Claude · Workflows

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.

QQuentin Megevand
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.

Claude AI Lab

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 →
✅What you need
1
Claude Code. Check with claude --version.
2
Node.js. Check with node --version: it gives you npm and npx.
3
A Higgsfield account with credits. Each analysis uses some, so check your balance before you run it.
4
Three hooks filmed for the same video. Same topic, same promise, only the opening changes.
3 s
the window Instagram's skip rate measures
15 s
the longest clip the predictor analyzes
24 h
before a Trial Reel shows its results

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.

🎯
Overall score
A score out of 100 for how well the clip holds attention.
⚡
Peak hook
The highest level of attention, and the second it happens.
📈
Hold
The share of attention kept across the whole clip.
🧠
Brain map
The regions that light up most, and the ones that drop off.
1

Install Higgsfield in Claude Code

🔗 higgsfield.ai/mcp
⚙️In your terminal
5
Install the CLI. npm i -g @higgsfield/cli
6
Sign in. higgsfield auth login, then finish signing in in the browser window that opens.
7
Add the skills. 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.
8
Check. higgsfield account status should answer with no error. If not, run higgsfield auth login again.
Without leaving Claude Code
You can also add the skills from inside Claude Code: /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.
2

Prepare three clips

🎬 your editing folder
✂️One variable: the hook
9
Keep the same body. Only the opening changes between the three versions. If the topic changes too, you are comparing two videos, not two hooks.
10
Cut to 15 seconds max. That is the predictor's limit, and those are the seconds that decide everything.
11
Export as mp4, mov or webm. Vertical or horizontal, both work.
My routine
I film three hooks for every reel and edit three versions. So the test costs no extra work: the three clips already exist.

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.
3

Run the analysis

💻 in Claude Code
▶️Three clips, one prompt
12
Open Claude Code in the clips folder. Type claude.
13
Paste the prompt below. Claude Code calls the 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
4

Read the report

📊 the report link
🔍How to decide
14
Only compare versions of the same video. The score ranks three openings, it does not compare Monday's reel with Thursday's.
15
Check where the peak hook lands. It should come in the first 3 seconds. A late peak means your opening makes people wait.
16
Find the second where the hold drops. Open the video at that moment: a shot that runs too long, text you cannot read, a setup sentence. That is where you cut.
17
Read the brain map. A very active "default mode" signals a wandering mind: the lower it is, the better.
The rule
Keep the top ranked hook, apply the three suggested changes, and run one more analysis on the fixed version before you publish.
5

Confirm for real

📱 Instagram, Trial Reels and insights
🧪The test on a real audience
18
Post the winning version as a Trial Reel. Instagram shows it to people who don't follow you, without putting it on your profile.
19
Wait 24 hours. Instagram shows you the views, likes, comments and shares, and how the trial performs against your previous ones. You then choose to share it with your followers or archive it.
20
Open the reel's insights. Two numbers answer the hook question: skip rate (the share of views that swiped away before the third second) and the retention chart (the flatter it is, the longer your audience stays).
The right order
The predictor filters before you publish, the Trial Reel decides on a real audience, the insights tell you why.
6

Calibrate the score on your account

📒 one file, ten reels
⚖️Prediction against result
21
Ask Claude Code to keep the log with the prompt below, for every new reel you test.
22
After about ten reels, compare the rankings. If the hooks the predictor ranks first also get the lowest skip rate, trust it. If not, use it to spot weak seconds and let Trial Reels decide.

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.

🛍️
Sales content
Three openings for the same offer, and the strongest one goes out first.
💸
Before you boost
The same test before you put ad budget behind a video.
🔁
Recurring content
A log that teaches you, reel after reel, which openings hold your audience.
Key takeaway
Three hooks, a 15 second test, a Trial Reel to confirm. The score shows you where attention drops, the real data tells you which hook wins.

Want to go further?

And day-to-day, I post one reel a day on Instagram: @quentin_iamarketing