Turn any reel into script & visuals

Paste an Instagram reel to deconstruct it frame‑by‑frame — then scrub the footage, rewrite the script, and generate a ready‑to‑paste Claude design prompt.

▶

{{ CC.frameCount }} frames

9:16

VISUAL · 45%

CREATOR · 55%

Stufe {{ CC.curFrame.intensity }}

FRAME {{ CC.counter }}

🎞 {{ CC.curFrame.motion }}

GESPROCHEN

ON-SCREEN TEXT

UP NEXT →

0s {{ CC.curTime }} {{ CC.totalTime }}

VISUAL-RHYTHMUS · geschätzt

FILMSTRIP

{{ CC.selCount }} selected · click to toggle, then Download

ORIGINAL SCRIPT · SPOKEN

{{ CC.transcript }}

NEW SCRIPT · AI

generated first, then rewritten by your skills — the original stays intact

{{ CC.gscript }}

Skills · {{ SK.activeCount }} active

Reusable instructions applied when you generate a script (here & in Studio).

Applies to:

No skills yet — add one to steer how your scripts are written.

PER-FRAME VISUALS

Scene‑by‑scene descriptions from the footage. These feed the prompt on the right.

✳

Claude Design Prompt

SCRIPT-QUELLE

{{ CC.assembledPrompt }}

Frames · {{ CC.totalTime }}

Talking head

UI / B-Roll

Ø Visual-Intensität

Visual-Intensität über die Zeit

Geschätzte Stufe 0–4 pro Szene (aus Bewegung + On-Screen-Text). Klick öffnet den Frame.

0–1 Ruhe

2 Support

3 Hero

4 Scroll-Stopper

Hook

Pacing

Höhepunkt

SUGGESTIONS

Animations‑Breakdown · Reverse‑Engineering

Bricht das extrahierte Reel Szene für Szene auf: die echten Frames, was animiert wird, und eine Spec, um jeden Frame sauber als Mockup nachzubauen.

Erst ein Reel im Frames‑Tab extrahieren — dann brichst du hier dessen Animationen Frame für Frame auf.

„{{ sec.narration }}"

ANIMATION BRIEF

ANIMATIONS-FLOW · {{ sec.frameCount }} FRAMES

{{ fr.n }}. {{ fr.caption }}

→

Hit Break down animations — the reel is split into scenes with its real frames + an animation brief each, plus a one‑click clean mockup.

Discovery · {{ DISC.headCount }}

🔥 Videos to copy, ranked for YOUR content

Every watched reel scored against the content you actually make (from your own transcripts) × how hard it outperforms its creator × freshness. Off-niche and stale reels sink automatically — no filtering needed.

Loading videos…

No videos yet — hit + Watch on a few creators below; their reels appear here ranked by outlier.

Content

Extract

@{{ v.creator }} · Open ↗

✨ Creators for your niche

Auto-found from the creators you already rate — no hashtags, no manual hunting. Ranked by how many of your top performers point to them. {{ REC.seedsText }}

Finding creators in your niche…

{{ r.at }} ✓

BY HASHTAG · GRAPH API

OR PASTE LINKS · NO TOKEN

CATEGORY

Supabase isn't configured — add SUPABASE_URL + SUPABASE_SERVICE_ROLE_KEY to .env to use the discovery library.

Loading…

Nothing in the library yet — discover by hashtag or paste a few reel links above.

▶ {{ d.views }}♥ {{ d.likes }}💬 {{ d.comments }}

⚡ {{ d.outlierTxt }}

Open ↗

WATCHLIST · {{ TR.creatorCount }} CREATORS · {{ TR.sweepTxt }}

Search & pick a creator to add, preview their reels, or fan out to similar creators — or

✨ {{ TR.related.title }} · one-click add to your watchlist

{{ r.at }} ✓

▶ {{ r.views }}

Open ↗

🎵 Trending sounds · a sound ≥2 of your creators are outperforming with

{{ f.label }} · {{ f.count }}

{{ f.medOutlier }}×

median outlier

Sort

Outlier

Length

Posted

Supabase isn't configured — add SUPABASE_URL + SUPABASE_SERVICE_ROLE_KEY to .env.

Add HIKERAPI_KEY on the server to look up and sweep creators.

Loading…

No swept reels yet — add a creator above, or hit Sweep now. Nothing matches these filters otherwise.

Content

Len

Extract

@{{ it.creator }} · Open ↗

Studio

Give a topic. The advisor learns the patterns that actually drove engagement on your reels, writes a script grounded in your winners, and you refine it in the chat. It cites the real reels it learned from.

REEL BRIEF

OPTIMIZE FOR

Skills · {{ SK.activeCount }} active

Your reusable instructions — applied to every script generated here & in Create.

Applies to:

No skills yet — add one to steer how your scripts are written.

◆

Your winning formula

›{{ f }}

LEARNED FROM

{{ ref.shortcode }} {{ ref.likes }}♥ · {{ ref.comments }}💬

◆

Enter a topic on the left and hit Generate.
The advisor pulls the patterns from your reels and writes a grounded script — then refine it here in chat.

Thinking…

GENERATED SCRIPT

{{ STU.script }}

HOOK VARIANTS

VISUAL-INTENSITY PLAN

Refinements are remembered — future scripts follow them automatically.

Lab

One pipeline, measured end to end: 1 what's on screen each second and whether it holds viewers → 2 the exact moments they leave → 3 what your opening lines do → 4 whether held attention converts to views. Trained on your frames + your exact retention, cross‑validated by reel. No LLM opinions.

{{ LAB.attSeconds }} per‑second observations · {{ LAB.attReels }} reels · group cross‑validation (no reel in both train & test)

Training the attention model on your per‑second data…

1 · ATTENTION MODEL gradient‑boosted trees · per‑second drop rate · cross‑validated by reel

MODEL ACCURACY

out‑of‑sample R² on per‑second loss

TIME‑DECAY ALONE

knowing only the timestamp · content adds {{ LAB.attLift }}

AVG LOSS RATE

{{ LAB.attHazard }}%/s

of remaining viewers, each second

WHAT HOLDS vs LOSES VIEWERS · green holds · red bleeds · per‑second effect, all else equal · levers the model barely uses (<0.5% weight) are hidden as noise

BLEEDING MORE THAN CONTENT PREDICTS · something unmeasured is hurting

HOLDING BETTER THAN PREDICTED · rewatch to find the hidden edge

Per‑second effects compound: at your average loss rate, 30 seconds keeps ~18% of viewers — hold one green edge of −0.7pp/s over that stretch and ~22% survive, a quarter more audience. The two lists flag reels where something the model can't see (delivery, topic, audio) is helping or hurting — those are worth rewatching.

2 · DEATH MOMENTS where viewers actually leave · exact per‑second retention × what's on screen

The 0–3s scroll‑off is universal. What's actionable is the second wave — the biggest drop after the hook — and which visual CUTS cost more retention than the reel's own natural decline. Every reel's own death moments are on its detail page.

Finding your death moments…

AVG HOOK SURVIVAL @3s

{{ LAB.dropHook }}%

still watching after the scroll‑off

BIGGEST POST‑HOOK CLIFF

where most reels take their next hit

WHICH CUTS COST RETENTION · n = how often the cut appears · −pts = extra retention lost vs the reel's own trend

⏳ Scene‑cut costs appear once the structured visual backfill finishes.

STEEPEST POST‑HOOK CLIFFS · click to open the reel

WHAT MAKES THEM SWIPE · skip‑rate model · LOO R² {{ LAB.skipR2 }} · n {{ LAB.skipN }} · effect in skip‑rate points · green = fewer skips

3 · HOOKS your opening lines, measured · what is SAID in the first 3.5s · {{ LAB.hookN }} reels

Deterministic patterns (regex on the spoken opener — no AI opinions) contrasted on the two things a hook controls: the swipe and the 3‑second hold. Descriptive averages at small n — where they agree with the models above, trust them.

TOPICS · keyword‑detected, descriptive only — topic hurt the predictor when ablated (thin cells) · ⚠ = under 5 reels, read as anecdote

TOPICNMED VIEWSSKIPHOLD @3s

OPENERS THAT HELD · lowest skip · click to open

OPENERS THAT GOT SWIPED · highest skip · click to open

4 · VIEW PREDICTOR content → retention → engagement, stacked · leave‑one‑out CV · maturity‑controlled

CONTENT ALONE

what you control up front

+ RETENTION

+ hook hold, completion, skip

+ ENGAGEMENT

+ saves/shares/comments per view

VIEW GROWTH

WHAT MOVES VIEWS · standardized effect, each controlled for the rest

R² is out‑of‑sample (leave‑one‑out): 0 = no better than guessing your average, 1 = perfect. Content covers the deterministic script + format + posting features (regex over your transcripts, counted frames, the timestamp). The engagement tier only exists after posting and its rates use views as denominator — read those coefficients as quality signals, not levers you can pull in advance. Too‑fresh reels are excluded so unsettled view counts don't skew it.

WHAT DRIVES LIKES · like‑rate model · LOO R² {{ LAB.likesR2 }} · n {{ LAB.likesN }} · avg {{ LAB.likesMean }}% · effect in like‑rate points · recent reels included

Regresses the REAL per‑view like rate on your pre‑posting content features — what makes people tap ♥, independent of how far the reel reached.

WHAT DRIVES COMMENTS · comment‑rate model · LOO R² {{ LAB.commentsR2 }} · n {{ LAB.commentsN }} · avg {{ LAB.commentsMean }}% · effect in comment‑rate points · recent reels included

Same, for the per‑view comment rate — what actually makes people reply.

Everything above is measured and honestly cross‑validated — strong enough to rank what to try next, not to prove cause. The proof is changing ONE lever in a new reel (e.g. cut the stale scene at its predicted death moment) and watching its curve beat the model's prediction.

Performance overview

Daily reach & plays

Engagement mix

Avg engagement rates

What's working

Needs attention

All reels · {{ reelCount }}

{{ R.cap }}

Audience retention

Engagement breakdown

Engagement rates

Reel audit

AI breakdown

Turn any reel into script & visuals

Reels Pipeline

Discovery · {{ DISC.headCount }}

Guides

Studio

Lab

Clippers