Publicist — social-W26-Mon-4 — Temerarii Media Back Office

post social-W26-Mon-4kind threadweek W26date 2026-06-29campaign thread · Monpillar strategic_relationsbeat Monasset videoduration 72.2sground blackscenes 9

Checklist the per-video bar — engine/sim

93.2/100

plain languagevo coverageno dead airuniquenesscaption fitcompletenesscleanliness

quantitative quality · weights learn from your reviews (engine.sim.memory review social-W26-Mon-4 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 SignalFieldReel

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 9 scenes · 72.2s · 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	0–8.1s	spatial-parallax	icon·ember-fill	♪ bed_in	A publicist used to be a person, a phone, and a Rolodex of favors built over a decade of expensive lunches. on-screen: A publicist used to live in a
	expected on screen: black ground · tetra hero in the shared Signal Field · Nexus leads · node-graph · spatial-parallax · icon·ember-fill logo · caption bottom-left spec (the prompt): comp_id shared Signal Fieldvisual node-graphshape tetraground blacktreatment ember-fillmotion spatial-parallaxpower summoninstrument summon→A publicist used to live in a Rolodex
2	still pending matches intent shared field signature-3d	hook	8.1–16.5s	kinetic-build	icon·ember-fill	♪ node_lock	That works, but it does not scale, because there are only so many lunches and only one of you to eat them. on-screen: Relationships do not scale by lunch
	expected on screen: black ground · tetra hero in the shared Signal Field · Nexus leads · mark · kinetic-build · icon·ember-fill logo · caption bottom-left spec (the prompt): comp_id shared Signal Fieldvisual markshape tetraground blacktreatment ember-fillmotion kinetic-buildpower laser-lockinstrument laser-trace→Relationships do not scale by lunch
3	still pending matches intent NumberedList template	teach	16.5–25.3s	kinetic-build	icon·wireframe	♪ node_lock	Now we use AI to map the field first: who covers your topic, how often, and which angle each one keeps returning to. on-screen: Now: AI maps who covers what
	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→Now: AI maps who covers whatcurate items, nodes
4	still pending matches intent ComparisonTable template	proof	25.3–33.4s	receipts-count	icon·white-knockout	♪ node_lock	The method: score each reporter by how recently they touched your topic, then prioritize the warm ones over the famous ones. on-screen: Score reporters by recent fit
	expected on screen: black ground · a ComparisonTable panel over a dimmed Signal Field · Nexus leads · receipts · receipts-count · icon·white-knockout logo · caption bottom-left spec (the prompt): comp_id ComparisonTablevisual receiptsshape tetraground blacktreatment white-knockoutmotion receipts-countpower receiptsinstrument spotlight→Score reporters by recent fitcurate colA, colB, rows, statsLabels
5	still pending matches intent TerminalRun template	diff	33.4–40.0s	crossfade-8f	icon·liquid-chrome	♪ node_lock	Our line holds: automate the research, never the relationship, because a copy-paste pitch insults a real person. on-screen: We automate the research, not the relationship
	expected on screen: black ground · a TerminalRun panel over a dimmed Signal Field · Nexus leads · code · crossfade-8f · icon·liquid-chrome logo · caption bottom-left spec (the prompt): comp_id TerminalRunvisual codeshape tetraground blacktreatment liquid-chromemotion crossfade-8fpower morphinstrument morph→We automate the research, not the relationshipcurate codeLines
6	still pending matches intent ChecklistCard template	teach	40.0–48.8s	kinetic-build	icon·ember-fill	♪ node_lock	So let the model draft a starting point, then rewrite it in your own voice with one detail only a human would notice. on-screen: Draft, then rewrite in your own voice
	expected on screen: black ground · a ChecklistCard panel over a dimmed Signal Field · Nexus leads · node-graph · kinetic-build · icon·ember-fill logo · caption bottom-left spec (the prompt): comp_id ChecklistCardvisual node-graphshape tetraground blacktreatment ember-fillmotion kinetic-buildpower morphinstrument morph+laser→Draft, then rewrite in your own voicecurate items, nodes
7	still pending matches intent KpiGrid template	proof	48.8–57.199999999999996s	receipts-count	icon·wireframe	♪ node_lock	We handed a client a scored list of ten warm reporters, and that beat months of cold luck in a single week. on-screen: A warm list of ten beat real
	expected on screen: black ground · a KpiGrid panel over a dimmed Signal Field · Nexus leads · receipts · receipts-count · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id KpiGridvisual receiptsshape tetraground blacktreatment wireframemotion receipts-countpower receiptsinstrument spotlight→A warm list of ten beat real luckcurate kpis, statsLabels
8	still pending matches intent LegacyCard template	step	57.2–64.9s	kinetic-build	icon·white-knockout	♪ node_lock	Your step: let AI build and score the list, then write every message like a person who read the work. on-screen: Step: score, then write like a person
	expected on screen: black ground · a LegacyCard panel over a dimmed Signal Field · Nexus leads · pipeline · kinetic-build · icon·white-knockout logo · caption bottom-left spec (the prompt): comp_id LegacyCardvisual pipelineshape tetraground blacktreatment white-knockoutmotion kinetic-buildpower throwinstrument laser-trace→Step: score, then write like a personcurate stages, steps
9	still pending matches intent shared field signature-3d	resolve	64.9–72.2s	coalescence	icon·ember-fill	♪ bed_out	The future publicist researches at machine scale and speaks in a human voice, and The Big T-M wires both. on-screen: Futurist: machine research, human voice
	expected on screen: black ground · tetra hero in the shared Signal Field · Nexus leads · coalescence · coalescence · icon·ember-fill logo · caption bottom-left spec (the prompt): comp_id shared Signal Fieldvisual coalescenceshape tetraground blacktreatment ember-fillmotion coalescencepower coalescenceinstrument coalescence→Futurist: machine research, human voice

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 9 destinations

LinkedInX/TwitterYouTubeInstagramFacebookThreadsTikTokPinterestBluesky

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

tiktokA publicist's whole job used to be a Rolodex of lunch favors. AI-assisted upgrade: map who covers your topic, score reporters by how recently they touched it, prioritize warm over famous. Automate the research — NEVER the relationship. We handed a client 10 warm reporters in a week.

instagramAutomate the research. Never the relationship. Map who covers your topic. Score by recent fit. Write every message like a human who read the work. #publicist #pr #mediarelations #pitching #temerarii

linkedinThe classic publicist was a phone and a Rolodex built over a decade of expensive lunches. It works — but it doesn't scale, because there's only one of you. The method: use AI to map the field first — who covers your topic, how often, and which angle each writer keeps returning to. Score each reporter by how recently they touched your topic and prioritize the warm ones over the famous ones. Then automate the research, never the relationship: let the model draft a starting point, but rewrite every message in your own voice with one detail only a human would notice. We handed a client a scored list of ten warm reporters; that beat months of cold luck in a week. Takeaway: research at machine scale, speak in a human voice.

xA publicist's job used to be a Rolodex of lunch favors. Automate the research, never the relationship: map coverage, score reporters by recent fit, write like a human who read the work. temerarii.com

facebookA publicist used to be a phone and a Rolodex of favors built over years of expensive lunches. It works — but it doesn't scale. The upgrade: use AI to map who covers your topic and score reporters by how recently they touched it, then prioritize warm over famous. Automate the research, never the relationship — every message still gets rewritten in your own voice. We handed a client ten warm reporters in a week. Want a scored media list? Message us.

threadsA publicist's job used to be a Rolodex of lunch favors. Automate the research, never the relationship: map who covers your topic, score reporters by recent fit, prioritize warm over famous, then write every message like a human who read the work. We delivered 10 warm reporters in a week.

pinterestModern publicist workflow: how to map media coverage with AI, score reporters by recent topic fit, and prioritize warm contacts over famous ones — while keeping every pitch human. PR strategy, media list building, journalist outreach, publicist method, earned media.

blueskyA publicist's job used to be a Rolodex of lunch favors. Automate the research, never the relationship: map coverage, score reporters by recent fit, write like a human who read the work. temerarii.com

youtubeThe Modern Publicist: Automate Research, Never the Relationship The classic publicist was a phone and a Rolodex built over a decade of lunches. It works — but it doesn't scale. The method: use AI to map the field (who covers your topic, how often, which angle), score each reporter by how recently they touched it, and prioritize warm over famous. Then automate the research, never the relationship — let the model draft, but rewrite every message in your own voice with one detail only a human would notice. We handed a client ten warm reporters in a week. Research at machine scale, speak in a human voice — by The Big T-M.

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

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