CLI

Create, preview, and render HTML video compositions from the command line.

The hyperframes CLI is the primary way to work with Hyperframes. It handles project creation, live preview, rendering, linting, and diagnostics — all from your terminal.

npm install -g hyperframes
# or use directly with npx
npx hyperframes <command>

When to Use

Use the CLI when you want to:

• Capture a website for video production (capture)
• Create a new composition project from an example (init)
• Preview compositions with live hot reload (preview)
• Render compositions to MP4 locally or in Docker (render)
• Lint compositions for structural issues (lint)
• Inspect rendered visual layout for text overflow, clipped containers, and overlapping text, plus verify motion intent against the seeked timeline (inspect)
• Capture key frames as PNG screenshots (snapshot)
• Check your environment for missing dependencies (doctor)

Use a different package if you want to:

• Render programmatically from Node.js code — use the producer
• Build a custom frame capture pipeline — use the engine
• Embed a composition editor in your own web app — use the studio
• Parse or generate composition HTML in code — use core

The CLI is the recommended starting point for all Hyperframes users. It wraps the producer, engine, and studio packages so you do not need to install them separately.

Agent-Friendly by Default

The CLI is agent-friendly by default: commands support explicit flags and parseable output so automation can run reliably.

• Inputs can be passed via flags (for example, --example, --video, --output)
• Missing required flags fail fast with a clear error and usage example
• Output is plain text suitable for parsing

Interactivity is command-specific. For example, init uses prompts on TTY by default; pass --non-interactive to force non-interactive mode.

--human-friendly is also command-specific (for example, catalog). It is not a global flag on every command.

Agent mode (default)

# Fully non-interactive — all inputs from flags
npx hyperframes init my-video --example blank --video video.mp4
npx hyperframes render --output output.mp4 --fps 30 --quality standard
npx hyperframes upgrade --check --json

Human mode

# Command-specific interactive flow
npx hyperframes init my-video

# Interactive picker supported by catalog
npx hyperframes catalog --human-friendly

JSON Output and _meta Envelope

All commands that support --json wrap their output with a _meta field containing version check info:

{
  "name": "my-video",
  "duration": 10.5,
  "_meta": {
    "version": "0.1.4",
    "latestVersion": "0.1.5",
    "updateAvailable": true
  }
}

This allows agents to detect outdated versions from any command’s output without running a separate upgrade check. The version data comes from a 24-hour cache — no network request is made during --json output.

Passive Update Notices

The CLI checks npm for newer versions in the background (cached 24 hours). If an update is available, a notice appears on stderr after command completion:

  Update available: 0.1.4 → 0.1.5
  Run: npx hyperframes@latest

This is suppressed in CI environments, non-TTY shells, and when HYPERFRAMES_NO_UPDATE_CHECK=1 is set.

Getting Started

1. Create a project

   Scaffold a new composition from an example:

   npx hyperframes init --example warm-grain
   

   You will be prompted for a project name, or pass it as an argument:

   npx hyperframes init my-video --example warm-grain
   

   See Examples for all available examples.

2. Preview in browser

   Start the development server with live hot reload:

   cd my-video
   npx hyperframes preview
   

   The Hyperframes Studio opens in your browser. Edit index.html and the preview updates instantly.

3. Lint your composition

   Check for structural issues before rendering:

   npx hyperframes lint
   

   ◆  Linting my-project/index.html
   
   ◇  0 errors, 0 warnings
   
4. Render to MP4

   Produce the final video:

   npx hyperframes render --output output.mp4
   

   Render a specific composition instead of index.html:

   npx hyperframes render -c compositions/intro.html -o intro.mp4
   

   For deterministic output, add --docker:

   npx hyperframes render --docker --output output.mp4
   

Commands

Create

init

Create a new composition project from an example:

# Agent mode (default) — --example is required
npx hyperframes init my-video --example blank --video video.mp4

# Include Tailwind CSS browser-runtime support
npx hyperframes init my-video --example blank --tailwind

# Human mode — interactive prompts on TTY by default
npx hyperframes init my-video

Flag	Description
--example, -e	Example to scaffold (required in default mode, interactive in --human-friendly)
--resolution	Canvas preset: landscape (1920×1080), portrait (1080×1920), landscape-4k (3840×2160), portrait-4k (2160×3840), square (1080×1080), square-4k (2160×2160). Aliases: 1080p, 4k, uhd, 1080p-square, square-1080p, 4k-square. Default: keep template dimensions.
--video, -V	Path to a video file (MP4, WebM, MOV)
--audio, -a	Path to an audio file (MP3, WAV, M4A)
--tailwind	Add Tailwind CSS browser-runtime support to scaffolded HTML
--skip-skills	Skip AI coding skills installation
--skip-transcribe	Skip automatic whisper transcription
--model	Whisper model for transcription (e.g. small.en, medium.en, large-v3)
--language	Language code for transcription (e.g. en, es, ja). Filters non-target speech.

Example	Description
blank	Empty composition — just the scaffolding
warm-grain	Cream aesthetic with grain texture
play-mode	Playful elastic animations
swiss-grid	Structured grid layout
vignelli	Bold typography with red accents

In non-interactive mode, --example is required — the CLI errors with a usage example if missing. In interactive mode (default on TTY), you choose the example interactively. Pass --non-interactive to require --example via flag. When --video or --audio is provided, the CLI automatically transcribes the audio with Whisper and patches captions into the composition (use --skip-transcribe to disable).

--tailwind injects the pinned Tailwind v4 browser runtime into scaffolded HTML and exposes a window.__tailwindReady promise that renders wait on before capturing frame 0. Use the /hyperframes-core skill when editing these projects so agents follow v4 CSS-first patterns instead of v3 tailwind.config.js and @tailwind directive patterns. The browser runtime is still intended for scaffolded projects and quick iteration; for fully offline or locked-down production renders, compile Tailwind to CSS and include the stylesheet directly.

After scaffolding, the CLI installs AI coding skills for Claude Code, Gemini CLI, and Codex CLI (use --skip-skills to disable). See skills command.

See Examples for full details.

add

Install a block or component from the registry into an existing project. Examples (full projects) are scaffolded with init; blocks and components are smaller units you add to a composition you already have.

# Add a block (sub-composition scene)
npx hyperframes add claude-code-window

# Add a component (effect / snippet)
npx hyperframes add shader-wipe

# Target a different project dir
npx hyperframes add shader-wipe --dir ./my-video

# Headless / CI (skip clipboard; also: --json for a machine-readable result)
npx hyperframes add shader-wipe --no-clipboard --json

Flag	Description
<name> (positional)	Registry item name (e.g. claude-code-window, shader-wipe)
--dir	Project directory (defaults to the current working directory)
--no-clipboard	Skip copying the include snippet to the clipboard
--json	Print a machine-readable summary (written files + snippet) to stdout

add reads hyperframes.json at the project root to know which registry to pull from and where to drop files. If the file is missing but the directory looks like a Hyperframes project (has index.html), a default hyperframes.json is written the first time you run add.

Output for a block or component is a set of files plus a paste snippet — the <iframe> tag (for blocks) or the fragment path (for components) to include in your host composition. The snippet is copied to the clipboard by default; add --no-clipboard for CI or headless environments.

Trying add with an example’s name (e.g. hyperframes add warm-grain) emits a clear error pointing you at init --example.

catalog

Browse the registry — list available blocks and components with optional filters:

# List everything (default: table output)
npx hyperframes catalog

# Filter by type or tag
npx hyperframes catalog --type block
npx hyperframes catalog --type block --tag social

# Machine-readable JSON
npx hyperframes catalog --json

# Interactive picker — select to install
npx hyperframes catalog --human-friendly

Flag	Description
--type	Filter by block or component
--tag	Filter by tag (e.g. social, transition, text)
--json	Print matching items as JSON (non-interactive)
--human-friendly	Interactive picker — select an item to install it

Default output is a table listing name, type, description, and tags — designed for agents to parse. --json produces structured output. --human-friendly opens an interactive picker that runs add on selection.

compositions

List all compositions in the current project:

npx hyperframes compositions

Flag	Description
--json	Output as JSON

Shows each composition’s ID, duration, resolution, and element count.

transcribe

Transcribe audio/video to word-level timestamps, or import an existing transcript:

# Transcribe audio/video with local whisper.cpp
npx hyperframes transcribe audio.mp3
npx hyperframes transcribe video.mp4 --model medium.en --language en

# Import existing transcripts from other tools
npx hyperframes transcribe subtitles.srt
npx hyperframes transcribe captions.vtt
npx hyperframes transcribe openai-response.json

# Export caption sidecars from transcript.json
npx hyperframes transcribe transcript.json --to srt

Flag	Description
--dir, -d	Project directory (default: current directory)
--model, -m	Whisper model (default: small.en). Options: tiny.en, base.en, small.en, medium.en, large-v3
--language, -l	Language code (e.g. en, es, ja). Filters out non-target language speech.
--to	Export transcript sidecar format: srt or vtt
--output, -o	Output path for exported SRT/VTT sidecar
--preserve-cues	Keep each transcript entry as its own caption cue (skip word-level grouping)
--timeout	Whisper spawn timeout in ms. Overrides the duration+model auto-scaled default. Increase on slow CPUs (e.g. emulated arm64/x64) where medium/large models take many seconds per audio second. Minimum 5000. Env: HYPERFRAMES_TRANSCRIBE_TIMEOUT_MS.
--json	Output result as JSON

The command auto-detects the input type. Audio/video files are transcribed with whisper.cpp. Transcript files (.json, .srt, .vtt) are normalized and imported. Pass --to srt or --to vtt with a transcript input to write a caption sidecar instead.

Supported transcript formats:

Format	Source
whisper.cpp JSON	hyperframes init --video, hyperframes transcribe
OpenAI Whisper API JSON	openai.audio.transcriptions.create() with word timestamps
SRT subtitles	Video editors, YouTube, subtitle tools
VTT subtitles	Web players, YouTube, transcription services

All formats are normalized to a standard [{text, start, end}] word array and saved as transcript.json. If the project has caption HTML files, they are automatically patched with the transcript data. Sidecar export reads the same normalized transcript and writes transcript.srt or transcript.vtt by default.

Word-level transcripts (whisper output) are grouped into readable caption cues on sentence boundaries. Exporting directly from an .srt/.vtt source keeps its cue boundaries unchanged. When exporting from a transcript.json whose entries are already finished cues with no internal spaces (single-word or CJK captions), pass --preserve-cues to keep them one cue per entry.

For music or noisy audio, use --model medium.en for better accuracy. For the best results with production content, transcribe via the OpenAI or Groq Whisper API and import the JSON.

tts

Generate speech audio from text using a local AI model (Kokoro-82M). No API key required — runs entirely on-device.

# Generate speech from text
npx hyperframes tts "Welcome to HyperFrames"

# Choose a voice
npx hyperframes tts "Hello world" --voice am_adam

# Save to a specific file
npx hyperframes tts "Intro" --voice bf_emma --output narration.wav

# Adjust speech speed
npx hyperframes tts "Slow and clear" --speed 0.8

# Generate Spanish speech (lang auto-detected from the `e` voice prefix)
npx hyperframes tts "La reunión empieza a las nueve" --voice ef_dora --output es.wav

# Override the phonemizer (read English text with a French voice)
npx hyperframes tts "Bonjour le monde" --voice af_heart --lang fr-fr

# Read text from a file
npx hyperframes tts script.txt

# List available voices
npx hyperframes tts --list

Flag	Description
--output, -o	Output file path (default: speech.wav in current directory)
--voice, -v	Voice ID (run --list to see options)
--speed, -s	Speech speed multiplier (default: 1.0)
--lang, -l	Phonemizer locale (en-us, en-gb, es, fr-fr, hi, it, pt-br, ja, zh). When omitted, inferred from the voice ID prefix.
--list	List available voices and exit
--json	Output result as JSON

Voice IDs encode the phonemizer language in their first letter (a=American, b=British, e=Spanish, f=French, h=Hindi, i=Italian, j=Japanese, p=Brazilian Portuguese, z=Mandarin). --lang is only needed when you want to override that — for example, giving English text a French phonemizer for a stylized accent.

Combine tts with transcribe to generate narration and word-level timestamps for captions in a single workflow: generate the audio with tts, then transcribe the output with transcribe to get word-level timing.

remove-background

Remove the background from a video or image using a local AI model. The output is transparent media you can drop into any composition’s <video> or <img> element — no green screen required.

# Default: VP9-with-alpha WebM (HTML5-native, ~1 MB / 4s @ 1080p)
npx hyperframes remove-background avatar.mp4 -o transparent.webm

# ProRes 4444 .mov for editing round-trip
npx hyperframes remove-background avatar.mp4 -o transparent.mov

# Single image → transparent PNG
npx hyperframes remove-background portrait.jpg -o cutout.png

# Layer separation: cutout AND inverse-alpha background plate in one pass
npx hyperframes remove-background avatar.mp4 \
  -o subject.webm --background-output plate.webm

# Force CPU on a machine that has CoreML or CUDA
npx hyperframes remove-background avatar.mp4 -o transparent.webm --device cpu

# Inspect detected providers without rendering
npx hyperframes remove-background --info

Flag	Description
--output, -o	Output path. Format inferred from extension: .webm (default), .mov, .png
--background-output, -b	Optional second output: inverse-alpha background plate (subject region transparent, surroundings opaque). Same source RGB, complementary mask. Must be .webm or .mov. Hole-cut, not inpainted — composite something underneath to fill the hole.
--device	Execution provider: auto (default), cpu, coreml, cuda
--quality	WebM encoder preset: fast (crf 30, smallest), balanced (crf 18, default), best (crf 12, near-lossless). Higher quality keeps the cutout’s RGB closer to the source mp4 — important when overlaying the cutout on its own source for text-behind-subject effects. Applies to both --output and --background-output. Ignored for .mov / .png.
--info	Print detected execution providers and exit (no render)
--json	Output result as JSON

The model is u2net_human_seg (MIT, ~168 MB ONNX). Weights download to ~/.cache/hyperframes/background-removal/models/ on first run and are reused thereafter. Peak inference RAM is ~1.5 GB.

--device auto picks CoreML on Apple Silicon, CUDA when available, and CPU otherwise. The CLI bundles the CPU build of onnxruntime-node; for CUDA, set HYPERFRAMES_CUDA=1 and provide a GPU-enabled onnxruntime-node build.

Output formats:

Format	Use case	Size (4s @ 1080p)
.webm (VP9 alpha)	Drop into <video> for HTML5-native transparent playback	~1 MB
.mov (ProRes 4444)	Editing round-trip in Premiere / Resolve / DaVinci	~50 MB
.png	Single-image cutout	varies

The <video> element in Chrome only respects the alpha plane when the WebM is encoded as yuva420p with the alpha_mode=1 metadata tag. The CLI sets both automatically — if you re-encode the output yourself, preserve those flags.

See the Remove Background guide for the full workflow — using transparent videos in compositions, performance per platform, limitations of u2net_human_seg, and free alternative tools when this model isn’t the right fit.

capture

Capture a website — extract screenshots, design tokens, fonts, assets, and animations for video production:

npx hyperframes capture https://stripe.com
npx hyperframes capture https://linear.app -o linear-capture
npx hyperframes capture https://example.com --json

◇  Captured Stripe | Financial Infrastructure → capture

  Screenshots: 12
  Assets: 45
  Sections: 15
  Fonts: sohne-var

Flag	Description
-o, --output	Output directory (default: ./capture; auto-suffixes to ./capture-2/, ./capture-3/, … if ./capture/ is taken)
--timeout	Page load timeout in ms (default: 120000)
--skip-assets	Skip downloading images and fonts
--max-screenshots	Maximum screenshot count (default: 24)
--json	Output structured JSON for programmatic use

The capture command extracts everything an AI agent needs to understand a website’s visual identity: viewport screenshots at every scroll depth, color palette (pixel-sampled + DOM computed), font files, images with semantic names, SVGs, Lottie animations, video previews, WebGL shaders, visible text, and page structure.

Output is a self-contained directory with a CLAUDE.md file that any AI agent can read to understand the captured site. Used by the /product-launch-video workflow as step 1 of the video production pipeline.

Set GEMINI_API_KEY in a .env file for AI-powered image descriptions via Gemini vision (~$0.001/image), or set OPENROUTER_API_KEY to use any vision model through OpenRouter instead (takes priority if both are set; override the model with HYPERFRAMES_OPENROUTER_MODEL). See the Website to Video guide for details.

Preview

preview

Start a live preview server with hot reload:

npx hyperframes preview [dir]
npx hyperframes preview --port 4567

Flag	Description
--port	Port to run the preview server on (default: 3002)

Opens your composition in the Hyperframes Studio with live preview. Edits to index.html and any referenced sub-compositions are reflected automatically. The preview uses the same Hyperframes runtime as production rendering, so what you see is what you get.

Visual output matches render exactly. Playback performance does not: preview plays in real time in your browser, so paint-heavy compositions (large images, stacked backdrop-filter layers, many shadowed elements) may stutter depending on your hardware. The rendered mp4 is always accurate regardless — render captures frames one at a time, so per-frame cost shows up as longer render duration, not dropped frames. See Performance for details.

The preview server runs in three modes, auto-detected:

1. Embedded mode (default for npx) — runs a standalone server with the studio bundled in the CLI. Zero extra dependencies.
2. Local studio mode — if @hyperframes/studio is installed in your project’s node_modules, spawns Vite with full HMR for faster iteration.
3. Monorepo mode — if running from the Hyperframes source repo, spawns the studio dev server directly.

publish

Upload the project and get back a stable hyperframes.dev URL:

npx hyperframes publish [dir]
npx hyperframes publish --yes

Flag	Description
--yes	Skip the confirmation prompt

publish zips the current project, uploads it to the HyperFrames publish backend, and prints a stable hyperframes.dev URL for that stored project.

The printed URL already includes the claim token, so opening it on hyperframes.dev lets the intended user claim the uploaded project and continue editing in the web app.

This flow does not keep a local preview server alive and does not open a tunnel. The published URL resolves to the persisted project stored by HeyGen, so it keeps working after the CLI process exits.

lint

Check a composition for common issues:

npx hyperframes lint [dir]
npx hyperframes lint [dir] --verbose   # include info-level findings
npx hyperframes lint [dir] --json      # machine-readable JSON output

◆  Linting my-project/index.html

  ✗ missing_gsap_script: Composition uses GSAP but no GSAP script is loaded.
  ⚠ unmuted-video [clip-1]: Video should have the 'muted' attribute for reliable autoplay.

◇  1 error(s), 1 warning(s)

By default only errors and warnings are printed. Info-level findings (e.g., external script dependency notices) are hidden to keep output clean for agents and CI. Use --verbose to include them.

Flag	Description
--json	Output findings as JSON (includes errorCount, warningCount, infoCount, and findings array)
--verbose	Include info-level findings in output (hidden by default)

Severity levels:

• Error (✗) — must fix before rendering (e.g., missing adapter library, invalid attributes)
• Warning (⚠) — likely issues that may cause unexpected behavior
• Info (ℹ) — informational notices, shown only with --verbose

The linter detects missing attributes, missing adapter libraries (GSAP, Lottie, Three.js), structural problems, and more. See Common Mistakes for details on each rule.

check

The browser verification gate: everything the old validate → inspect → snapshot loop did, in one command with one browser session:

npx hyperframes check [dir]
npx hyperframes check [dir] --json         # {ok, lint, runtime, layout, motion, contrast, snapshots}
npx hyperframes check [dir] --snapshots    # annotated overview frames + per-finding crops
npx hyperframes check [dir] --at 1.5,4,7.25
npx hyperframes check [dir] --strict       # exit non-zero on warnings too

check runs the linter first (browser skipped entirely on lint errors), then loads the bundled composition once and sweeps one seek grid running every audit per sample: runtime console errors and failed requests, layout defects (overflow, clipping, held overlaps, occlusion, coordinate-frame drift), *.motion.json sidecar assertions, and WCAG AA contrast.

Flag	Description
--json	Aggregated machine-readable envelope; every finding carries selector, data-* identity, source file, bbox, and sample time
--snapshots	Write overview frames (annotated with labeled finding boxes when there are errors) plus finding-NN-<code>.png crops
--samples / --at / --at-transitions	Control the seek grid (default 9 samples; --at-transitions adds tween boundaries)
--tolerance	Allowed overflow in px before reporting (default 2)
--timeout	Initial settle budget in ms (default 3000)
--no-contrast	Skip the WCAG audit while iterating
--strict	Exit non-zero on warnings too (default: only errors)
--caption-zone "<x0=..;y0=..;x1=..;y1=..>"	Opt-in band gate: flags content whose center sits inside the fractional band (optional severity, seek)
--frame-check	Opt-in media out-of-frame detection (img/svg/video/canvas)

Contrast failures are errors and include the sampled fg/bg colors, measured vs required ratio, and a suggested compliant color. Severity is persistence-aware: single-sample transients demote to info, held findings gate the exit code, and a frozen timeline on a 3s+ composition fails with sweep_static.

beats

Detect the beats in a composition’s music track and write them to a beat file the Studio uses to draw beat guides on the timeline:

npx hyperframes beats [dir]
npx hyperframes beats [dir] --json   # machine-readable JSON output

The command finds the music track (an <audio> element with data-timeline-role="music", or an id like music/bgm/soundtrack), runs the same detection the Studio uses inside a headless Chrome (identical decode + BPM analysis), and writes beats/<audio-path>.json:

{
  "version": 1,
  "audio": "music.wav",
  "beats": [{ "time": 2.027, "strength": 0.924 }]
}

Run it when authoring a composition so the beat file exists before the Studio is opened — the Studio loads this file as-is (it only auto-generates one when none exists). time is in seconds into the audio file; strength (0–1) is the beat’s relative loudness. Beats edited in the Studio (add/move/delete) persist back to the same file.

Flag	Description
--json	Output { ok, file, count, bpm } as JSON

Requires a local Chrome (the same one used by render; run npx hyperframes browser ensure if missing). Detection runs the same algorithm the Studio uses; results are near-identical (a different headless-Chrome audio sample rate can shift beat times by a frame or two).

inspect

Deprecated: use check — it covers this layout sweep plus runtime, motion, and contrast in one browser session. inspect keeps working and marks _meta.deprecated: true in JSON output.

Inspect rendered visual layout across the composition timeline:

npx hyperframes inspect [dir]
npx hyperframes inspect [dir] --json
npx hyperframes inspect [dir] --samples 15
npx hyperframes inspect [dir] --at 1.5,4,7.25

◆  Inspecting layout for my-project (9 timeline samples)

  ✗ text_box_overflow t=3.25s #headline inside .bubble overflowed right 18px — "Quarterly plan"
    Fix: Text is 418px x 42px inside 400px x 120px and overflows by up to 18px; widen the container to at least ~418px, or allow wrapping with max-width/fitTextFontSize.

◇  1 error(s), 0 warning(s), 0 info(s)

inspect bundles the project, serves it locally, opens headless Chrome, seeks through the composition, and reports text or elements that escape their intended boxes, plus pairs of text blocks that overlap each other (content_overlap) and text that is hidden beneath an opaque element (text_occluded). It is designed for agent workflows: each finding includes a schema version, timestamp or collapsed timestamp range, selector, nearest container selector, measured bounding boxes, overflow sides, and a fix hint.

Flag	Description
--json	Output agent-readable findings with schemaVersion, samples, issues, bounding boxes, and summary counts
--samples	Number of midpoint samples across the composition duration (default: 9)
--at	Comma-separated timestamps in seconds for explicit hero-frame checks
--tolerance	Allowed pixel overflow before reporting an issue (default: 2)
--timeout	Ms to wait for runtime initialization (default: 5000)
--collapse-static	Collapse repeated static issues across samples (default: true)
--max-issues	Maximum findings to print or return after static collapse (default: 80)
--strict	Exit non-zero on warnings as well as errors

Use data-layout-allow-overflow on an element or ancestor when overflow is intentional, such as a planned off-canvas entrance. Use data-layout-ignore for decorative elements that should not be audited. Use data-layout-allow-overlap on a text element that is intentionally stacked over another (for example a lower-third caption above a heading). Use data-layout-allow-occlusion when text is intentionally layered beneath another element (for example a caption behind a foreground prop).

layout remains available as a compatibility alias for the same visual inspection pass:

npx hyperframes layout [dir] --json

Motion verification

inspect also checks motion intent against the same seeked timeline the renderer uses — catching the render-≠-preview bugs that layout sampling can’t, like an entrance reveal the seek skips, a broken stagger order, an element that drifts off-frame mid-tween, or a shot that freezes. Drop a *.motion.json sidecar next to the composition and inspect evaluates it automatically (no flag, no authoring changes); without a sidecar, inspect behaves exactly as before.

{
  "duration": 6,
  "assertions": [
    { "kind": "appearsBy", "selector": "#headline", "bySec": 0.5 },
    { "kind": "before", "a": "#headline", "b": "#cta" },
    { "kind": "staysInFrame", "selector": ".card" },
    { "kind": "keepsMoving", "withinSelector": ".scene" }
  ]
}

Assertion	Checks
appearsBy(selector, bySec)	the element is visible (opacity ≥ 0.5) no later than bySec — catches reveals the seek lands past (motion_appears_late)
before(a, b)	a first appears strictly before b — catches broken stagger order (motion_out_of_order)
staysInFrame(selector)	once visible, the element’s box never leaves the canvas — catches off-frame drift (motion_off_frame)
keepsMoving(withinSelector?)	no fully-static window longer than maxStaticSec (default 2s) — catches frozen shots (motion_frozen)

duration, keepsMoving.withinSelector, and keepsMoving.maxStaticSec are optional. Findings are reported in the same shape and JSON envelope as layout findings, are errors by default (a failed assertion fails the run), and a selector that matches nothing is reported as motion_selector_missing rather than silently passing.

snapshot

Capture key frames from a composition as PNG screenshots — verify visual output without a full render:

npx hyperframes snapshot my-project --at 2.9,10.4,18.7
npx hyperframes snapshot my-project --frames 10

◆  Capturing 3 frames at [2.9s, 10.4s, 18.7s] from my-project

◇  3 snapshots saved to snapshots/
   snapshots/frame-00-at-2.9s.png
   snapshots/frame-01-at-10.4s.png
   snapshots/frame-02-at-18.7s.png

Flag	Description
--frames	Number of evenly-spaced frames to capture (default: 5)
--at	Comma-separated timestamps in seconds (e.g., 3.0,10.5,18.0)
--timeout	Ms to wait for runtime to initialize (default: 5000)

The snapshot command bundles the project, serves it locally, launches headless Chrome, seeks to each timestamp, and captures a 1920×1080 PNG. Useful for visual verification during the build step of the website-to-video workflow.

Build

render

Render a composition to MP4 or WebM:

# Local mode (fast iteration)
npx hyperframes render --output output.mp4

# Docker mode (deterministic output)
npx hyperframes render --docker --output output.mp4

# WebM with transparency (for overlays, captions, lower thirds)
npx hyperframes render --format webm --output overlay.webm

# With options
npx hyperframes render --output output.mp4 --fps 60 --quality high

# Opt out of local browser GPU capture
npx hyperframes render --no-browser-gpu --output cpu-browser.mp4

# Add hardware FFmpeg encoding
npx hyperframes render --gpu --output gpu.mp4

Flag	Values	Default	Description
--output	path	renders/<name>.mp4	Output file path
--composition, -c	path	index.html	Render a specific composition file instead of index.html
--format	mp4, webm, mov, gif, png-sequence	mp4	Output format (WebM/MOV render with transparency; gif for inline embeds; png-sequence writes a directory of RGBA PNGs)
--fps	1-240 or rational (e.g. 30000/1001)	30	Frames per second
--gif-loop	0-65535	0	GIF loop count (0 = loop forever). Only applies with --format gif
--quality	draft, standard, high	standard	Encoding quality preset (drives CRF/bitrate)
--crf	0-51	—	Override encoder CRF (lower = higher quality). Mutually exclusive with --video-bitrate
--video-bitrate	e.g. 10M, 5000k	—	Target video bitrate. Mutually exclusive with --crf
--video-frame-format	auto, jpg, png	auto	Source video frame extraction format. Use png for UI recordings, screen captures, and color-sensitive source videos
--resolution	landscape, portrait, landscape-4k, portrait-4k, square, square-4k (aliases: 1080p, 4k, uhd, 1080p-square, square-1080p, 4k-square)	—	Output resolution preset. Supersamples a smaller composition via Chrome deviceScaleFactor so the screenshot lands at the requested dimensions. Aspect ratio must match the composition; the scale must be an integer multiple. Not supported with --hdr. See 4K Rendering
--hdr	—	off	Force HDR output even if no HDR sources are detected. MP4 only. See HDR Rendering
--sdr	—	off	Force SDR output even if HDR sources are detected
--workers	1-24 or auto	auto	Parallel render workers (auto = CPU cores minus 2)
--low-memory-mode / --no-low-memory-mode	—	auto (≤ 8 GB RAM)	Force the low-memory safe render profile on or off. Safe mode pins to 1 worker, uses screenshot capture, and skips auto-worker calibration so the pipeline doesn’t launch multiple concurrent Chrome instances on constrained machines. Auto-detection reads host RAM (os.totalmem()), not cgroup/container limits — containerised or serverless callers (incl. --docker) should set PRODUCER_LOW_MEMORY_MODE explicitly. Env fallback PRODUCER_LOW_MEMORY_MODE.
--gpu	—	off	GPU encoding (NVENC, VideoToolbox, AMF, VAAPI, QSV)
--browser-gpu / --no-browser-gpu	—	on locally, off in Docker	Use or opt out of host GPU acceleration for local Chrome/WebGL capture
--docker	—	off	Use Docker for deterministic rendering
--quiet	—	off	Suppress verbose output
--variables	JSON object	—	Variable overrides merged over data-composition-variables defaults. Read via window.__hyperframes.getVariables()
--variables-file	path	—	Path to a JSON file with variable overrides (alternative to --variables)
--strict-variables	—	off	Fail render if any --variables key is undeclared or has a wrong type vs the composition’s data-composition-variables. Without this flag, mismatches print as warnings and the render continues.
--browser-timeout	seconds (0.001–86400)	60	Puppeteer page-navigation timeout for the entry HTML. Increase when heavy compositions (many videos, fonts, or asset requests) cannot reach domcontentloaded within the default 60 s. The flag takes seconds; the env fallback PRODUCER_PAGE_NAVIGATION_TIMEOUT_MS takes milliseconds. This controls page.goto only — very heavy compositions may also need PRODUCER_PUPPETEER_PROTOCOL_TIMEOUT_MS and/or PRODUCER_PLAYER_READY_TIMEOUT_MS bumped (post-navigation window.__hf readiness has its own 45 s budget).
--protocol-timeout	milliseconds (≥ 1000)	300000 (5 min)	Puppeteer CDP protocol timeout — the per-call budget for Runtime.callFunctionOn seek/paint, Page.captureScreenshot, and other CDP round-trips. Raise on RAM-pressured hosts (≤ 8 GB), heavy-asset compositions (many videos + images), or when the render fails with Runtime.callFunctionOn timed out / Target closed. The default is auto-scaled per composition by output pixel area (a 4K comp bumps the ceiling proportionally, capped at 30 min); an explicit override sets the floor and disables scaling below it. Env fallback PRODUCER_PUPPETEER_PROTOCOL_TIMEOUT_MS (also milliseconds).

CRF and target bitrate default to the --quality preset. Use --crf or --video-bitrate for fine-grained overrides; RenderConfig.crf and RenderConfig.videoBitrate accept the same overrides programmatically. Use --video-frame-format png when source videos are UI recordings, screen captures, or other color-sensitive clips that should avoid JPEG frame extraction.

Parametrized renders

Render the same composition with different content by declaring variables on the composition root and overriding them at render time:

index.html

<html
  data-composition-id="root"
  data-composition-variables='[
    {"id":"title","label":"Title","type":"string","default":"Hello"},
    {"id":"theme","label":"Theme","type":"enum","options":[
      {"value":"light","label":"Light"},
      {"value":"dark","label":"Dark"}
    ],"default":"light"}
  ]'>
  <body>
    <h1 id="hero" class="clip" data-start="0" data-duration="3"></h1>
    <script>
      const vars = window.__hyperframes.getVariables();
      document.getElementById("hero").textContent = vars.title;
      document.body.dataset.theme = vars.theme;
    </script>
  </body>
</html>

# Render with declared defaults (preview also uses the defaults)
npx hyperframes render --output default.mp4

# Override at render time — missing keys fall through to declared defaults
npx hyperframes render --variables '{"title":"Q4 Report","theme":"dark"}' --output q4.mp4

# Pass values from a JSON file
npx hyperframes render --variables-file ./vars.json --output out.mp4

getVariables() returns the merged result of declared defaults and any --variables overrides, so the same composition runs unchanged in dev preview and in production renders.

WebM with Transparency

Use --format webm to render compositions with a transparent background. This produces VP9 video with alpha channel in a WebM container — the standard format for overlayable video.

# Render a caption overlay with transparent background
npx hyperframes render --format webm --output captions.webm

# Overlay on another video with FFmpeg
ffmpeg -c:v libvpx-vp9 -i captions.webm -i background.mp4 \
  -filter_complex "[1:v][0:v]overlay=0:0" -y composited.mp4

For transparency to work, your composition’s HTML should use background: transparent on the root elements. WebM renders use PNG frame capture (instead of JPEG) to preserve the alpha channel.

See Rendering for all options and modes.

benchmark

Find optimal render settings for your system:

npx hyperframes benchmark [dir]

Flag	Values	Default	Description
--runs	1-20	3	Number of runs per configuration
--json	—	off	Output results as JSON

Runs multiple render configurations (varying fps, quality, and worker count) and compares timing and file size for each.

Utilities

doctor

Check your environment for required dependencies:

npx hyperframes doctor

hyperframes doctor

  ✓ Version          0.1.4 (latest)
  ✓ Node.js          v22.x (linux x64)
  ✓ FFmpeg            7.x
  ✓ FFprobe           7.x
  ✓ Chrome            (system or cached)
  ✓ Docker            24.x
  ✓ Docker running    Running

  ◇  All checks passed

Flag	Description
--json	Output as JSON (includes _meta envelope)

Verifies CLI version, Node.js, FFmpeg, FFprobe, Chrome, and Docker availability. If a newer CLI version is available, the version row shows an upgrade hint.

CI gating. hyperframes doctor --json always exits 0 on successful execution — the command succeeded if it produced valid output. Whether the environment is healthy is carried in the ok field of the payload, so a new CLI release (which flips Version.ok to false) never breaks your pipeline. Pipe through jq to gate on the payload instead:

hyperframes doctor --json | jq -e '.ok' > /dev/null || handle_failure

Paths in detail and hint are redacted in JSON mode — the user’s home directory is replaced with the literal $HOME so output is safe to paste into bug reports and agent contexts.

info

Display project metadata:

npx hyperframes info [dir]

Flag	Description
--json	Output as JSON

Shows project name, resolution, duration, element counts by type, track count, and total project size.

upgrade

Check for updates and show upgrade instructions:

npx hyperframes upgrade
npx hyperframes upgrade --check         # check and exit (no prompt)
npx hyperframes upgrade --check --json  # machine-readable for agents
npx hyperframes upgrade --yes           # show upgrade commands without prompting

Flag	Description
--check	Check for updates and exit (no prompt, agent-friendly)
--json	Output as JSON (includes _meta envelope)
--yes, -y	Show upgrade commands without prompting

Compares your installed version against the latest on npm. With --check --json, returns:

{
  "current": "0.1.4",
  "latest": "0.1.5",
  "updateAvailable": true,
  "_meta": { "version": "0.1.4", "latestVersion": "0.1.5", "updateAvailable": true }
}

browser

Manage the Chrome browser used for rendering:

# Find or download Chrome for rendering
npx hyperframes browser ensure

# Print the browser executable path (for scripting)
npx hyperframes browser path

# Remove cached Chrome download
npx hyperframes browser clear

The path subcommand outputs only the path, useful in scripts: $(npx hyperframes browser path).

docs

View inline documentation in the terminal:

npx hyperframes docs [topic]

Available topics: data-attributes, examples, rendering, gsap, troubleshooting, compositions. Run without a topic to see the full list.

feedback

Submit anonymous recommendation feedback about your experience:

# Quick rating (0 = not likely, 10 = extremely likely)
npx hyperframes feedback --rating 10

# Rating with optional details
npx hyperframes feedback --rating 7 --comment "render succeeded but GSAP timeline didn't animate"

# Also file a pre-filled GitHub issue with a published minimal repro (opt-in, consented)
npx hyperframes feedback --rating 3 --comment "GSAP timeline froze on seek" --file-issue

Flag	Description
--rating	Recommendation score, 0–10 (required)
--comment	Optional free-text details
--file-issue	Also open a pre-filled GitHub issue with a published minimal repro (opt-in)
--dir	Project directory to publish as the repro (default: current directory)
--yes	Skip the publish + file-issue consent prompt (for scripts)

With --file-issue, the CLI publishes a minimal repro to a public URL (with consent) and opens a pre-filled bug issue draft you review and submit yourself (no token or backend). See Feedback Collection.

This command is also available to AI agents after a render — see Feedback Collection for how agent detection and the automatic post-render hint work.

No-op when telemetry is disabled — prints Telemetry is disabled. Feedback not sent. and exits cleanly.

telemetry

Manage anonymous usage telemetry:

npx hyperframes telemetry enable
npx hyperframes telemetry disable
npx hyperframes telemetry status

Telemetry collects command names, render performance, render checkpoint/error names, aggregate browser diagnostic counts, browser initialization duration and tween count, aggregate video extraction workload counts (such as extracted frame count and VFR preflight count), example choices, and system info — including a coarse environment fingerprint (OS, kernel string, CPU/memory shape, sandbox runtime such as gVisor or Docker, and the name of a coding agent driving the CLI when one is detected, e.g. claude_code / codex / cursor). The agent name is derived from the existence of well-known environment variables; their values are never read. Telemetry redacts local paths and URL query strings from render error/checkpoint messages and does not collect project names, video content, or environment variable values. It collects no personally identifiable information until you sign in: when you authenticate with hyperframes auth login, your HeyGen account email (or your username, if your account has no email) is linked to your usage so CLI activity can be associated with your account (and your prior anonymous usage is stitched to it). Nothing else personal is collected, and this only happens after you choose to sign in. Disable all telemetry with HYPERFRAMES_NO_TELEMETRY=1 or the command above.

See Feedback Collection for how the periodic post-render prompt and Studio feedback bar work, what data they collect, and how to opt out.

skills

Install HyperFrames skills for AI coding tools, including first-party runtime adapter skills:

# Install to all default targets (Claude Code, Gemini CLI, Codex CLI)
npx hyperframes skills

# Install to specific tools
npx hyperframes skills --claude
npx hyperframes skills --cursor
npx hyperframes skills --claude --gemini

Flag	Description
--claude	Install to Claude Code (~/.claude/skills/)
--gemini	Install to Gemini CLI (~/.gemini/skills/)
--codex	Install to Codex CLI (~/.codex/skills/)
--cursor	Install to Cursor (.cursor/skills/ in current project)

Skills are fetched from GitHub: the /hyperframes entry skill (routes “make me a video” to a workflow), the composition contract and Tailwind v4 browser-runtime guidance (/hyperframes-core), all animation including the GSAP / Anime.js / CSS / Lottie / Three.js / WAAPI / TypeGPU runtime adapters (/hyperframes-animation), creative direction (/hyperframes-creative), media preprocessing (/media-use), registry block/component wiring (/hyperframes-registry), and the video workflows. The init command also offers to install skills automatically after scaffolding a project.

Troubleshooting: fatal: active post-checkout hook found during git clone

If you installed Git LFS globally (git lfs install), Git 2.45+ refuses to run the LFS post-checkout hook during any git clone — including the clone the upstream skills CLI performs under the hood. The error looks like:

■  Failed to clone repository
fatal: active `post-checkout` hook found during `git clone`
└  Installation failed

Using hyperframes skills is already fine — as of v0.4.5 the CLI sets GIT_CLONE_PROTECTION_ACTIVE=0 on the child environment, which is the opt-in knob Git provides for exactly this case. You don’t need to do anything.

If you ran npx skills add heygen-com/hyperframes directly (bypassing the HyperFrames CLI), set the env var yourself:

GIT_CLONE_PROTECTION_ACTIVE=0 npx skills add heygen-com/hyperframes

This is tracked in GH #316. An upstream fix in the skills CLI itself is the right long-term answer; until that lands, the env var is the correct workaround.

hyperframes auth

Sign in to HeyGen and manage credentials. Credentials are stored in ~/.heygen/credentials (mode 0600) and are shared with the heygen CLI — sign in with one and the other picks up the session.

Resolution order (first match wins):

1. HEYGEN_API_KEY environment variable
2. HYPERFRAMES_API_KEY environment variable (hyperframes alias)
3. ~/.heygen/credentials

Subcommands

auth login --api-key

Save a HeyGen API key. The key is verified against GET /v3/users/me before the command reports success; a rejected key is not left on disk.

# Interactive hidden-input prompt
hyperframes auth login --api-key

# Pipe a key from stdin (CI-friendly)
echo "$HEYGEN_API_KEY" | hyperframes auth login --api-key

auth status

Show the active credential’s source, type, and verified identity (account + billing snapshot). Exits non-zero when nothing is configured or the API rejects the credential, so scripts can check sign-in state.

hyperframes auth status
hyperframes auth status --json   # machine-readable

auth logout

Remove the stored credential. Prompts for confirmation on a TTY.

hyperframes auth logout
hyperframes auth logout --keep-api-key   # clear only an OAuth session
hyperframes auth logout --yes            # skip the confirmation prompt

Environment variables

Variable	Description
HEYGEN_API_KEY	Override the stored credential.
HYPERFRAMES_API_KEY	Alias for HEYGEN_API_KEY.
HEYGEN_API_URL	API base URL (default https://api.heygen.com).
HEYGEN_CONFIG_DIR	Credentials directory (default ~/.heygen).

For the keys other capabilities use — ElevenLabs and Gemini for voice/music fallback, OpenRouter/Gemini for capture — and how the skills prioritize them, see Authentication & API keys.

hyperframes cloud

Render a HyperFrames composition on HeyGen’s hosted cloud — no local Chrome, no local ffmpeg, no AWS to manage. Sign in once with hyperframes auth login and the same credential drives every cloud subcommand.

hyperframes auth login                          # one-time
hyperframes cloud render ./my-video             # zip + upload + poll + download
hyperframes cloud render ./my-video --no-wait   # submit and exit with the render_id
hyperframes cloud list                          # browse recent renders

Subcommands

cloud render [<projectDir>]

End-to-end render: zips the project (excluding .git, node_modules, dist, .next, coverage, dotfiles), uploads it via POST /v3/assets, submits POST /v3/hyperframes/renders, polls GET /v3/hyperframes/renders/{id} until the render completes or fails, and streams the resulting video to disk.

Render parameters mirror the local hyperframes render UX where they overlap:

Flag	Default	Meaning
--fps	30	Integer 1-240.
--quality	standard	draft, standard, or high.
--format	mp4	mp4, webm, or mov.
--resolution	1080p	1080p or 4k. 4k is billed at 1.5× and can’t be combined with webm/mov.
--aspect-ratio	auto	16:9, 9:16, or 1:1. Auto-detected from a local project’s data-width/data-height; for --asset-id/--url it defaults to 16:9 unless set.
--composition / -c	index.html	Entry HTML file inside the zip.
--variables	—	Inline JSON object overriding data-composition-variables.
--variables-file	—	Path to a JSON file (alternative to --variables).
--strict-variables	off	Fail when variables are undeclared or have the wrong type.
--title	—	Free-text label echoed back in detail responses.
--output / -o	renders/<render_id>.<ext>	Local destination for the downloaded video.

Lifecycle / control flags:

Flag	Meaning
--no-wait	Submit and exit immediately; print the render_id to stdout.
--callback-url	HTTPS webhook fired when the render terminates (compose with --no-wait).
--callback-id	Opaque tracking ID echoed in webhook payloads.
--asset-id	Skip zip+upload; submit an already-uploaded composition. Mutually exclusive with the project dir and --url.
--url	Submit a public HTTPS zip URL. Same mutual-exclusion as --asset-id.
--poll-interval	Poll cadence in seconds (default 10).
--max-wait	Max poll duration in minutes (default 60).
--idempotency-key	Optional Idempotency-Key for safe retries (1-255 chars from [A-Za-z0-9_:.-]).
--json	Emit machine-readable JSON instead of human-friendly progress.

# Default flow — render the current directory.
hyperframes cloud render

# Pick a composition + output path.
hyperframes cloud render . \
  --composition compositions/intro.html \
  --output ./renders/intro.mp4

# Higher quality at 60fps.
hyperframes cloud render --quality high --fps 60

# Fire-and-forget with a webhook (no local polling).
hyperframes cloud render --callback-url https://example.com/hf-hook --no-wait

# Re-render an already-uploaded composition (skips zip + upload).
hyperframes cloud render --asset-id asst_abc123

# Render from a public URL (no upload).
hyperframes cloud render --url https://cdn.example.com/site.zip

Safe retries via --idempotency-key

The CLI transparently retries on a 401 Unauthorized by force-refreshing the OAuth token and replaying the failed request. For most reads that’s harmless, but POST /v3/assets (the zip upload) is not idempotent on its own — a retry without an Idempotency-Key would create a duplicate asset and bill the workspace twice.

Pass --idempotency-key <key> whenever you want safe retries on cloud render. The key is forwarded to both the upload and submit calls; the server scopes idempotency per-endpoint, so reusing the same value across the two steps is safe and prevents duplicates on either step. Use a UUID per logical render, or any opaque string in [A-Za-z0-9_:.-] (1-255 chars).

hyperframes cloud render . --idempotency-key "$(uuidgen)"

cloud list

Pages through recent renders. Cursor-based: --limit caps a single page (1-100), --token resumes from a previous next_token, --all walks the full list until exhausted.

hyperframes cloud list
hyperframes cloud list --limit 50 --json
hyperframes cloud list --all

cloud get <render_id>

Fetches the full detail record for one render, including the short-lived signed video_url and thumbnail_url (presigned S3 URLs — re-fetch on demand rather than cache).

hyperframes cloud get hfr_abc123
hyperframes cloud get hfr_abc123 --json

cloud delete <render_id>

Soft-deletes a render. Subsequent GET calls return 404 and the signed video URL stops working shortly after. Prompts for confirmation interactively; pass --no-confirm to bypass for scripts.

hyperframes cloud delete hfr_abc123
hyperframes cloud delete hfr_abc123 --no-confirm --json

When to pick cloud vs lambda vs local render

• hyperframes render (local): fastest iteration loop. Use during composition authoring.
• hyperframes lambda render: bring-your-own-AWS distributed rendering. Use when you’ve already invested in AWS and want chunked parallelism on your own account.
• hyperframes cloud render: zero-infra option. HeyGen runs the render; you pay per credit. Use when you don’t want to manage Chrome/ffmpeg/AWS locally.

Auth + base URL

cloud reuses the credential resolved by hyperframes auth status. Override the API base for staging tests with HEYGEN_API_URL (default https://api.heygen.com).

hyperframes lambda

Deploy HyperFrames distributed rendering to AWS Lambda and drive renders from your laptop or CI.

The hyperframes lambda command group wraps the @hyperframes/aws-lambda SDK plus AWS SAM so an end-to-end render is three commands:

hyperframes lambda deploy
hyperframes lambda render ./my-project --width 1920 --height 1080 --wait
hyperframes lambda destroy   # when you're done

Prerequisites

• AWS credentials configured (env vars, ~/.aws/credentials, SSO, or IMDS).
• AWS SAM CLI on PATH.
• bun on PATH (used to build the Lambda handler ZIP).

Subcommands

lambda deploy

Builds packages/aws-lambda/dist/handler.zip and SAM-deploys the stack at examples/aws-lambda/template.yaml. On success, writes <cwd>/.hyperframes/lambda-stack-<stackName>.json so the other subcommands don’t need to re-derive the bucket / state-machine ARN.

hyperframes lambda deploy \
  --stack-name=hyperframes-prod \
  --region=us-east-1 \
  --concurrency=8 \
  --memory=10240

Idempotent — re-running on the same --stack-name resolves to a no-op when nothing changed.

lambda sites create <projectDir>

Tars + uploads <projectDir> to S3 with a content-addressed key. Returns a siteId you can reuse across multiple renders so a re-render of the same tree skips the upload.

hyperframes lambda sites create ./my-project
# → siteId: abc1234deadbeef0  (stable across re-runs of the same tree)

hyperframes lambda render ./my-project --site-id=abc1234deadbeef0 --width 1920 --height 1080

lambda render <projectDir>

Starts a Step Functions execution. Returns immediately with a renderId (use lambda progress to poll) unless --wait is set, in which case the CLI blocks until the render finishes and streams per-chunk progress lines.

hyperframes lambda render ./my-project \
  --width=1920 --height=1080 --fps=30 --format=mp4 \
  --chunk-size=240 --max-parallel-chunks=16 \
  --wait

--json swaps the human-readable output for a machine-parseable JSON snapshot.

The composition can be parameterised with --variables / --variables-file, mirroring the local hyperframes render flags. Variables flow into the Step Functions execution input and reach every chunk worker as window.__hfVariables. Mismatches against the composition’s data-composition-variables declaration print as warnings; pass --strict-variables to fail the command instead.

hyperframes lambda render ./my-template --site-id=abc1234deadbeef0 \
  --width=1920 --height=1080 \
  --variables '{"title":"Hello Alice","accent":"#ff0000"}'

hyperframes lambda render ./my-template --site-id=abc1234deadbeef0 \
  --width=1920 --height=1080 \
  --variables-file ./alice.json --strict-variables

Variables travel inside the Step Functions Standard execution input, which AWS caps at 256 KiB for the entire payload. Pass typed data (strings, numbers, structured records) through variables; URL-reference media assets (images, audio, video) the composition resolves at render time rather than inlining bytes. The SDK validates the size client-side and rejects oversize inputs with a clear error before any AWS call runs — see the templates-on-lambda guide for the URL-your-assets convention.

lambda render-batch <projectDir>

Fans out N personalised renders from a JSONL batch file — the headline ergonomic for automated template-rendering pipelines. Deploys the site once (or skips with --site-id), then invokes renderToLambda per batch row with per-entry variables and outputKey. Concurrent Step Functions starts are capped at --max-concurrent (default 50) so a 10 000-entry batch doesn’t try to spawn 10 000 executions at once and trip AWS account limits.

Batch file format (JSONL — one JSON object per line):

{"outputKey": "renders/alice.mp4", "variables": {"name": "Alice", "accent": "#ff0000"}}
{"outputKey": "renders/bob.mp4",   "variables": {"name": "Bob",   "accent": "#0000ff"}}
{"outputKey": "renders/carol.mp4", "variables": {"name": "Carol"}, "executionName": "hf-carol-001"}

hyperframes lambda render-batch ./my-template \
  --batch ./users.jsonl \
  --width 1920 --height 1080 \
  --max-concurrent 10

The verb prints a manifest — one row per input line — with executionArn + status:

Batch dispatched: 3 started, 0 failed-to-start.

  ✓ line 1  renders/alice.mp4  arn:aws:states:us-east-1:1234:execution:hf:hf-render-...
  ✓ line 2  renders/bob.mp4    arn:aws:states:us-east-1:1234:execution:hf:hf-render-...
  ✓ line 3  renders/carol.mp4  arn:aws:states:us-east-1:1234:execution:hf:hf-carol-001

Pass --json for the machine-readable form. Poll each execution via hyperframes lambda progress <renderId> (or use the returned executionArn).

--dry-run skips the AWS calls and prints the manifest with status: "would-invoke" for every entry — use it to lint the batch file before committing to N billable executions:

hyperframes lambda render-batch ./my-template --batch ./users.jsonl \
  --width 1920 --height 1080 --dry-run --json

--max-concurrent is orchestrator-side only: it caps how many StartExecution calls run simultaneously, not how many Lambda invocations the account can run. AWS account-level Lambda concurrency limits live one level up and render-batch cannot enforce them; pick --max-concurrent based on your account’s concurrent-execution quota and the Lambda reserved concurrency you provisioned via lambda deploy --concurrency=<N>.

lambda progress <renderId | executionArn>

Prints one progress snapshot — overall percent, frames rendered, Lambda invocations, accrued cost, and any errors. Accepts either a bare renderId (resolved against the stack’s state-machine ARN) or a full SFN execution ARN.

hyperframes lambda progress hf-render-abcd1234

lambda destroy

Calls sam delete --no-prompts and drops the local state file. The render S3 bucket is configured with CloudFormation Retain so it survives destruction — empty and delete it via the AWS console / CLI if you want the storage back.

lambda policies role | user | validate

Print or validate the minimum IAM policy the CLI needs to deploy / invoke / destroy the stack.

# Print an inline-policy doc you can attach to an IAM user that runs the CLI.
hyperframes lambda policies user

# Print { TrustRelationship, InlinePolicy } for an IAM role (default: cloudformation principal).
hyperframes lambda policies role --principal=cloudformation

# Validate a checked-in policy still covers the CLI's needs.
hyperframes lambda policies validate ./infra/iam/hyperframes-deploy.json

validate reads the JSON doc and checks the union of its Effect: Allow actions against the CLI’s required action set, expanding s3:* / s3:Get* / * wildcards. Missing actions print to stderr and the command exits non-zero — wire it into CI to catch drift before the next deploy fails.

The actions list is deliberately broad (Resource: "*") because CloudFormation creates new function / state-machine / bucket ARNs on every adopter’s first deploy. Adopters with stricter security postures should narrow Resource to the deployed ARNs after the first successful run.

State files

hyperframes lambda keeps per-stack metadata under <cwd>/.hyperframes/lambda-stack-<name>.json so the verbs don’t need to call describe-stacks every time. Commit the file to a repo or .gitignore it depending on your workflow — it contains the bucket name, state-machine ARN, and region, none of which are secrets but all of which are AWS-account-identifying.

hyperframes cloudrun

The Google Cloud counterpart to hyperframes lambda. Deploys HyperFrames distributed rendering to Cloud Run + Cloud Workflows and drives renders from your laptop or CI. Wraps the @hyperframes/gcp-cloud-run SDK plus terraform (the module shipped with the package) and gcloud / Cloud Build for the image.

hyperframes cloudrun deploy --project my-gcp-project
hyperframes cloudrun render ./my-project --width 1920 --height 1080 --wait
hyperframes cloudrun destroy --project my-gcp-project   # when you're done

cloudrun deploy

Enables the required APIs, builds + pushes the render image via Cloud Build (unless you pass --image), then terraform applys the module that provisions the GCS bucket, Cloud Run service, Cloud Workflows definition, two service accounts, and a runaway-request alert. Caches the resulting bucket / service URL / workflow id so later verbs don’t need them re-passed.

hyperframes cloudrun deploy --project my-gcp-project --region us-central1
hyperframes cloudrun deploy --project my-gcp-project --image us-central1-docker.pkg.dev/my-gcp-project/hyperframes/hyperframes-render:v1

Flags: --project (required), --region (default us-central1), --image (skip the build), --repo (Artifact Registry repo, default hyperframes). Machine sizing / scaling: --cpu (1/2/4/8, default 4), --memory (e.g. 32Gi, default 16Gi), --max-instances (render fan-out ceiling, default 100), --timeout (per-request seconds, max 3600). Omitted sizing flags keep the module defaults; for anything finer, apply the Terraform module directly.

cloudrun sites create <projectDir>

Tar + upload a project to GCS once and reuse it across renders. --site-id overrides the content hash. Prints the gs:// URI.

cloudrun render <projectDir>

Start a distributed render. --width / --height are required; --fps (24/30/60), --format, --codec, --quality, --chunk-size, --max-parallel-chunks, --target-chunk-frames, and --output-resolution (deviceScaleFactor supersampling, e.g. 4k) mirror the local render flags. --target-chunk-frames caps the frames per chunk so a single chunk can’t run past a per-chunk timeout on a long video: the planner uses the fewest chunks that keep each at or below the bound, up to --max-parallel-chunks, and short videos still collapse to fewer chunks. It’s a ceiling, not a fixed size, and is ignored when --chunk-size is set. Pass composition variables with --variables '{"title":"Hi"}' or --variables-file alice.json; add --strict-variables to fail on a key that’s undeclared or mistyped vs the composition’s data-composition-variables. --wait polls until the render finishes and prints the output URI + cost; without it the command returns an execution name.

cloudrun render-batch <projectDir>

Fan out N personalised renders from a JSONL batch file (--batch users.jsonl, one {"outputKey":"...","variables":{...}} per line). Deploys the site once and starts an execution per entry, capped at --max-concurrent (default 50). --dry-run prints the resolved manifest without starting anything. Shares the render flags above.

cloudrun progress <executionName>

Print progress + cost for an in-flight or finished render. Coarse running progress; exact frame counts + cost on success.

cloudrun destroy

terraform destroy the stack (force-destroys the render bucket). Reads the cached project/region, or pass --project / --region.

When to pick cloudrun vs lambda

Same trade-off as lambda, on Google Cloud instead of AWS. Pick cloudrun when your backend + storage already live on GCP. The render primitives are identical; only the storage (GCS), compute (Cloud Run), and orchestration (Cloud Workflows) adapters differ.

State file

hyperframes cloudrun caches the deployed stack’s coordinates under ~/.hyperframes/cloudrun-state.json (project id, region, bucket, service URL, workflow id) so render / progress / destroy don’t need them re-passed. None are secrets, but all are GCP-project-identifying.

hyperframes.json

hyperframes init writes a hyperframes.json file at the root of every new project. hyperframes add reads it to know which registry to pull items from and where to drop them. Edit the file (or delete it to fall back to defaults) to reshape your project layout or point at a custom registry.

{
  "$schema": "https://hyperframes.heygen.com/schema/hyperframes.json",
  "registry": "https://raw.githubusercontent.com/heygen-com/hyperframes/main/registry",
  "paths": {
    "blocks": "compositions",
    "components": "compositions/components",
    "assets": "assets"
  }
}

Field	Description
registry	Base URL of the registry add pulls from. Defaults to the public Hyperframes registry.
paths.blocks	Where block .html files land (relative to project root).
paths.components	Where component files land (relative to project root).
paths.assets	Where referenced asset files (images, fonts) land.

Missing fields are filled with defaults — you only need to specify what you want to override.

Related Packages

Producer

The rendering pipeline the CLI calls under the hood. Use directly for programmatic rendering.

Studio

The editor UI that powers hyperframes preview. Use directly to embed in your own app.

Core

Types, linter, and runtime. Use directly for custom tooling and integrations.

Engine

The capture engine. Use directly for custom frame capture pipelines.