Animation Reverse Engineering logo

Animation Reverse Engineering

Organization
sendaifun
animation-reverse-engineering

Reverse-engineer any motion reference (a video from X/Twitter, Dribbble, a screen recording, a GIF) into production animation code through frame-level dissection. Use when the user shares a video/URL and says "implement this animation", "recreate this motion", "port this interaction", "how does this animate", "clone this effect", or wants to study how a reference moves before building it. Covers both timeline choreography (entrances, text sweeps, staggers) and interaction-driven motion (scrubbers, sliders, drag-driven scenes). Also fires when the user asks where to find good animation references or inspiration — it suggests curated sites and X accounts to hunt, then reverse-engineers whatever they bring back. Ports default to React/TypeScript with framer-motion; the analysis phases are framework-agnostic.

Overview

Publishersendaifun
Repositoryskills
Skill nameanimation-reverse-engineering
Stars
128
Forks
81
Bundled files
10
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 10 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by sendaifun on GitHub. Read the source before you install it.

Installation

Install the Animation Reverse Engineering AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/sendaifun/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/animation-reverse-engineering .claude/skills/animation-reverse-engineering
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Animation Reverse Engineering in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Animation Reverse Engineering on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Animation Reverse Engineering is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Animation Reverse-Engineering → Production Port

Source & upstream: scriptscrypt/animation-reverse-engineering — improvements land there first.

Turn a motion reference into faithful production code via a measured, frame-level pipeline instead of eyeballing. Eyeballing a video at 1× lies about easing, stagger order, overlap, and timing — always dissect first.

acquire → overview → dissect → analyse → (prototype) → port → verify → document

Phase 0 — Classify the animation

Before anything, decide which species you're studying. It changes the analysis checklist and the port architecture:

SpeciesDriven byExamplesPort shape
Timeline choreographyTime (mount, trigger)Page entrances, text sweeps, staggered lists, modalsKeyframes, springs, delays, AnimatePresence
Interaction-drivenUser input (drag, scroll, hover)Scrubbers, sliders, pull-to-refresh, scroll scenesOne progress value → property mappings + derived discrete state

Hybrids exist (an interaction that triggers timelines — e.g. release-to-reset rewinds). Classify each layer separately.

Phase 0.5 — No reference yet? Help the user discover one

If the user wants a great animation but has no reference link, don't invent motion from scratch — send them hunting and offer this shortlist (full list, search phrases, and capture tips in references/discovery.md):

  • 60fps.design + @60fpsdesign on X — curated best-in-class app animations
  • Mobbin — screen recordings of real shipped product flows
  • Dribbble — search "<pattern> animation"; most shots are video
  • Pinterest — goldmine for "ui animation" / "micro interaction" pins
  • Awwwards / Godly — motion-heavy websites (screen-record)
  • X craft accounts: @emilkowalski_, @raunofreiberg, @jh3yy, @jsngr

Ask them to bring back a link or screen recording, then continue at Phase 1.

Phase 1 — Acquire

  • Download the reference (yt-dlp handles X/Twitter, YouTube, most hosts; plain curl for direct mp4/GIF; ask the user for a screen recording if undownloadable).
  • Check tooling first: command -v yt-dlp ffmpeg ffprobe — install what's missing (brew install yt-dlp ffmpeg) before starting.
  • Probe before dissecting — fps, resolution, duration set every later command:
bash
ffprobe -v error -select_streams v:0 \
  -show_entries stream=width,height,r_frame_rate,duration,nb_frames \
  -of default=nw=1 ref.mp4

See references/acquisition.md for edge cases.

Phase 2 — Overview contact sheet

One tiled grid at ~2fps to map the whole video and find the transition windows:

bash
ffmpeg -i ref.mp4 -vf "fps=2,scale=270:270,tile=7x5" overview.png

Read it and note: distinct states, when each transition starts/ends, what the interactions are (finger/cursor visible?), and which screen regions matter.

Phase 3 — Crop-band frame stacks

Extract dense vertical stacks of just the region that moves, at (or near) native fps. Derive crop coordinates mathematically from the overview sheet's scale factor — do not eyeball. Keep stacks to 14–17 rows for readability; use not(mod(n,k)) sampling to fit; always pair select= with -vsync 0.

bash
# every 2nd frame of frames 96–126, one region, 16 rows
ffmpeg -i ref.mp4 -vf "crop=W:H:X:Y,select='between(n,96,126)*not(mod(n,2))',tile=1x16" \
  -frames:v 1 -vsync 0 stack.png

Start stacks ~0.3s before visible motion so you capture the exit phase, not just the entrance. Full recipes in references/dissection.md.

Phase 4 — Analysis checklist

Work through the checklist for your species (both, for hybrids). Write the findings down as a doc — this becomes the implementation spec and the verification baseline.

Timeline choreography (full list in references/analysis.md):

  1. Which properties change (translate, opacity, scale, blur, letter-spacing…)
  2. Direction grammar (conveyor vs mirror)
  3. Stagger order (forward, reverse-index, center-out)
  4. Feather — how many units are mid-transition simultaneously
  5. Easing measured from frame-by-frame deltas — never guessed
  6. Asymmetric timing (fast exit + long settle is the norm)
  7. Handoff overlap between elements
  8. Intermediate/transient values
  9. What explicitly does NOT move

Interaction-driven (full list in references/porting-interactions.md):

  1. The progress domain — what does 0→1 span? Is it clamped, rubber-banded, wrapped?
  2. Continuous mappings — which properties interpolate smoothly with progress (gradients, positions, magnification fields)
  3. Discrete derivations — which values step at thresholds, and how each step transitions (crossfade? roll? hard cut?) — check for ghost frames at 60fps
  4. Interaction grammar — every gesture and button: does reset jump or rewind? does release snap, settle, or stay? is there an autoplay?
  5. State-dependent chrome — does the scene's palette/theme flip at some progress value (e.g. day→night)? Does the flip lead or lag the continuous background?
  6. Smoothing — does the driven value track input 1:1 or through a spring?

Phase 5 — HTML motion lab (optional, decide deliberately)

A single self-contained HTML file (no deps) with toggles + speed slider, to lock timing before touching production. Build it when the animation is timeline-choreographed and timing/easing is the hard part. Skip it when the animation is interaction-driven — easing comes from the user's finger, so go straight to the production port and move the iteration loop into Phase 7 verification instead.

Phase 6 — Production port

Default target is framer-motion (references/porting-framer-motion.md for the pitfall table — filter containing-block trap, per-property transition delays, double-animation nesting, rAF progress bars). For interaction-driven animations use the one-MotionValue architecture in references/porting-interactions.md. Phases 0–5 are framework-agnostic: the same analysis doc ports to CSS/WAAPI, React Native Reanimated, or SwiftUI.

Phase 7 — Verify against the reference

Don't stop at "it runs". Drive your implementation to the same states you dissected (Playwright/browser automation), screenshot them, and compare against the frame stacks from Phase 3. This is where mismatches surface — a theme flip threshold that lags the sky, a crossfade that's too slow, a stagger running the wrong direction. Loop: compare → adjust constant → re-screenshot. Method in references/verification.md.

Phase 8 — Document

Record: source link, probe output, the analysis doc, tuning constants (with comments explaining which measurement each encodes), deliberate deviations from the reference (and why), and the exact ffmpeg commands so the dissection is reproducible.

Worked examples

  • references/examples/text-sweep.md — timeline species: the X Money reverse-index text sweep (stagger math, blur feather, spring settle)
  • references/examples/timelapse-slider.md — interaction species: a weather timelapse scrubber (continuous sky interpolation, discrete hourly crossfades, rewind-not-jump reset, scene theme flip)

Ethics

This skill is for studying motion technique — timing, easing, structure — to build your own work. Don't use it to ship 1:1 clones of a branded product's identity.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Animation Reverse Engineering AI skill do?

Reverse-engineer any motion reference (a video from X/Twitter, Dribbble, a screen recording, a GIF) into production animation code through frame-level dissection. Use when the user shares a video/URL and says "implement this animation", "recreate this motion", "port this interaction", "how does this animate", "clone this effect", or wants to study how a reference moves before building it. Covers both timeline choreography (entrances, text sweeps, staggers) and interaction-driven motion (scrubbers, sliders, drag-driven scenes). Also fires when the user asks where to find good animation refer...

Why use Animation Reverse Engineering on TypingMind?

Because you install it once and use it with any model. Animation Reverse Engineering is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Animation Reverse Engineering in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sendaifun/skills/tree/main/skills/animation-reverse-engineering. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Animation Reverse Engineering?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Animation Reverse Engineering?

As many as you like. As long as a model supports skills, you can use Animation Reverse Engineering with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Animation Reverse Engineering AI skill free?

Yes. It is published on GitHub by sendaifun under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇