal Field · Nexus leads · coalescence · coalescence · icon·white-knockout logo · caption bottom-left
spec (the prompt): comp_id shared Signal Fieldvisual coalescenceshape tetraground blacktreatment white-knockoutmotion 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)

tiktokThe right partner opens doors ad spend never will, so finding the fit is the whole game. Here's the method. Define fit on paper first: audience overlap, shared values, a non-competing offer. Scan the market and let the model surface real matches, not just famous names. Check the actual audience overlap. Score each candidate on fit and effort. Tailor the outreach per partner. Track the pipeline as data. A human builds the trust. (AI-assisted) temerarii.xyz
instagramThe right partner opens doors ad spend never will. So finding the fit is the whole game. Define fit on paper first. Let the model surface real matches, not famous names. Check the actual audience overlap. Score each one on fit and effort. A human builds the trust. Partnerships are still human. #partnerships #bizdev #audiencefit #strategicrelations #temerarii
linkedinThe right partner opens doors that ad spend never will, so finding the fit is the whole game. The old way chased whoever you happened to know, then hoped the audiences lined up. The method: - Define fit on paper first: audience overlap, shared values, a non-competing offer - Scan the market and let the model surface companies that match that definition, not just famous names - Check the real audience overlap, because two big brands with no shared audience help nobody - Score each candidate on fit and effort, so you chase the few that are truly worth it - Tailor the opening note to each partner's own goals, not a copy-paste ask - Track every conversation as structured data, so the pipeline is visible, not in your head - Let the model find and rank, but a person builds the trust, because partnerships are still human - Tie each partnership to a tracked result, so you know which to renew and which to drop This week, write your fit criteria and score three possible partners against it. Start the pipeline there. temerarii.xyz
xThe right partner opens doors ad spend never will. Define fit on paper first, let the model surface real matches, check audience overlap, score each on fit and effort, then track the pipeline as data. A human builds the trust. temerarii.xyz
facebookThe right partner opens doors that ad spend never will, so finding the fit is the whole game. The old way chased whoever you already knew and hoped the audiences lined up. Here is the method. Define fit on paper first: audience overlap, shared values, a non-competing offer. Scan the market and let the model surface real matches, not just famous names. Check the actual audience overlap. Score each candidate on fit and effort. Tailor the outreach to each partner's goals. Track the whole pipeline as structured data. The model finds and ranks, but a person builds the trust. This week, write your fit criteria and score three possible partners against it. temerarii.xyz
threadsThe right partner opens doors ad spend never will, so finding the fit is the whole game. Define fit on paper first. Let the model surface real matches, not famous names. Check the actual audience overlap. Score each on fit and effort. Track the pipeline as data. A human builds the trust. Partnerships are still human.
pinterestFind brand partners by fit, not by who you already know: the method. Define fit on paper first with audience overlap, shared values, and a non-competing offer. Let a model scan the market and surface real matches, check the actual audience overlap, score each candidate on fit and effort, tailor outreach per partner, and track the pipeline as structured data. A copyable partnership scoring method. temerarii.xyz
blueskyThe right partner opens doors ad spend never will. Define fit on paper first, let the model surface real matches, check audience overlap, score each on fit and effort, track the pipeline as data. A human builds the trust. temerarii.xyz
youtubeFind Brand Partners by Fit, Not by Who You Know: The Method, Given Away The right partner opens doors that ad spend never will, so finding the fit is the whole game. The old way chased whoever you happened to know, then hoped the audiences lined up. Here is the method. The steps: - Define fit on paper first: audience overlap, shared values, a non-competing offer - Scan the market and let the model surface companies that match, not just famous names - Check the real audience overlap, because two big brands with no shared audience help nobody - Score each candidate on fit and effort, so you chase the few that are truly worth it - Tailor the opening note to each partner's own goals, not a copy-paste ask - Track every conversation as structured data, so the pipeline is visible - The model finds and ranks, but a person builds the trust, because partnerships are still human - Tie each partnership to a tracked result, so you know which to renew and which to drop What is next: you state the goal and the system returns a ranked, vetted list of partners to call this week. To start, write your fit criteria and score three possible partners against it. temerarii.xyz

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

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