Music To Video logo

Music To Video

OrganizationPopular
heygen-com
music-to-video

Turn a music track (an audio file, a video to pull audio from, or a track generated from a mood brief) into a beat-synced video — lyric video, slideshow, or kinetic promo. The music drives all pacing; any user-supplied images/videos are cut onto the same beat grid, and a complete video needs zero assets. Narrated pieces → the input-matched workflow (see /hyperframes). Unclear → /hyperframes.

Overview

Publisherheygen-com
Repositoryhyperframes
Skill namemusic-to-video
Stars
54.8K
Forks
5K
Bundled files
168
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.

  • 168 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 Music To Video 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/tree/main/skills/music-to-video
  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.git /tmp/hyperframes
mkdir -p .claude/skills
cp -r /tmp/hyperframes/skills/music-to-video .claude/skills/music-to-video
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Music To Video 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 Music To Video 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 Music To Video 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.

Plugin installs: Before setup or freshness commands, follow plugin execution rules when this skill is inside a HyperFrames plugin. Standalone installs keep the update instructions below.

First, keep this skill fresh — confirm with the user before running: npx hyperframes skills update music-to-video. A fast no-op when everything is current; otherwise it refreshes this skill plus the core domain skills it depends on before you rely on them.

music-to-video — one music-grounded, beat-synced video workflow

Use this skill to turn a music track into a beat-synced HyperFrames video. You analyze the track once, lay out the frames, fill in a per-frame plan, and build each frame as a composition. The input is a music track plus optional user images or videos — there is no narration and no website capture. Typography and templates are the floor (a complete video needs zero assets); any media the user supplies is cut in on the same beat grid.

You are the orchestrator. Work in videos/<project>/. Run the steps in order and pass each Gate before moving on. Two steps need the user: Step 3 (plan approval) and Step 6 (render approval) — both are checkpoint gates per ../hyperframes/references/brief-contract.md (read it before Step 0): in autonomous mode, post the summary as a heads-up and proceed instead of waiting. Do every step yourself except Step 4, where you dispatch one sub-agent per frame. Keep design and motion rules out of this file — they live in references/ and the frame-worker sub-agent.

SKILL_DIR = this skill directory. PROJECT_DIR = videos/<project-name>/.

Workflow: Step 0 setup → hyperframes.json + assets/bgm.mp3; Step 1 analyze → audiomap.json; Step 2 skeleton → STORYBOARD.md (frames, groups TBD); Step 3 plan → complete STORYBOARD.md + frame.md; Step 4 build → compositions/frames/NN-*.html; Step 5 assemble → index.html; Step 6 render → renders/video.mp4.

Two ideas that shape everything

  • One analyzer, and you trust it. analyze-beatgrid.py is the only beat analyzer — never re-measure beats with another tool or by ear. Its energy / density / rolls / onsets / silences are always reliable. Its bpm and beats_sec are reliable only when the music is genuinely rhythmic; on calm music the grid is a metronome the tracker imposed, so pace by phrases and energy instead and never hard-cut to it. Deciding which case you're in is each frame's pacing (Step 2).
  • One frame = one file; groups live inside. Step 2 cuts the track into frames, and each frame becomes one composition file compositions/frames/NN-<frame_id>.html, built by one frame-worker. A frame can subdivide into groups (each a template or a motion-primitives combo). Extra density goes inside a group, so frame count tracks distinct treatments, not beats — a fast track does not blow up the number of sub-agents.

Step 0: Setup, BGM, and inputs

Goal: Establish the music source, create the HyperFrames project, and note any user-supplied media.

The brief starts at the intent layer. Opening rule, in order: (1) BRIEF.md exists → read it and ask nothing it answers — its flow/storyboard derive the mode (brief contract § 1). (2) No BRIEF.md but the project exists → resume from what's on disk; never re-interrogate. (3) A fresh creation request that arrived here directly → read /hyperframes and run its intent layer (references/intent-interview.md): it confirms this route's must-haves (the music source, destination → aspect — ../hyperframes/references/routes/music-to-video.md) and announces what stays deferred — brand and genre are chosen at Step 3 by design. Write BRIEF.md immediately after init (never before — init refuses a non-empty directory) and record the preference-backed answers (brief-format.md). Edit requests skip all of this.

The music is the spine — establish one track before anything else. This skill is tuned for fast, high-energy BGM: a strong beat grid drives the cuts (calm tracks work, but pace by phrase rather than beat). If the user supplied audio — a music file, or a video to pull audio from — use it. Otherwise choose the mood from the request and generate a track through /media-use (references/bgm.md). Before the first authenticated provider action, run npx hyperframes auth status and relay its output verbatim. If signed out, apply one branch:

  • Collaborative: wait for sign-in or an explicit choice to continue offline with the local provider.
  • Autonomous: state the status and continue through the available local provider.

If no offline provider can satisfy the required music capability, surface the blocker. Never write keys into a per-repo .env. Auth ownership and offline fallbacks live in /media-use references/setup-providers.md § Providers. The resulting track lands at assets/bgm.mp3. Stage supplied images or videos so frames can use them on the beat grid; otherwise typography carries the video.

Lyric videos: for lyrics synced to the vocals, get word/line timing by transcribing the track via /media-use, or ask the user for the lyrics text and place lines on the beat grid.

Initialize only if hyperframes.json is missing. Name <project> from the brief in kebab-case, such as midnight-drive-loop — never a timestamp. init checks the installed skills against the latest on GitHub and updates the global set if any are out of date.

bash
npx hyperframes init "videos/<project>" --non-interactive --example=blank --skill=music-to-video
mkdir -p "$PROJECT_DIR/assets" "$PROJECT_DIR/renders"
cp "<user-music>" "$PROJECT_DIR/assets/bgm.mp3"   # extract from a video first if needed
# only if the user gave you images/videos:
node <SKILL_DIR>/scripts/stage-assets.mjs --from <dir> --hyperframes "$PROJECT_DIR" --into public

The brand (font + palette) is chosen at Step 3, not here. Don't pick a genre or a track type up front — assets are just an optional ingredient, and the genre emerges from the per-frame choices.

Gate: hyperframes.json + assets/bgm.mp3 exist; aspect / length / fps and (if any) the asset inventory are noted.


Step 1: Analyze the music

Goal: Produce the one canonical timing analysis the whole video is built on.

analyze-beatgrid.py is the only beat analyzer — never re-measure beats with another tool or by ear. It reads the track once and writes audiomap.json: energy phases (level / density / feel), onsets + onset_rate, rolls, silences, hard_stops, key_moments, phrases, tempo / grid, and audio.duration_sec. It's deterministic — the same file always gives the same map. Most fields are reliable on any music; bpm and beats_sec are reliable only when the music is genuinely rhythmic, and judging that is the call you make at Step 2.

Prerequisites: Python 3 with librosa, numpy, and soundfile available. If import fails, install them into the active Python environment before running the analyzer:

bash
python3 -m pip install librosa numpy soundfile
bash
python3 <SKILL_DIR>/scripts/analyze-beatgrid.py "$PROJECT_DIR/assets/bgm.mp3" \
  -o "$PROJECT_DIR/audiomap.json" --print

Gate: audiomap.json exists; audio.duration_sec is known.


Step 2: Frame skeleton (structure only)

Goal: Read the music and lay out the frames — the skeleton of STORYBOARD.md.

Read references/frame-skeleton.md. Turn audiomap.json into the skeleton of STORYBOARD.md yourself — there is no intermediate JSON. Cut the track into frames at real musical changes (hard_stops, SURGE / DROP key_moments, the edges of a roll, a stretch with no onsets, a big energy jump), snapping every boundary to an audiomap anchor. For each frame set span_sec, pacing (the verdict from Step 1's trust call — beat_cut when the grid is real, phrase_flow when it's a metronome imposed on calm music), mood, and a one-line feel (the plain music situation Step 3 matches a template against). Only classify and lay out here: leave every frame's ### Groups as TBD (Step 3) and the frontmatter style blank — no templates, copy, color, or fonts. Expect ~1–6 frames.

Gate: frames tile the track (first at 0, last at duration_s); each carries span_sec + pacing + mood + feel; every ### Groups is TBD; no content anywhere.


Step 3: Fill the plan (user-gated)

Goal: Turn the skeleton into an approved, complete STORYBOARD.md.

Read references/planning.md, storyboard-format.md, template-catalog.md, motion-primitive-catalog.md, and montage.md (only if the user supplied assets). Editing the same file in place, do two things:

  1. Pick the brand. Choose one preset from ../hyperframes-creative/frame-presets/ using the table in ../hyperframes-creative/references/design-spec.md (match the track's mood; only its fonts and colors matter — templates own composition). Copy it into frame.md unmodified and fill the frontmatter style (font + a ≤4–6 swatch palette) from it.
  2. Fill every frame. Decide its groups and give each a treatment: a matched template from the catalog (with bound params and real audiomap anchors), a free-compose from the primitive catalog, or an asset treatment that obeys pacing. Before you free-compose a named look, search the live catalog for it: for every look, effect, treatment or transition the user asked for — "CRT scanlines", "glitch", "film grain", "shimmer sweep" — run npx hyperframes catalog --query "<the look, in plain English>" --json and read the top results. template-catalog.md and motion-primitive-catalog.md list only this skill's own local materials; the search ranks the whole hosted registry (~400 blocks and components) and needs nothing installed — no project, no prior add, no account. Free-compose a look only after a search for it came back with nothing that fits. Write the copy. You own WHAT (template / primitives + content + anchors); the frame-worker owns HOW — never write millisecond tweens into the storyboard.
bash
node <SKILL_DIR>/scripts/validate-plan.mjs --storyboard "$PROJECT_DIR/STORYBOARD.md" \
  --audiomap "$PROJECT_DIR/audiomap.json" --templates <SKILL_DIR>/references/templates

Fix every ✗ (hard errors: duration mismatch, frames not tiling the track, a missing src); warnings are best-effort. Then present the frame-by-frame summary in chat as a proposal (../hyperframes/references/review-loop.md § 1) and iterate on the user's replies until they approve; for storyboard: yes, also write it as storyboard.html (../hyperframes-creative/references/storyboard-recipe.md § 3) for them to open. In autonomous mode this is a checkpoint gate: post the summary as a heads-up and proceed (the validate-plan.mjs pass is a quality gate and still blocks).

Gate: frame.md is a verbatim preset copy; validate-plan.mjs exits 0; the user approved the plan (autonomous: the summary was posted as a heads-up).


Step 4: Build frames from the plan

Goal: Build every frame as a self-contained composition file.

Create compositions/frames/. Read sub-agents/frame-worker.md and ../hyperframes/references/subagent-dispatch.md. Dispatch one frame-worker per frame, in parallel where possible (otherwise in waves). Each worker gets exactly one frame and this context:

text
PROJECT_DIR: <abs path>
frame_id: <NN-frame_id>              # = the frame file stem, e.g. 02-f2; the composition id
Your block: the `## Frame N — <frame_id>` block in PROJECT_DIR/STORYBOARD.md
audiomap: PROJECT_DIR/audiomap.json
frame.md: PROJECT_DIR/frame.md
Materials: for each group, <SKILL_DIR>/references/templates/<id>/index.html (templates) and
           <SKILL_DIR>/references/motion-primitives/<id>/ (free); staged assets/ (asset groups)
Contracts: ../hyperframes-core/references/sub-compositions.md + determinism-rules.md
Canvas: <w>×<h>   Pacing: <beat_cut|phrase_flow>
Write to: PROJECT_DIR/compositions/frames/<frame_id>.html

The worker forks the cited materials, converts every anchor to frame-local seconds (local_t = track_t − span_sec[0]), gates its groups with 0ms cuts, and writes one seek-safe frame file. The worker never runs the hyperframes CLI — those commands operate on the assembled project, which doesn't exist yet, so they'd report on the wrong files. The worker just writes to the contract and stops; you verify after assembly (Step 6). As each worker returns, you can confirm its file landed on disk.

Gate: every frame has its compositions/frames/NN-*.html on disk.


Step 5: Assemble

Goal: Wire the built frames + BGM into the playable index.html.

assemble-index.mjs is deterministic — no subagent, no judgment. It references each frame file at its cumulative data-start, mounts assets/bgm.mp3 on track 11, and hard-cuts frame → frame (frames tile the track with no gaps, so there is no transition injector).

bash
node <SKILL_DIR>/scripts/assemble-index.mjs --storyboard "$PROJECT_DIR/STORYBOARD.md" \
  --hyperframes "$PROJECT_DIR" --audiomap "$PROJECT_DIR/audiomap.json"

Fix any ✗ it reports — a missing or blank frame file means that worker wrote a partial file; re-dispatch it (Step 4) and re-assemble.

Gate: index.html exists; total duration == audiomap.audio.duration_sec.


Step 6: Verify and render

Goal: Verify the assembled video, get user approval, and render the final MP4.

Run the CLI on the assembled project — that's the correct unit (the per-frame workers couldn't run it). check runs structural lint and the headless-browser runtime, layout, motion, and contrast gate in one pass; --snapshots also emits the review frames.

bash
( cd "$PROJECT_DIR" && npx hyperframes check . --snapshots )

Inspect at t=0, each frame start, the strongest DROP / SURGE, every hard_stops[].t, and the final frame. On failure, make the cheapest safe fix yourself: edit the offending compositions/frames/NN-*.html. Never change duration or audio timing to hide a sync issue. Once the gates pass, pause for user review, then render only on approval (autonomous mode: ask the one kept question — "preview first, or render?" — then deliver the MP4 with the contact sheet):

bash
( cd "$PROJECT_DIR" && npx hyperframes render . --skill=music-to-video -q draft -o renders/video.mp4 --fps 30 )

Gate: check passed and the snapshots were inspected; the user approved (autonomous: checks passed and the delivery includes the contact sheet); renders/video.mp4 exists with audio, duration == audiomap.audio.duration_sec. The final reply states the MP4 path and duration.


Resume table

You haveContinue from
assets/bgm.mp3 onlyStep 1
audiomap.jsonStep 2
STORYBOARD.md (skeleton)Step 3
STORYBOARD.md (complete)Step 4
all frame filesStep 5
index.htmlStep 6

Quick Reference

Formats: landscape 1920x1080 by default; portrait 1080x1920; square 1080x1080. Set the canvas once in the storyboard frontmatter (canvas: { w, h, fps }).

Scripts under scripts/: analyze-beatgrid.py (the one analyzer), validate-plan.mjs (plan check), assemble-index.mjs (index assembly), stage-assets.mjs (stage user media), lib/storyboard.mjs (vendored parser). Everything else is the hyperframes CLI.

ReadWhen
references/frame-skeleton.mdStep 2: read the music, lay out the frames, set pacing
references/planning.md · storyboard-format.mdStep 3: pick the brand, fill each frame, write the plan
references/template-catalog.mdStep 3: pick a template per group
references/motion-primitive-catalog.mdStep 3/4: L0 recipes for free-compose
references/montage.mdStep 3/4: asset treatments (beat-cut / ken-burns)
sub-agents/frame-worker.mdStep 4: dispatch + build one frame
../hyperframes/references/subagent-dispatch.mdStep 4: dispatch sub-agents safely
../hyperframes-creative/references/design-spec.mdStep 3: pick the preset (the brand)

Directory layout

music-to-video/
  SKILL.md
  references/   frame-skeleton.md · planning.md · storyboard-format.md
                template-catalog.md · motion-primitive-catalog.md · montage.md
                templates/<id>/          { index.html (+ assets/ · program.json) }  ← L1 catalog impls
                motion-primitives/<id>/  { index.html (mounts the scene), scene.html (the sub-composition) } (+ ../assets/gsap.min.js shared by recipes) ← L0 catalog impls
  scripts/      analyze-beatgrid.py · assemble-index.mjs · validate-plan.mjs · stage-assets.mjs · lib/storyboard.mjs
  sub-agents/   frame-worker.md   ← the one subagent (one per frame)

Bundled files

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

and 108 more files.

Frequently asked questions

What does the Music To Video AI skill do?

Turn a music track (an audio file, a video to pull audio from, or a track generated from a mood brief) into a beat-synced video — lyric video, slideshow, or kinetic promo. The music drives all pacing; any user-supplied images/videos are cut onto the same beat grid, and a complete video needs zero assets. Narrated pieces → the input-matched workflow (see /hyperframes). Unclear → /hyperframes.

Why use Music To Video on TypingMind?

Because you install it once and use it with any model. Music To Video 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 Music To Video in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/heygen-com/hyperframes/tree/main/skills/music-to-video. 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 Music To Video?

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 Music To Video?

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

Is the Music To Video 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.

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