Checklist the per-video bar — engine/sim
100.0/100
quantitative quality · weights learn from your reviews (
engine.sim.memory review longform-W31-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
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 7 scenes · 120.5s · comp_id + rendered still + tier + the script
| # | Layer (comp_id · still · tier) | Beat | Timecode | Motion | Logo | Audio | VO / on-screen / caption |
|---|---|---|---|---|---|---|---|
| 1 |  matches intent shared field signature-3d | open | 0–20.9s | spatial-parallax | icon·wireframe | ♪ bed_in | Cut busywork, keep revenue. Today that means SEO. Most SEO work is slow, manual, and easy to fake. We are going to show you how to use your own search data to fix pages that already almost rank, instead of writing fresh content into the void. You will learn the one move that turns guessing into editing. on-screen: Rank without the busywork |
expected on screen: white ground · octa hero in the shared Signal Field · Lumen leads · node-graph · spatial-parallax · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id shared Signal Fieldvisual node-graphshape octaground whitetreatment wireframemotion spatial-parallaxpower summoninstrument summon→Rank without the busywork | |||||||
| 2 |  matches intent NumberedList template | teach | 20.9–40.0s | kinetic-build | icon·wireframe | ♪ node_lock | Step one. Open Google Search Console and verify your site. This is the only place that shows the exact words people typed before they found you. Most teams never look. Connect it to Claude Code through a Search Console MCP server so the model can read those queries directly, no manual export. on-screen: Pull your real search queries |
expected on screen: white ground · a NumberedList panel over a dimmed Signal Field · Lumen leads · node-graph · kinetic-build · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id NumberedListvisual node-graphshape octaground whitetreatment wireframemotion kinetic-buildpower morphinstrument morph+laser→Pull your real search queriescurate items, nodes | |||||||
| 3 |  matches intent ChecklistCard template | teach | 40.0–58.8s | kinetic-build | icon·wireframe | ♪ node_lock | Step two. Ask the model for queries where you rank in positions eleven through twenty. Those are pages sitting on the second results page, one nudge from the first. They already work a little. Fixing them is cheap. Writing a brand new page to compete is the busywork you can skip. on-screen: Find the page on page two |
expected on screen: white ground · a ChecklistCard panel over a dimmed Signal Field · Lumen leads · node-graph · kinetic-build · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id ChecklistCardvisual node-graphshape octaground whitetreatment wireframemotion kinetic-buildpower morphinstrument morph+laser→Find the page on page twocurate items, nodes | |||||||
| 4 |  matches intent StepFlow template | teach | 58.8–76.8s | kinetic-build | icon·wireframe | ♪ node_lock | Step three. Hand the model the real queries for one of those pages and tell it to rewrite the page to answer them plainly. Not stuffed with keywords, answered. The model is editing toward words real people already used, so the page matches intent instead of chasing a hunch. on-screen: Let the model rewrite from the data |
expected on screen: white ground · a StepFlow panel over a dimmed Signal Field · Lumen leads · node-graph · kinetic-build · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id StepFlowvisual node-graphshape octaground whitetreatment wireframemotion kinetic-buildpower morphinstrument morph+laser→Let the model rewrite from the datacurate nodes, steps | |||||||
| 5 |  matches intent LegacyCard template | teach | 76.8–96.6s | kinetic-build | icon·wireframe | ♪ node_lock | We are not selling a trick we do not use. Our own office pages get tuned with this exact loop, the model reading our Search Console data and rewriting from it. We do not pay for traffic we can earn by fixing what we already published. That is the whole point of keeping revenue. on-screen: We tune our own pages this way |
expected on screen: white ground · a LegacyCard panel over a dimmed Signal Field · Lumen leads · node-graph · kinetic-build · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id LegacyCardvisual node-graphshape octaground whitetreatment wireframemotion kinetic-buildpower morphinstrument morph+laser→We tune our own pages this waycurate nodes | |||||||
| 6 |  matches intent BuildLog template | proof | 96.6–114.6s | receipts-count | icon·wireframe | ♪ node_lock | So the takeaway is small and real. SEO done right is mostly editing pages you forgot, using the words searchers gave you for free. No mystery, just your own data fed back in. Go watch the studio run this loop in the open at office dot temerarii dot xyz. on-screen: Watch it work in public |
expected on screen: white ground · a BuildLog panel over a dimmed Signal Field · Lumen leads · receipts · receipts-count · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id BuildLogvisual receiptsshape octaground whitetreatment wireframemotion receipts-countpower receiptsinstrument spotlight→Watch it work in publiccurate lines, statsLabels | |||||||
| 7 |  matches intent shared field signature-3d | resolve | 114.6–120.5s | coalescence | icon·wireframe | ♪ bed_out | Creative workflows: the give-it-away method, start to finish, with the prompts and the steps shown. on-screen: Chapter 6 |
expected on screen: white ground · octa hero in the shared Signal Field · Lumen leads · coalescence · coalescence · icon·wireframe logo · caption bottom-left spec (the prompt): comp_id shared Signal Fieldvisual coalescenceshape octaground whitetreatment wireframemotion coalescencepower coalescenceinstrument coalescence→Chapter 6 | |||||||
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)
youtubeSEO Without the Busywork: Fix Page-Two Pages Using Your Own Search Console Data
Most SEO work is slow, manual, and easy to fake. This walkthrough shows how to use your own search data to fix pages that already almost rank, instead of writing fresh content into the void.
The method:
- Pull your real search queries. Open Google Search Console and verify your site. This is the only place that shows the exact words people typed before they found you, and most teams never look. Connect it to a coding agent through a Search Console MCP server so the model can read the data itself.
- Find the page on page two. Ask the model for queries where you rank in positions eleven through twenty. Those are pages sitting on the second results page, one nudge from the first. They already work a little, and fixing them is cheaper than writing a brand new page from scratch.
- Let the model rewrite from the data. Hand it the real queries for one of those pages and tell it to rewrite the page to answer them plainly, not stuffed with keywords, answered. The model is editing toward words real people already used, so the page matches the search.
Our own office pages get tuned with this exact loop, the model reading our Search Console data and rewriting from it. We do not pay for traffic we can earn by fixing what we already published.
SEO done right is mostly editing pages you forgot, using the words searchers gave you for free. No mystery, just your own data fed back in. Watch the studio run this loop in the open at office.temerarii.xyz.
Keywords: SEO, Google Search Console, MCP server, on-page optimization, search queries, content editing.