How do I install the Reverse Motion System skill?

Run npx skillmds@latest add timkoda/reverse-motion-system in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Reverse Motion System safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Reverse Motion System?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Reverse Motion System free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Reverse Motion System?

timkoda (@timkoda) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Reverse Motion System by timkoda — Agent Skill

Name: Reverse Motion System
Author: timkoda

Reverse Motion System

Reverse-engineer any motion design video from pixels alone, then recreate it — or your own original piece — through the Higgsfield MCP. Measures beat structure, camera moves, shapes and palette with computer vision, writes a spec.json contract, converts it into a structured JSON video prompt (Google Omni style), and ships the render via Higgsfield. Trigger on - reverse engineer this video, recreate this motion design, measure this reference, clone this animation, build my custom animation from my references.

timkoda 5d421c5 3 files · 7.3 KB Updated Sep 17, 2026

File contents

The Reverse Motion System

Recreate pixel-perfect motion design from any reference video — by MEASURING it, not eyeballing it. Then flip the same system to render something entirely your own.

Every camera move, shape, colour and cut in the final prompt is a calculated value extracted with computer vision. You are never prompting blind.

Requirements

Python 3.10+ with opencv-python-headless and numpy (pip install opencv-python-headless numpy — use a venv if your system blocks pip)
The Higgsfield MCP connected to Claude Code (claude mcp add --transport http --scope user higgsfield https://mcp.higgsfield.ai/mcp)

The pipeline (run it in order)

Stage 1 — Measure the reference

python scripts/measure.py reference.mp4 -o spec.json

Computer vision reads every frame and pulls out:

Structure: cuts scored as frame-diff ratio against the local median, fitted black-window and white-flash detectors → the beat list
Camera: Farneback optical flow on lit pixels, solved per frame pair for a similarity transform (scale / rotation / translation) with physical admissibility guards → push-in, pull-back, pan, roll, per-frame values
Elements: connected-component blobs at each beat's midframe → shape class, normalized position and size
Palette: k-means over lit pixels per beat → locked hex values
Look: background floor, grain, bloom notes

spec.json is the contract. Later stages read it and never re-derive a number from pixels.

Stage 2 — Convert the spec into a structured prompt

python scripts/spec_to_prompt.py spec.json -o prompt.json --style "neon motion graphics on black"

Builds a shot-by-shot JSON video prompt: one shot per scene beat, transitions carried from the measured black/flash windows, camera phrased from the measured motion, palette locked to the measured hexes.

Stage 3 — Render through the Higgsfield MCP

Inside Claude Code, with the Higgsfield MCP connected, ask:

"Read prompt.json and generate this video on Higgsfield. Use the Google Omni model if it is available in the marketplace, otherwise Seedance 2.0. 9:16, 1080p, honor every shot duration, camera move and palette exactly."

The agent lists the available Higgsfield models, picks the conversational JSON-prompt model, fires the generation, polls the job, and returns the video URL.

Stage 4 — Your own sauce (the point of the system)

Copying proves the control. Now feed the system YOUR direction instead of a reference:

Edit spec.json directly — change palettes, swap camera moves, retime beats
Or keep a folder of reference clips (your inspiration bank), measure each one, and mix beats across specs into one prompt
Add your own custom shapes, fonts and camera moves by describing them in the shot descriptions

Then re-run stage 2 + 3. The render follows your numbers with the same precision it followed the reference's.

Rules for the agent

NEVER skip the measurement stage and improvise a prompt — the measured values are the whole point.
NEVER re-derive numbers from pixels after spec.json is written; edit the spec instead.
If the Higgsfield MCP is not connected, stop and give the user the one-line install command above.
Verify the model actually available on Higgsfield before claiming it (list models first; prefer Google Omni for JSON prompts, fall back to Seedance 2.0).
Aspect ratio 9:16 and 1080p by default unless the user says otherwise.

---name: reverse-motion-systemdescription: Reverse-engineer any motion design video from pixels alone, then recreate it — or your own original piece — through the Higgsfield MCP. Measures beat structure, camera moves, shapes and palette with computer vision, writes a spec.json contract, converts it into a structured JSON video prompt (Google Omni style), and ships the render via Higgsfield. Trigger on - reverse engineer this video, recreate this motion design, measure this reference, clone this animation, build my custom animation from my references.---# The Reverse Motion SystemRecreate pixel-perfect motion design from any reference video — by MEASURING it, not eyeballing it. Then flip the same system to render something entirely your own.Every camera move, shape, colour and cut in the final prompt is a calculated value extracted with computer vision. You are never prompting blind.## Requirements- Python 3.10+ with `opencv-python-headless` and `numpy` (`pip install opencv-python-headless numpy` — use a venv if your system blocks pip)- The **Higgsfield MCP** connected to Claude Code (`claude mcp add --transport http --scope user higgsfield https://mcp.higgsfield.ai/mcp`)## The pipeline (run it in order)### Stage 1 — Measure the reference```bashpython scripts/measure.py reference.mp4 -o spec.json```Computer vision reads every frame and pulls out:- **Structure**: cuts scored as frame-diff ratio against the local median, fitted black-window and white-flash detectors → the beat list- **Camera**: Farneback optical flow on lit pixels, solved per frame pair for a similarity transform (scale / rotation / translation) with physical admissibility guards → push-in, pull-back, pan, roll, per-frame values- **Elements**: connected-component blobs at each beat's midframe → shape class, normalized position and size- **Palette**: k-means over lit pixels per beat → locked hex values- **Look**: background floor, grain, bloom notes`spec.json` is the contract. Later stages read it and never re-derive a number from pixels.### Stage 2 — Convert the spec into a structured prompt```bashpython scripts/spec_to_prompt.py spec.json -o prompt.json --style "neon motion graphics on black"```Builds a shot-by-shot JSON video prompt: one shot per scene beat, transitions carried from the measured black/flash windows, camera phrased from the measured motion, palette locked to the measured hexes.### Stage 3 — Render through the Higgsfield MCPInside Claude Code, with the Higgsfield MCP connected, ask:> "Read prompt.json and generate this video on Higgsfield. Use the Google Omni model if it is available in the marketplace, otherwise Seedance 2.0. 9:16, 1080p, honor every shot duration, camera move and palette exactly."The agent lists the available Higgsfield models, picks the conversational JSON-prompt model, fires the generation, polls the job, and returns the video URL.### Stage 4 — Your own sauce (the point of the system)Copying proves the control. Now feed the system YOUR direction instead of a reference:- Edit `spec.json` directly — change palettes, swap camera moves, retime beats- Or keep a folder of reference clips (your **inspiration bank**), measure each one, and mix beats across specs into one prompt- Add your own custom shapes, fonts and camera moves by describing them in the shot descriptionsThen re-run stage 2 + 3. The render follows your numbers with the same precision it followed the reference's.## Rules for the agent1. NEVER skip the measurement stage and improvise a prompt — the measured values are the whole point.2. NEVER re-derive numbers from pixels after `spec.json` is written; edit the spec instead.3. If the Higgsfield MCP is not connected, stop and give the user the one-line install command above.4. Verify the model actually available on Higgsfield before claiming it (list models first; prefer Google Omni for JSON prompts, fall back to Seedance 2.0).5. Aspect ratio 9:16 and 1080p by default unless the user says otherwise.

Frequently asked questions