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Motion Graphics

Community
waker240
motion-graphics

Create a short design-led motion graphic, title treatment, or transparent overlay in HyperFrames. Use when the main deliverable is visual motion rather than narration.

Overview

Publisherwaker240
RepositoryFullVideoProductionSkill
Skill namemotion-graphics
Stars
211
Forks
47
Bundled files
22
LicenseApache-2.0
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.

  • 22 bundled files

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

  • Open source

    Published by waker240 on GitHub. Read the source before you install it.

Installation

Install the Motion Graphics 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.
    https://github.com/waker240/FullVideoProductionSkill/tree/main/skills/motion-graphics
  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/waker240/FullVideoProductionSkill.git /tmp/FullVideoProductionSkill
mkdir -p .claude/skills
cp -r /tmp/FullVideoProductionSkill/skills/motion-graphics .claude/skills/motion-graphics
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Motion Graphics 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 Motion Graphics 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 Motion Graphics 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.

Install this skill with its sibling skills. See runtime setup and capability boundaries before executing commands. User instructions and the requested production stage take precedence over workflow defaults.

motion-graphics — dispatch entry

Confirm the route before Step 0. This skill makes a short, design-led, unnarrated motion graphic (motion is the message; ~under 10s, no voice-over). A longer, multi-scene, or narrated treatment → /general-video; any admitted multi-region Spatial Canvas whose persistent geography/revisit/synthesis is the composition grammar → /general-video regardless of duration (an ordinary one-way pan can stay here); a narrated video of a website → /website-to-video; a topic explainer → /faceless-explainer; a product promo → /product-launch-video; captions on existing footage → /embedded-captions. Out of scope: live / at-render-time data, or footage it can't capture. Unsure motion-first-vs-narrated? Read /hyperframes first.

A short design-led motion graphic. Asset-first: decide the asset strategy and source real material before designing the shot, then design the shot around what you have, then compose by reusing catalog capabilities. All artifacts go to PROJECT_DIR = videos/<project-name>/ (created in Step 0); all paths below are relative to it.

PhaseExecutionPrimary artifactDetailed flow
initBashhyperframes.jsonStep 0
plansubagent — decide external asset? + classify + asset strategyshot-plan.json (draft: category, asset_needs, brief)agents/director.md (Part 1)
source ◇review/freeze files + media-use adopt (skip if needs are empty)assets/ + provenance ledgerphases/source/guide.md
designsubagent — shot design around resolved assetsshot-plan.json (final: block(s) + layout + motion + positions)agents/director.md (Part 2)
buildsubagent — reuse-first compositioncompositions/index.htmlagents/builder.md
renderBash — hyperframes render (MP4, or --format webm/mov for overlay)renders/video.mp4Step 5
verifyBash — lint / inspect -> repair subagent on failure(fixes in place)agents/finalize.md

◇ source runs only when the chosen category declares assets. Pure code/text categories (e.g. kinetic-type, most charts/stat) have asset_needs: [] and skip straight from plan to design.

Categories — split by the asset decision

plan's first decision is: does this need an external asset? That fork splits the categories into two groups; then the specific category is picked — for source-backed work, by the type of reviewed content available. Each category is one categories/<id>/module.md (its planning + build rules); the shared motion vocabulary lives in references/motion-vocabulary.md (→ hyperframes-animation rules/blueprints + registry blocks).

Form categories — no external asset; the user supplies the content:

CategoryIntentLeans on
kinetic-typepunchy line / quote / title, motion-first textcaption-* blocks + animation rules
statsingle hero number / count-up + ringapple-money-count / rules/{counting-dynamic-scale, stat-bars-and-fills}
chartsbar / line / pie / race / % from datadata-chart block
logo-reveallogo sting / brand lockup (user logo)logo-outro / rules/svg-path-draw
lower-thirdsname / title bars, callouts, social overlayscaption-* + registry overlay blocks

Source-backed categories — review/freeze first, then animate by content type:

Returned contentCategoryAnimation
webpage / linkwebpagewebpage / UI animation (scroll, reveal, cursor, callouts)
news articlenewsheadline reveal + source card + key-fact callouts
tweettweetanimated tweet card
image / entityasset-fusionthe asset's geometry becomes the chart (RWA diegetic fusion)

Build order: one at a time, coverage-first (rough is fine). kinetic-type ported from the prototype; the rest follow.

Prerequisites

macOS Apple Silicon or Linux x64. System tools: brew install node ffmpeg. npx hyperframes doctor once. macOS GPU render: export PRODUCER_BROWSER_GPU_MODE=hardware.

media-use needs no provider or generation key. It inventories reviewed local files, probes them, and registers their provenance. Any external discovery/download happens separately with an authorized research or capture capability and must finish with a human/agent review before adoption.

Flow

Step 0 — Initialize

cwd is the agent workspace root; write all artifacts under PROJECT_DIR = videos/<project-name>/. <project-name>: use the dir the user gave, else a short kebab-case name from the intent (<subject>-motion). Not the workspace basename or a timestamp.

Only when $PROJECT_DIR/hyperframes.json is absent:

bash
PROJECT_DIR="${MOTION_GRAPHICS_DIR:-videos/<project-name>}"
mkdir -p "$(dirname "$PROJECT_DIR")"
npx --yes hyperframes@0.7.17 init "$PROJECT_DIR" --non-interactive --skip-skills --example=blank

init checks the installed skills against the latest on GitHub and updates the global set if any are out of date.

Constraints: never hyperframes init in the workspace root; never nest another hyperframes/ inside PROJECT_DIR; every Bash command (master + subagents) is a (cd "$PROJECT_DIR" && ...) subshell — never bare cd.

Step 1 — Plan (subagent: Director Part 1)

Dispatch one subagent. prompt = full agents/director.md + ## Dispatch context (SKILL_DIR / PROJECT_DIR / the user's request / Schema: <SKILL_DIR>/references/shot-plan-ir.md). It must:

  1. Decide: does this need an external asset? (the first fork)
    • No → pick a form category (kinetic-type / stat / charts / logo-reveal / lower-thirds); content is user-supplied; asset_needs: [].
    • Yes → emit an asset plan into asset_needs[] (news / web / tweet / image, including source and license requirements). The specific source-backed category (webpage / news / tweet / asset-fusion) is confirmed by the reviewed content available after Step 2, and finalized in Step 3.
  2. Write a draft shot-plan.json (envelope + chosen form category or source-backed intent + asset_needs + a one-paragraph shot brief). Schema: references/shot-plan-ir.md.

Validation: [ -s "$PROJECT_DIR/shot-plan.json" ] && echo ok || echo missing.

Step 2 — Source ◇ (Bash: media-use, conditional)

If shot-plan.json.asset_needs is non-empty, satisfy each need with a user-supplied file, an already-reviewed local file, or a separately researched/captured item whose source and usage terms were reviewed. Freeze the selected files under the project, then run media-use --adopt to probe and register them. media-use does not search, fetch, or generate. See phases/source/guide.md. If asset_needs is empty, skip to Step 3.

bash
# after individually reviewed files have been frozen under assets/
(cd "$PROJECT_DIR" && node <MEDIA_USE_DIR>/scripts/resolve.mjs --adopt --project .)

Degrade gracefully: if a needed reviewed asset is unavailable, the category falls back to asset-free where possible (note it in context.log).

Step 3 — Design (subagent: Director Part 2)

Dispatch a subagent (prompt = agents/director.md Part 2 + dispatch context including the resolved assets/index.md if Step 2 ran + catalog-map.md). It designs the shot around the available assets: pick the catalog block(s) + the hyperframes-animation rules/blueprints, the layout, the motion, beats, and (for asset-fusion) the element_positions + eyedropper palette. If the request intentionally calls for a high-velocity montage/overwhelm/rupture treatment, it reads ../hyperframes-creative/references/fast-paced-editing.md and fills the optional fast_passage plan; ordinary energetic motion leaves it null. Finalizes shot-plan.json (content.block + content.customize + per-category content).

Step 4 — Build (subagent: Builder, reuse-first)

Dispatch a subagent. prompt = full agents/builder.md + dispatch context (shot-plan.json, catalog-map.md, the category's module.md, references/motion-vocabulary.md, references/builder-contract.md). Reuse-first: npx hyperframes add <block> + customize in place; hand-author only gaps + the asset-fusion affordance. Output compositions/index.html honoring the HF contract (paused GSAP timeline on window.__timelines, class="clip" + stable ids, tl.seek(0), deterministic).

Step 5 — Render (Bash)

bash
(cd "$PROJECT_DIR" && npx hyperframes render . --skill=motion-graphics -q draft -o ./renders/video.mp4)
# transparent overlay variant: --format webm  (or mov)

Step 6 — Verify (Bash → repair subagent on failure)

bash
(cd "$PROJECT_DIR" && npx hyperframes lint . && npx hyperframes inspect .)

exit 0 → done. On lint/inspect errors, dispatch the repair subagent (agents/finalize.md: snapshot QA + one in-place fix pass + re-render). Never change a fixed duration in repair.

When shot-plan.json.fast_passage is present, watch the final render at normal speed from baseline through release and confirm one legible focal path, an earned peak, reacquired orientation, restrained luminance changes, and sound/readability appropriate to this unnarrated format. Repair and rerender the same complete arc if any fails; snapshots are not sufficient.

Report + optional preview

Report the final output (renders/video.mp4, or the .webm / .mov overlay variant) + duration. Don't open a preview during the run. Offer one only on request, started after render so it serves the final file:

bash
(cd "$PROJECT_DIR" && npx hyperframes preview)   # Studio UI; or `npx hyperframes play` for a shareable link

The separate hyperframes-cli skill is not included. Use npx --yes hyperframes@0.7.17 preview --help or render --help for the installed CLI's actual flags.

Resume table

StateContinue from
no shot-plan.jsonStep 1 (plan)
shot-plan.json has asset_needs, no assets/Step 2 (source)
shot-plan.json final, no compositions/index.htmlStep 3/4 (design+build)
compositions/index.html exists, no renders/video.mp4Step 5 (render) + Step 6
renders/video.mp4 existsReport + stop

Design notes (maintainers — execution does not read this)

  • Asset-first rationale: sourcing is front-loaded and informs shot design: analyze the need → inspect or research candidates → review the selected source and usage terms → freeze → adopt → compose. Source-backed categories (webpage/news/tweet) and asset-fusion consume only reviewed project-local files.
  • Reuse-first: the in-ecosystem analog of LLM-generated templates is "compose catalog blocks + hyperframes-animation rules". HF's paused GSAP timeline ≙ Remotion's useCurrentFrame.
  • Category module contract: one categories/<id>/module.md (planning + build), sharing references/motion-vocabulary.md (+ optional eval). Adding a category = drop the folder + register its classifier line in agents/director.md + its row in catalog-map.md; the phase pipeline is untouched.
  • Directory shape:
    videos/<project-name>/
      hyperframes.json  context.log
      shot-plan.json            # the IR (Director output)
      assets/                   # reviewed, frozen media (if sourced)
      .media/index.md           # media-use adoption ledger
      compositions/index.html   # Builder output
      renders/video.mp4
  • Registration: in hyperframes router — add the "design-led short motion graphic" intent + Workflow description; carve the motion-graphics triggers out of /general-video; add reverse Do-NOT-use edges. See motion-graphics-genre.md §5-7.

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 Motion Graphics AI skill do?

Create a short design-led motion graphic, title treatment, or transparent overlay in HyperFrames. Use when the main deliverable is visual motion rather than narration.

Why use Motion Graphics on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/waker240/FullVideoProductionSkill/tree/main/skills/motion-graphics. 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 Motion Graphics?

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 Motion Graphics?

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

Is the Motion Graphics AI skill free?

Yes. It is published on GitHub by waker240 under the Apache-2.0 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.

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