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Rn Motion Replica

CommunityPopular
Pluviobyte
rn-motion-replica

Analyze a public or local reference video, isolate a time range, deconstruct its layout, motion, timing, and transitions, then build an original verified motion replica with reusable templates and final MP4 QC. Use when the user says 复刻这个视频效果, 参考这个视频做同款动效, 拆解前 N 秒, 还原方框/连线/卡片动画, or provides a video or URL as a motion reference rather than asking to copy its footage or script.

Overview

PublisherPluviobyte
Repositoryrnskill
Skill namern-motion-replica
Stars
1.6K
Forks
181
Bundled files
12
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.

  • 12 bundled files

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

  • Open source

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

Installation

Install the Rn Motion Replica 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/Pluviobyte/rnskill.git /tmp/rnskill
mkdir -p .claude/skills
cp -r /tmp/rnskill/skills/rn-motion-replica .claude/skills/rn-motion-replica
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Rn Motion Replica 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 Rn Motion Replica 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 Rn Motion Replica 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.

RN Motion Replica

Turn a reference clip into an original motion study that preserves the visual grammar and timing while replacing the source's copy, presenter, footage, and unique assets.

This Skill targets an editable original adaptation. Route any pixel-exact, render-frame-exact, or encoded-frame-aligned claim through rn-replica-qc and its asset, runtime, and delivery gates.

Source and rights boundary

  • Work only from a public link or local file the user is authorized to use.
  • Do not bypass private-account controls, DRM, paywalls, or deleted media.
  • Keep the source URL, title, downloaded media, transcript, and source screenshots in a private working directory.
  • Do not ship the source media inside the finished project or Skill package.
  • Recreate motion structure, layout logic, pacing, and interaction language. Write new copy and use original or generic assets.

Workflow

1. Lock the study range

Identify the exact time range, the requested effects, the delivery aspect ratio, and whether the user wants a study preview or a full production asset. When the request is already specific, proceed without another intake round.

Default the canvas and fps to the reference unless the user or destination requires another format.

2. Acquire and verify the reference

Download the public reference with the workspace's trusted downloader or yt-dlp. Verify it with ffprobe; an HTTP success is not enough.

Require:

  • a playable video stream
  • non-zero duration
  • known dimensions and frame rate
  • an untouched source file in the private reference directory

3. Generate visual evidence

Run the bundled analyzer on the selected range:

bash
python3 scripts/analyze_reference.py <reference.mp4> \
  --start 0 --duration 35 --out <private-analysis-dir>

Inspect all generated contact sheets and scene frames. Do not infer choreography from one thumbnail.

Record five layers of evidence:

  1. visual tokens: background, border, palette, type, shadow, texture
  2. geometry: safe margins, anchors, card sizes, connector routing, z-order
  3. motion primitives: pop, slide, path draw, swap, push, blur, zoom
  4. sequence: what enters first, what waits, what replaces an earlier state
  5. timing: entrance duration, dwell, overlap, transition time, final hold

Write frame.md for the visual contract and STORYBOARD.md for the timestamped sequence before authoring animation.

For outlined cards and orthogonal connectors, read references/box-motion-pattern.md.

4. Choose the production route

Use HyperFrames by default for an authored finished video:

  • short, unnarrated, motion-first shot around 10 seconds or less: route through the motion-graphics workflow
  • longer, multi-state, or multi-scene replica: route through general-video
  • existing talking-head footage that stays intact under new graphics: route through talking-head-recut

For the bundled box-motion starting point:

bash
python3 scripts/init_motion_project.py <new-project-dir>

The template is an editable example, not a content contract. Replace its labels, timings, colors, and layout with evidence from the current reference.

5. Build layout before motion

Construct each scene at its most visible state first. Confirm that cards, labels, and connectors fit without animation.

Then animate in reading order:

  1. frame and section label
  2. first focus node
  3. next endpoint
  4. connector between visible endpoints
  5. supporting cards one at a time
  6. state swap or scene transition
  7. readable final hold

Keep connectors behind cards. Initialize every SVG connector and junction as hidden at time zero; reveal each only at its cue. A floating connector or junction before its nodes is a failed frame.

Use seek-safe deterministic timelines. Do not use render-time clocks, unseeded randomness, hover state, or infinite animation loops.

6. Validate the composition

Run the framework's full check, then capture explicit snapshots at:

  • the opening entrance
  • every graph or layout state change
  • immediately before, during, and after each scene transition
  • the final held frame

Inspect the rendered pixels. Fix premature elements, blank handoffs, connector crossings, clipped text, inconsistent borders, and off-brand easing before rendering a full MP4.

7. Render and verify the final MP4

Render only when the user's request includes an output video or the user has approved the preview.

Run post-render QC against the MP4 itself, not only the HTML preview. Read references/qc-checklist.md and preserve:

  • media probe JSON
  • final-MP4 contact sheet
  • representative opening, state-change, transition, and final frames
  • black-frame scan result

8. Deliver the reusable package

Use this shape:

text
<project>/
├── frame.md
├── STORYBOARD.md
├── index.html
├── renders/
│   └── motion-replica.mp4
└── qc/
    ├── media-probe.json
    ├── contact-sheet.jpg
    └── representative-frames/

Keep the original reference and source-identifying analysis outside this deliverable.

Completion gates

  • Do not claim a match without inspecting multiple timestamps from the requested range.
  • Do not claim pixel or frame exactness from this workflow alone; use rn-replica-qc for full-frame evidence.
  • Do not copy the source's presenter, subtitles, script, thumbnail, or unique branded assets unless the user owns them and explicitly requests reuse.
  • Do not allow a connector, junction, or card to appear before its timeline cue.
  • Do not treat preview-only correctness as delivery; recheck the final MP4.
  • Do not replace the requested video with contact sheets. Contact sheets are QC evidence, not the deliverable.

Bundled resources

  • scripts/analyze_reference.py: generate probe data, overview and dense contact sheets, and scene-change frames.
  • scripts/init_motion_project.py: copy the reusable HyperFrames box-motion project without overwriting an existing directory.
  • references/box-motion-pattern.md: card, connector, state-swap, and push-transition implementation guidance.
  • references/qc-checklist.md: pre-render and final-MP4 validation gates.
  • assets/templates/box-motion-hyperframes/: editable source project used by the initializer.
  • assets/showcases/box-motion-replica-35s.mp4: 35-second finished example.

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 Rn Motion Replica AI skill do?

Analyze a public or local reference video, isolate a time range, deconstruct its layout, motion, timing, and transitions, then build an original verified motion replica with reusable templates and final MP4 QC. Use when the user says 复刻这个视频效果, 参考这个视频做同款动效, 拆解前 N 秒, 还原方框/连线/卡片动画, or provides a video or URL as a motion reference rather than asking to copy its footage or script.

Why use Rn Motion Replica on TypingMind?

Because you install it once and use it with any model. Rn Motion Replica 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 Rn Motion Replica in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Pluviobyte/rnskill/tree/main/skills/rn-motion-replica. 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 Rn Motion Replica?

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 Rn Motion Replica?

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

Is the Rn Motion Replica AI skill free?

It is published on GitHub by Pluviobyte. Check the repository for licensing terms. 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.

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