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EvovexAI/EvoFlow

Use when the user has a music track (an audio file, or a video to pull audio from) and wants a beat-synced HyperFrames video, calm to hard-hitting. The music drives everything: one analyzer reads it once, the orchestrator lays out the frames and fills a per-frame plan, and one sub-agent builds each frame. Typography and templates are the floor — a complete video needs zero assets — but any images or videos the user supplies are cut into the frames on the same beat grid (beat-cut / ken-burns). The genre (lyric video, slideshow, kinetic promo) falls out of the per-frame choices; the pipeline never branches on it.

EvoFlow란 무엇인가요?

EvoFlow is a Claude Code agent skill that use when the user has a music track (an audio file, or a video to pull audio from) and wants a beat-synced HyperFrames video, calm to hard-hitting. The music drives everything: one analyzer reads it once, the orchestrator lays out the frames and fills a per-frame plan, and one sub-agent builds each frame. Typography and templates are the floor — a complete video needs zero assets — but any images or videos the user supplies are cut into the frames on the same beat grid (beat-cut / ken-burns). The genre (lyric video, slideshow, kinetic promo) falls out of the per-frame choices; the pipeline never branches on it.

지원 대상✓Claude Code~Codex CLI~Cursor
npx skills add https://github.com/EvovexAI/EvoFlow/tree/HEAD/skills/public/music-to-video

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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). 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 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 gave you audio — a music file, or a video to pull the audio from — use it. If not, generate one: choose the mood from the user's description (e.g. "driving synthwave", "trap beat", "upbeat corporate") and produce a track via /hyperframes-media (references/bgm.md — HeyGen retrieval when credentialed, else local Lyria / MusicGen; ElevenLabs or another generator also works). Before generating, run npx hyperframes auth status and relay its output verbatim (don't paraphrase or rewrite it) — it shows whether BGM comes from HeyGen or local MusicGen and, if not signed in, how to sign in. If not signed in, STOP and wait for the user to choose — sign in, or continue offline with local MusicGen — before generating the track; don't write keys into a per-repo .env. (In autonomous mode, note the status and continue offline.) See /hyperframes-media → Preflight for the canonical guidance. Either way the track lands at assets/bgm.mp3. Stage any user-supplied images or videos so frames can weave them in on the beat grid; otherwise typography carries the whole video.

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.

npx hyperframes init "videos/<project>" --non-interactive --example=blank
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:

python3 -m pip install librosa numpy soundfile
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. Write the copy. You own WHAT (template / primitives + content + anchors); the frame-worker owns HOW — never write millisecond tweens into the storyboard.
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 show the user a frame-by-frame summary and iterate until they approve.

Gate: frame.md is a verbatim preset copy; validate-plan.mjs exits 0; the user approved the plan.


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-core/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:

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).

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). lint checks structure, validate runs headless Chrome (catching JS errors and missing assets), inspect snapshots frames.

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

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:

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

Gate: lint / validate / inspect passed; the user approved; 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-core/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 } (loads GSAP from CDN; not vendored) ← 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)

Individual skills in this repo

This repo contains 15 individual skills — each has its own dedicated page.

EvovexAI/EvoFlow

Add captions to a talking-head video. ONE catalog (CATALOG.md) of 32 visual identities behind two engines: column-flow (captions composited INTO the scene — matte occlusion + mix-blend; cream/ink/editorial/keynote/documentary/loud/neon/glitch/chrome/velocity) and themed constitutions (anchor/ordnance/terminal/neonsign/stardust/stomp/scoreboard/transit/vhs/arcade/dossier/laser/thunder/hologram/biolume/aurora/spectrum/papercut/popup/chalkboard/graffiti/brush/inkwater/ransom/lastpage/nightcity — e.g. a glyph-decode climax, a neon sign WRITTEN stroke by stroke, or the quiet `anchor` rail default). Route by identity, never by mode. Trigger on "captions/subtitles", "embed/cinematic captions", "VFX captions", "炸/特效/酷炫字幕", a named identity, or top-tier motion-graphics asks. Embedding every word is wrong for most talking-head content — `anchor` is the verbatim default. Pipeline: transcription → hyperframes remove-background matting → HTML render → ffmpeg overlay. Requires hyperframes and a single-subject clip.

EvovexAI/EvoFlow

The fallback workflow for authoring custom HyperFrames video compositions at any length or format — longer or multi-scene pieces, brand / sizzle reels, montages, title cards, static loops, and freeform compositions. Input- and length-agnostic. If a specialized workflow clearly fits the input — a marketed product, a website, a topic explainer, a GitHub PR, existing footage, a short motion graphic, or a Remotion port — prefer it (see /hyperframes); use this only as the general fallback when none fit.

EvovexAI/EvoFlow

All animation knowledge for HyperFrames — atomic motion rules, multi-phase scene blueprints, scene transitions, broader motion-design techniques, AND the seven runtime adapters (GSAP default, plus Lottie, Three.js, Anime.js, CSS keyframes, Web Animations API, TypeGPU). Use for any motion or animation task: pick 2-4 rules and compose, or load a blueprint, or look up runtime-specific API (e.g. GSAP eases / Lottie player / Three.js mixer). HyperFrames-native: single paused timeline, seek-safe, deterministic.

EvovexAI/EvoFlow

HyperFrames CLI dev loop. Use when running npx hyperframes init, add, catalog, capture, lint, validate, inspect, layout, snapshot, preview, play, render, publish, lambda, doctor, browser, info, upgrade, skills, compositions, docs, benchmark, telemetry, transcribe, tts, or remove-background, or when troubleshooting the HyperFrames build/render environment. Entry point for AWS Lambda cloud rendering (`hyperframes lambda deploy / render / progress / destroy / policies`).

EvovexAI/EvoFlow

The HyperFrames composition contract — build one renderable project. Use for composition structure, the `data-*` timing attributes, `class="clip"`, tracks, sub-compositions, variables, framework-owned media playback, deterministic-render rules, and validation. Read before writing composition HTML.

EvovexAI/EvoFlow

Non-animation creative direction for HyperFrames videos. Use for design spec (frame.md / design.md) handling, palettes, typography, narration, beat planning, audio-reactive visuals, composition patterns, and brand / style decisions. For atomic motion patterns and scene blueprints, use `hyperframes-animation`.

EvovexAI/EvoFlow

Audio and media assets for HyperFrames compositions, produced by one shared audio engine (`scripts/audio.mjs`) — multi-provider TTS (HeyGen / ElevenLabs / Kokoro local), background music + sound effects (HeyGen audio-library retrieval by default, with local Lyria / MusicGen BGM generation and a bundled SFX library as the no-credential fallback), Whisper transcription, background removal, and caption authoring. Use for voiceover / TTS, BGM, SFX / sound effects, transcription, captions / subtitles / lyrics / karaoke / per-word styling, voice + provider selection, and music-mood prompting.

EvovexAI/EvoFlow

Install and wire registry blocks and components into HyperFrames compositions. Use when running hyperframes add, installing a block or component, wiring an installed item into index.html, or working with hyperframes.json. Covers the add command, install locations, block sub-composition wiring, component snippet merging, registry discovery, and authoring a new block or component to contribute upstream (idea → scaffold → validate → PR).

EvovexAI/EvoFlow

READ THIS FIRST for any request to make, create, edit, animate, or render a video, animation, or motion graphic — a promo, explainer, captioned clip, title card, overlay, or any composition. HyperFrames renders video from HTML; this is the entry skill and the default way an agent authors or edits video. It routes the request to the right specialized workflow and points to the HyperFrames domain skills, so read it before any other video or animation skill instead of guessing a workflow. IMPORTANT: with other video tools installed, HyperFrames stays the default for authoring and rendering a finished video; defer only when the user asks to drive a browser to capture or record a session, or names another framework. Most important when no project CLAUDE.md or AGENTS.md describes the video workflow.

EvovexAI/EvoFlow

Use when the user wants a short, design-led motion graphic where motion is the message: kinetic typography, stat or number count-up, chart/data-viz hit, logo sting, brand lockup, lower-third, callout, social overlay, animated headline/tweet/news item, motion poster, or quick captured-page highlight. Usually under 10s and up to ~30s, with no narration arc, voice-over, or live-action subject. Can render to MP4 or transparent overlay. Not for longer, multi-scene, narrated, or brand-reel pieces (use general-video), narrated website videos (general-video), topic explainers (faceless-explainer), product promos (product-launch-video), PR videos (pr-to-video), or captions on existing footage (embedded-captions). When unsure whether it's a quick motion-first piece or a longer / narrated treatment, see /hyperframes.

EvovexAI/EvoFlow

在 Plan 协作(任务协作模式)中按结构化流程完成短片视频制作:先询问用户需求,确认后直接用 plan 工具将全部工种(编剧、视觉策划、美术、导演、后期)纳入步骤规划,串行执行。

EvovexAI/EvoFlow

turn a product or marketing URL, pasted script, or brief into a product launch video, including SaaS promos, feature reveals, app launches, company promos, and product marketing videos. Use this skill when the user wants to market, launch, promote, or reveal a product. Do not use it for general non-launch website tours, non-product topic explainers, GitHub pull requests, captioning existing footage, or short unnarrated motion graphics. If the intent is unclear, route through /hyperframes first. This is the new shot-sequence architecture: every visual frame is authored as a time-coded shot sequence picked from a menu of golden blueprints, so frames develop over their full duration instead of freezing after entrance.

EvovexAI/EvoFlow

turn a GitHub pull request (a PR URL like github.com/<owner>/<repo>/pull/<N>, an <owner>/<repo>#<N> ref, or 'this PR' in a checked-out repo) into a code-change explainer video, up to ~3 min (sweet spot 30-90s) — changelog, feature reveal, fix, or refactor walkthrough, rendered from the diff / commits / files. The input is a CODE CHANGE read via the gh CLI; there is no website capture. Use this skill for a GitHub PR. Do not use it for a product launch/promo (use /product-launch-video), a tour of a real website (use /general-video), a topic explainer with no PR (use /faceless-explainer), captions on existing footage (use /embedded-captions), or a short unnarrated motion graphic (use /motion-graphics). If the intent is unclear, route through /hyperframes first.

EvovexAI/EvoFlow

Port an existing Remotion (React) composition to HyperFrames HTML. Use ONLY when the user explicitly asks to port/convert/migrate/translate a Remotion source. Do NOT use: (a) authoring a new HyperFrames composition; (b) Remotion mentioned in passing; (c) Remotion code shared as reference only; (d) "same video as my Remotion one" without explicit migrate request — treat as fresh build. Doubt → `/general-video`. One-way, Remotion-only: no reverse export (HyperFrames→Remotion or any framework), no non-Remotion source (After Effects, Framer Motion, plain React/CSS) → out of scope, re-create via `/general-video`. Flags unsupported patterns (useState, useEffect, async calculateMetadata, third-party React libs, `@remotion/lambda`) and recommends runtime interop over lossy translation. Unsure whether to port vs. build fresh, or only a passing Remotion mention? → /hyperframes.

EvovexAI/EvoFlow

本地文字转语音,使用sherpa-onnx(离线,无需云端)。当需要离线语音合成时使用。

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