Predict a reel's performance before you post it, with Claude Code
Test three hooks of the same video before you publish: Higgsfield's Virality Predictor, plugged into Claude Code, estimates attention second by second. Then you confirm for real with Trial Reels and skip rate.
October 22, 2026 · 7 min read
A reel is won or lost in three seconds. Instagram measures exactly that: the share of people who swipe away from your video before the third second. The problem is that you find out after posting, when the shoot and the edit are already done.
This guide flips the order. You test your hooks before you publish, with a model that estimates an audience's attention second by second, right from Claude Code. Then you confirm with Instagram's real data.
The Claude AI Lab is my Skool community where I share my Claude systems and the more advanced modules. Access is $67/month.
Join the Lab →claude --version.node --version: it gives you npm and npx.What the predictor measures
Higgsfield's Virality Predictor does not guess a view count. It has a modeled audience watch your clip and maps its brain response: vision, sound, memory, attention, language. You get an estimate of the attention your video holds, second by second.
That is the signal you need to pick between three hooks: comparative, fast, and available before your audience ever sees the video.
Install Higgsfield in Claude Code
npm i -g @higgsfield/clihiggsfield auth login, then finish signing in in the browser window that opens.npx skills add higgsfield-ai/skills. The command detects Claude Code and installs Higgsfield's eight skills, including higgsfield-generate, the one that runs the predictor.higgsfield account status should answer with no error. If not, run higgsfield auth login again./plugin marketplace add higgsfield-ai/skills, then /plugin install higgsfield@higgsfield. You still need the CLI and the sign-in from steps 5 and 6.Prepare three clips
Don't want to cut by hand? Ask Claude Code, it uses FFmpeg (installed on your machine):
Cut the first 15 seconds of version-a.mp4, version-b.mp4 and version-c.mp4 with ffmpeg, and save them as hook-a.mp4, hook-b.mp4 and hook-c.mp4.
Run the analysis
claude.higgsfield-generate skill, sends each clip to the predictor and waits for the results.Analyze these three clips with Higgsfield's Virality Predictor: hook-a.mp4, hook-b.mp4, hook-c.mp4.
For each one, give me the overall score, the peak hook and its second, the hold, and the weakest region.
Rank the three hooks from strongest to weakest.
For the best one, tell me what happens on screen and in the text at the second where attention drops, then suggest three specific changes for its first 3 seconds.
Present the scores as attention estimates, never as a view count.
Give me the report link for each clip.
Under the hood, each analysis is this command, which you can also run yourself:
higgsfield generate create brain_activity --video ./hook-a.mp4 --wait
Read the report
Confirm for real
Calibrate the score on your account
An attention model is still an estimate. The log tells you how far to trust it on your own audience:
Create a predictions.csv file with these columns: date, reel, hook, overall score, peak hook (second), hold, real skip rate, views at 24 h.
Add one row for each clip you just analyzed, with its Higgsfield scores.
When I give you a reel's Instagram insights, fill in its last two columns.
After 10 reels, tell me whether the predictor's ranking matches the real ranking by skip rate.
What this changes for your business
Every reel costs a shoot, an edit and a slot in your calendar. Testing the hook before you publish means you stop paying those hours for a video people swipe past in two seconds.
Want to go further?
In the Lab, I share my end-to-end Claude workflows, the ones that save me hours every week.
A dedicated session or program, tailored to your tools and use cases.
And day-to-day, I post one reel a day on Instagram: @quentin_iamarketing