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 longform-W45-Monkind longformweek W45date 2026-11-09campaign longform-youtubepillar strategic_relationsbeat asset videoduration 113.2sground redscenes 6

Checklist the per-video bar — engine/sim

100.0/100
plain languagevo coverageno dead airuniquenesscaption fitcompletenesscleanliness
quantitative quality · weights learn from your reviews (engine.sim.memory review longform-W45-Mon good|bad)
✓ all static checks pass — one-focal/scene · tier-by-beat · one-track caption · colorway · cast+shape correct · no banned/fabricated. (audio + visual tiers verify on the rendered finals — Phase 2)

Composition comp · template family · expected output

composition LongFormChaptersfamily / template LongFormChapters
9:16 Reelpending1:1 Squarepending16:9 Widepending9:16 4Kpending1:1 4Kpending16:9 4KpendingGIF (SMS)pending
render pending — silent master not yet on disk
expected output: 0/7 rendered — same matrix the /media preview surfaces for this asset.

Composition layer × scene 6 scenes · 113.2s · comp_id + rendered still + tier + the script

#Layer (comp_id · still · tier)BeatTimecodeMotionLogoAudioVO / on-screen / caption
1s1
matches intent
shared field
signature-3d
open0–17.3sspatial-parallaxicon·liquid-chrome♪ bed_in
The social engine starts with listening, not talking. Today is about social listening. By the end you will know how to make a machine read every room your buyers sit in and tell you, in their words, what they actually want. No surveys. No guessing. Just reading.
on-screen: Stop guessing what people want
expected on screen: red ground · tetra hero in the shared Signal Field · Nexus leads · node-graph · spatial-parallax · icon·liquid-chrome logo · caption bottom-left
spec (the prompt): comp_id shared Signal Fieldvisual node-graphshape tetraground redtreatment liquid-chromemotion spatial-parallaxpower summoninstrument summon→Stop guessing what people want
2s2
matches intent
NumberedList
template
teach17.3–37.5skinetic-buildicon·white-knockout♪ node_lock
First move. Wire a search tool into the terminal. We connect a Reddit reader and an X reader through MCP, small adapters that let the agent pull posts and comments directly. You give it the subreddits and the search terms where your buyers gather. It pulls hundreds of real threads in one pass, no clicking.
on-screen: Point a reader at the crowd
expected on screen: red ground · a NumberedList panel over a dimmed Signal Field · Nexus leads · node-graph · kinetic-build · icon·white-knockout logo · caption bottom-left
spec (the prompt): comp_id NumberedListvisual node-graphshape tetraground redtreatment white-knockoutmotion kinetic-buildpower morphinstrument morph+laser→Point a reader at the crowdcurate items, nodes
3s3
matches intent
ChecklistCard
template
teach37.5–57.3skinetic-buildicon·liquid-chrome♪ node_lock
Second move. Hand all that raw text to the model and ask one thing. What do these people complain about over and over. It groups the noise into a short list of real pains, ranked by how often they show up. You stop arguing about what matters. The crowd already voted in plain writing.
on-screen: Let the model find the pattern
expected on screen: red ground · a ChecklistCard panel over a dimmed Signal Field · Nexus leads · node-graph · kinetic-build · icon·liquid-chrome logo · caption bottom-left
spec (the prompt): comp_id ChecklistCardvisual node-graphshape tetraground redtreatment liquid-chromemotion kinetic-buildpower morphinstrument morph+laser→Let the model find the patterncurate items, nodes
4s4
matches intent
FlowSchematic
template
teach57.3–76.4skinetic-buildicon·white-knockout♪ node_lock
Third move, the one most people skip. Ask the model to pull the exact phrases buyers use, not your industry jargon. If they say my posts get no reach, you write my posts get no reach. You feed those phrases straight into next week's posts. The crowd wrote your hook for you.
on-screen: Steal their exact words
expected on screen: red ground · a FlowSchematic panel over a dimmed Signal Field · Nexus leads · node-graph · kinetic-build · icon·white-knockout logo · caption bottom-left
spec (the prompt): comp_id FlowSchematicvisual node-graphshape tetraground redtreatment white-knockoutmotion kinetic-buildpower morphinstrument morph+laser→Steal their exact wordscurate nodes, stages
5s5
matches intent
KpiGrid
template
proof76.4–95.5sreceipts-counticon·liquid-chrome♪ node_lock
Proof, no numbers needed. The angle of this very video came from listening. We read the threads where small business owners vent about social, saw the same fear again and again, and built the post around it. We did not invent a topic. We caught one that was already in the air.
on-screen: We listened before this video
expected on screen: red ground · a KpiGrid panel over a dimmed Signal Field · Nexus leads · receipts · receipts-count · icon·liquid-chrome logo · caption bottom-left
spec (the prompt): comp_id KpiGridvisual receiptsshape tetraground redtreatment liquid-chromemotion receipts-countpower receiptsinstrument spotlight→We listened before this videocurate kpis, statsLabels
6s6
matches intent
shared field
signature-3d
resolve95.5–113.2scoalescenceicon·liquid-chrome♪ bed_out
That is social listening. Wire a reader, find the pattern, steal the words, write back. A machine does the reading so you do the thinking. See the whole engine working in public at office dot temerarii dot xyz, then point a reader at your own crowd this week.
on-screen: Watch the listening run
expected on screen: red ground · tetra hero in the shared Signal Field · Nexus leads · coalescence · coalescence · icon·liquid-chrome logo · caption bottom-left
spec (the prompt): comp_id shared Signal Fieldvisual coalescenceshape tetraground redtreatment liquid-chromemotion coalescencepower coalescenceinstrument coalescence→Watch the listening run

Format stack 1 aspects · same scenes[], re-cropped

16:9
1920×1080
X/Twitter · YouTube · LinkedIn video

Channels 2 destinations

YouTubeBlog

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

youtubeSocial Listening: Let a Machine Read the Room and Tell You What Buyers Want The engine starts with listening, not talking. In this video we show how to make a machine read every room your buyers sit in and tell you, in their words, what they actually want. No surveys, no guessing, just reading. The moves: - Point a reader at the crowd. Wire a search tool into the terminal. We connect a Reddit reader and an X reader through MCP, small adapters that let the agent pull posts and comments directly. Give it the subreddits and search terms where your buyers gather, and it pulls hundreds of real threads in one pass. - Let the model find the pattern. Hand all that raw text to the model and ask one thing: what do these people complain about over and over. It groups the noise into a short list of real pains, ranked by how often they show up. - Steal their exact words. Ask the model to pull the exact phrases buyers use, not your industry jargon. If they say my posts get no reach, you write my posts get no reach. The crowd wrote your hook for you. The honest proof: the angle of this very video came from listening. We read the threads where small business owners vent about social, saw the same fear again and again, and built the post around it. We did not invent a topic, we caught one already in the air. The takeaway: wire a reader, find the pattern, steal the words, write back. A machine does the reading so you do the thinking. See the whole engine working at office.temerarii.xyz. Keywords: social listening, audience research, Reddit, MCP, AI agent, content ideas.

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