Vox Explainer logo

Vox Explainer

Organization
heygen-com
vox-explainer

Create a 60–90 second, collage-style HyperFrames explainer from a hiding-in-plain-sight topic or supplied documents and links. Use for requests such as 'make a plain-sight explainer', 'Vox-style history of X', 'why is X everywhere?', or 'turn this document into an explainer'. Covers topic or source routing, research, script, static design approval, voiceover timing, motion continuity, build, and numeric quality gates.

Overview

Publisherheygen-com
Repositoryhyperframes-community-skills
Skill namevox-explainer
Stars
226
Forks
22
Bundled files
13
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.

  • 13 bundled files

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

  • Open source

    Published by heygen-com on GitHub. Read the source before you install it.

Installation

Install the Vox Explainer 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/heygen-com/hyperframes-community-skills/tree/master/skills/vox-explainer
  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/heygen-com/hyperframes-community-skills.git /tmp/hyperframes-community-skills
mkdir -p .claude/skills
cp -r /tmp/hyperframes-community-skills/skills/vox-explainer .claude/skills/vox-explainer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Vox Explainer 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 Vox Explainer 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 Vox Explainer 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.

Plain-Sight Explainer (Gateway Workflow)

Build against the HyperFrames composition contract (clips, data-* timing, and the #root schema). Install the upstream HyperFrames skills with npx skills add heygen-com/hyperframes, or scaffold with npx hyperframes init, and read the generated project documentation first.

Make a 60–90s explainer about something the viewer sees daily but has never looked at. The film manufactures recognition first, then pays it off with a causal history. Grammar measured from Vox's Cooper Black film; motion from the same frame-by-frame calibration process.

This is a Vox-inspired design vocabulary, not an affiliation or endorsement. Do not use Vox logos or imply that Vox produced the result.

Trust boundary and requirements

  • Require Node.js 22 or newer for the bundled seam scripts. When they start a preview server, npx may download the exact HyperFrames version pinned in scripts/seam-gate.mjs from the npm registry.
  • Read only the files and URLs the user supplies. Source mode may fetch public citations and public-domain or openly licensed assets from declared sources. Never send private documents, credentials, or private URLs to another service.
  • Ask before using any TTS or transcription API. State the provider, data sent, required credential, and possible cost before the call.
  • Write project source, downloaded assets, audio, contact sheets, and renders only inside the user's chosen project directory. Do not publish or upload the result automatically.
  • The seam verifier starts a local preview server and an isolated Chrome profile. Its --url and --comp-url inputs accept localhost only. A preview may still load remote assets declared by the user's composition.

Reference map

Read only the references needed for the current stage:

Pipeline (run in order; each gate blocks the next)

  1. Entry router — two modes, one gate. Read plain-sight-topics.md.

    • Topic mode (the user names a subject, or asks for ideas): the four-part filter governs IDEATION and tie-breaking only. If a user-chosen topic fails it, name which part fails and what the film loses, offer passing alternatives — then build the film they asked for if they confirm. The filter never refuses a chosen subject.
    • Source mode (the user supplies material — documents: a memo, strategy doc, report, paper, deck, transcript, PDF, or a folder of them; or links: an article, docs page, repo, announcement): the filter does not apply at all. After confirming the allowed network scope, read every supplied file, fetch every supplied URL, and follow at most one public citation hop when needed for a claim. When a citation is dead or truncated (common in exported PDFs), do not guess the source — verify the claim independently on the open web before it becomes a receipt, or cut it. Then run the storyline mine. Person+date and PD-archive requirements are waived; the mechanism beat is not. The audience is the source's INTENDED audience, not "any viewer" — the FAMILIAR beat calibrates to what they see daily. The VO never mentions the source — see plain-sight-script.md.

    The storyline mine (source mode). The source is ore, not an outline — never film it section by section. Extract five things:

    • The on-ramp — the recognition the intended audience already has, in the source's own world (a habit, a number they see weekly, a thing on their screen right now).
    • The tension — the sharpest claim in the source that contradicts what that audience assumes.
    • The turn — the DIG's landing point: a person+date when the source has one, otherwise the decision, number, or moment the story pivots on.
    • The mechanism — the one claim the viewer can verify on screen unaided. If the source only asserts, BUILD the demonstration (two-panel compare, dots redistributed two ways, before/after at equal scale).
    • The receipts — real artifacts from the source plus the open web (charts, screenshots, filings, on-screen attributed quotes). Real-assets rule still holds.

    Map those onto the structural spine; beats the source can't fill honestly get cut, not faked. Multiple sources — or ONE source carrying several candidate storylines — mine each, then pick ONE spine: a film gets one storyline; everything else demotes to receipts. Deliver the beat map for approval before drafting script.

  2. Script — read plain-sight-script.md. Draft VO + beat map. GATE: every line passes the deletion test; opener/closer rules hold. If ghost-writing for a specific person, their voice skill outranks this one.

  3. Design pass — read vox-collage-layout.md. Build a contact sheet (one frame per beat, 960×540 tiles, HTML → headless-Chrome screenshot) from REAL assets. Deliver the sheet BEFORE building any comp. Iterate here — it is 10x cheaper than comp notes.

    LOOK GATE — check before delivering the sheet. Use this collage grammar without implying Vox affiliation. Verify, don't assume:

    • Palette is the measured set — ink #1a1a1a, yellow #FFE619, process blue #66CFFF, specimen blue #58BCEC, greys #F2F2F2 / #8d9399, ground #fbfaf8. Treat bright blues as fills and accents. Use #3E87A8 or darker for small text on white, and pass the renderer's contrast checks.
    • Headlines are Archivo Black.
    • Every frame is a named recipe from the layout reference (evidence stack, zoom-isolation, two-panel compare, specimen grid, lower-third, newsprint layering, circle reveal). Label each frame with its recipe on the sheet.
    • The yellow circle appears as the recurring visual carrier.

    The trap: when the source document belongs to a company with its own design system — or the project folder already holds a brand capture, frame.md, or an earlier build in that brand — the pull is to inherit those tokens. Do not. brand-faithful is a DIFFERENT workflow for product-launch films. A brand skin here is an explicit user decision, never a default. Ask; don't substitute.

  4. VO — record or TTS. The TTS input file is a PRONUNCIATION script: numbers written out ("eighteen thirty-nine"), names spelled as spoken. Transcribe the result for word timestamps — audio is the clock; every cut, pop, and highlight keys to a word time. A replacement VO = a full retime. Captions transcribe the PERFORMED read verbatim, not the script doc. TTS playbook: npx hyperframes tts first; if its bundled speech stack fails (old Python, espeakng-loader abort), install espeak-ng and call the Kokoro model directly with the CLI's cached model files. Use a TTS API only after the user approves its provider, data sharing, credentials, and cost. Then transcribe whatever produced the audio; the timestamps are the clock regardless of the engine.

  5. Build — one HyperFrames comp, one clip group per beat, quiet caption rail (suppress cues wherever promoted on-screen text carries the words). Read vox-text-overlays.md and vox-collage-motion.md before writing timelines.

  6. QC gates (all numeric, all mandatory)

    • lint/check: 0 errors.
    • Dead-time sweep: MAD every consecutive frame pair of the RENDER; any still run >3s is a planning bug — add a staged reveal, not wobble.
    • Seam measurement: for any zoom-isolation cut, render the last pre-swap and first post-swap frames, detect the carrier in both, verify centers within ~30px and sizes within ~10%. Fix by measuring, not eyeballing.
    • Event-density: per beat, largest gap between authored timeline events ≤3.0s (the motion reference explains why the MAD sweep cannot see creep-only holds).
    • Composition integrity: audit the final keyframe sheet against the layout reference's hard rules, item by item, logged.
    • Keyframe sheet: screenshot one frame per beat; look at it.

Technique floor (anti-slideshow gate — declare at beat-map time)

Passing the defect gates does not make a film. A build that resolves every beat to cards, side-by-sides, and lower-third text passes lint, seams, and dead-time and still reads as a slideshow — the collage idiom lives in techniques a cautious agent will never volunteer. So techniques are DECLARED in the beat map, not improvised at build time: every beat names its layout recipe AND its motion treatment from the motion reference's catalog, and the film's distribution must clear this floor:

  • ≥1 zoom-isolation swap — the camera pushes INTO a photograph, isolates the carrier, and cuts through it (numerically aligned per the motion reference).
  • ≥1 inverse zoom-through arrival, spent on a payoff beat.
  • ≥2 drive-pasts or scale traversals that exit at/through frame-fill.
  • ≥1 background-dropped cutout (subject lifted off its plate) doing motion a flat card cannot — docking into a grid, riding a drive-past, anchoring an iso swap.
  • ≥1 newsprint layering moment; the circle reveal (the brand carrier).
  • CAPS: static-card / side-by-side layouts ≤ one third of beats; no two consecutive beats share the same layout recipe or the same treatment.
  • When a photograph with interior depth is on frame, the camera enters it rather than drifting over the card row — flat drift over cards is the slideshow tell. Entering is governed by the zoom-in contract below.

The zoom-in contract. Pushing into a photograph promises the viewer the payoff is INSIDE the image. Exactly two legal continuations:

  1. Background dropout — the subject lifts off its plate and becomes the carrier (iso swap, dock, drive-past);
  2. Full-frame hold + callouts — the image holds at/near full-bleed while annotations draw ON it (labels, arrows, attention windows). A push into a photo that then cuts to an unrelated composition is banned — that is a transition wearing a technique's clothes. If neither payoff serves the beat, do not enter the photo.

Contract boundaries (what counts as "entering"): a sustained push whose origin sits inside the image and that reaches near-full-bleed. Seam-scale zooms (~0.2s whole-scene mechanics) and sub-10% camera creeps are different categories and are not entries. An element driving AT the camera is the mirror of an entry and obeys the same contract (it must already be a dropped-out carrier, never a flat card flying at the lens). A traversal may exit through frame-fill only into the same image or into the beat its own motion caused — never into an unrelated composition.

QC: the beat map logs the treatment distribution table; the final keyframe audit verifies each declared treatment actually appears on screen (the seam verifier already measures the zoom-isolation numerically). A film that cannot clear the floor honestly is a planning bug: re-plan the beats, do not pad with decoration. When the floor's minimums and the zoom-in contract conflict, the contract wins and the technique is cut — a missing technique is a smaller defect than an unmotivated one.

Structural spine (weights of a 60s cut)

This weighted spine is authoritative. THE FAMILY (see the script reference) is the back half of FAMILIAR — more instances of the recognition, not a new beat. THE ARTIFACT BEAT sits between STORY and MECHANISM in the script reference's spine too; when the two references seem to disagree, this list wins. In source mode, spine beats the source cannot fill honestly (SECOND WIND, the person in DIG) are CUT and their weight redistributed — the cut-don't-fake rule outranks the spine's completeness.

FAMILIAR 15% → DIG (person + date named) 5% → STORY 15% → THE ARTIFACT BEAT 10% → MECHANISM the viewer verifies on screen 20% → SECOND WIND (a technology removes a constraint) 15% → THESIS in the yellow circle 10% → CLOSE mirrors the open 10%.

Non-negotiables inherited from the motion layer

  • motion-continuity.md governs seams. Make hard-looking swaps land inside a verified continuation seam; a literal static-to-static cut is not exempt from the seam gate.
  • Real assets only — PD archives (Wikimedia Commons API, LOC), flagged recreations when a scan is missing. Recreating artifacts with shapes reads as slop.
  • Highlights always animate on per the text-overlay reference.
  • Elements step at 12fps over smooth eases; camera moves stay smooth per the collage-motion reference.

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 Vox Explainer AI skill do?

Create a 60–90 second, collage-style HyperFrames explainer from a hiding-in-plain-sight topic or supplied documents and links. Use for requests such as 'make a plain-sight explainer', 'Vox-style history of X', 'why is X everywhere?', or 'turn this document into an explainer'. Covers topic or source routing, research, script, static design approval, voiceover timing, motion continuity, build, and numeric quality gates.

Why use Vox Explainer on TypingMind?

Because you install it once and use it with any model. Vox Explainer 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 Vox Explainer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/heygen-com/hyperframes-community-skills/tree/master/skills/vox-explainer. 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 Vox Explainer?

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 Vox Explainer?

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

Is the Vox Explainer AI skill free?

Yes. It is published on GitHub by heygen-com 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.

View all

Set up your own AI workspace now

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