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Hyperframes Cli

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calesthio
hyperframes-cli

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`).

Overview

Publishercalesthio
RepositoryOpenMontage
Skill namehyperframes-cli
Stars
67K
Forks
8.5K
Bundled files
6
LicenseAGPL-3.0
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.

  • 6 bundled files

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

  • Open source

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

Installation

Install the Hyperframes Cli 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/calesthio/OpenMontage/tree/main/.agents/skills/hyperframes-cli
  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/calesthio/OpenMontage.git /tmp/OpenMontage
mkdir -p .claude/skills
cp -r /tmp/OpenMontage/.agents/skills/hyperframes-cli .claude/skills/hyperframes-cli
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hyperframes Cli 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 Hyperframes Cli 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 Hyperframes Cli 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.

HyperFrames CLI

Everything runs through npx hyperframes unless project instructions specify a local wrapper. Obey the local wrapper exactly. Requires Node.js >= 22 and FFmpeg.

Workflow

  1. Scaffold — npx hyperframes init my-video (or capture from a URL). init also checks the installed skills against the latest on GitHub and updates the global set if any are out of date. The --skip-skills flag is currently neutered (temporary, while the skills.sh registry catches up), so every init runs this check and pulls our latest skills regardless.
  2. Write — author HTML composition (see the hyperframes-core skill)
  3. Lint — npx hyperframes lint
  4. Validate — npx hyperframes validate (runtime errors + contrast)
  5. Visual inspect — npx hyperframes inspect
  6. Preview — npx hyperframes preview opens Studio, the timeline editor where the user can manually edit anything (not just watch). Review there, then ask before rendering.
  7. Render — pick the variant:
    • Iterate: npx hyperframes render --quality draft
    • Deliver: npx hyperframes render --quality high --output out.mp4
    • CI / cross-host repro: npx hyperframes render --docker --strict --output out.mp4
    • Cloud (long / large): npx hyperframes lambda render ./my-project --width 1920 --height 1080 --wait (see Lambda below)

Run lint, validate, and inspect before preview. lint catches missing data-composition-id, overlapping tracks, and unregistered timelines. validate loads the composition in headless Chrome and reports runtime console errors plus WCAG contrast issues. inspect seeks through the timeline and reports text spilling out of bubbles/containers or off the canvas — and, when a *.motion.json sidecar is present, verifies motion intent (entrances firing under seek, stagger order, in-frame, liveness) against that same seeked timeline.

For motion-heavy work, prefer snapshot-driven iteration and a *.motion.json sidecar — see references/lint-validate-inspect.md for the discipline and motion-verification spec.

Agent Conventions

Cross-cutting rules that hold for every command:

  • --json is available on every command except render, preview, and play. Use it for any agent / CI invocation of the supported commands; output includes a _meta envelope (cli version, latest available, update advice). render reports status via stdout + exit code only — verify success with the post-render check below; preview / play are servers, no JSON.
  • doctor --json always exits 0, even when the environment is broken. Gate on the payload's ok field: npx hyperframes doctor --json | jq -e '.ok' > /dev/null. This insulates pipelines from CLI release churn.
  • Non-TTY mode is auto-detected. When stdout is not a TTY (CI, agents, piped output) the CLI auto-switches to non-interactive; init then requires --example. Pass --non-interactive to force this mode even on a TTY.
  • CI gating on render: --strict fails on lint errors, --strict-all fails on warnings too, --strict-variables fails on undeclared --variables keys.
  • Paths in --json are redacted — $HOME becomes the literal $HOME so output is safe to paste into bug reports and agent contexts.
  • Render is user-gated. Never auto-render once the checks pass. Pause at preview, tell the user the video is editable in Studio, and render only after they approve.
  • Post-render verification. After render returns exit 0, confirm the output file exists and has plausible size before reporting success: [ -s "$OUTPUT" ] || echo "render produced no output". The CLI prints ◇ <path> on success; for long renders also sanity-check duration with ffprobe -i "$OUTPUT" -show_format -v error.

Routing

Want to…Read
Scaffold a project (init, capture, skills)references/init-and-scaffold.md
Check correctness (lint, validate, inspect, snapshot)references/lint-validate-inspect.md
Preview or render (preview, play, render, publish)references/preview-render.md
Diagnose the environment (doctor, browser)references/doctor-browser.md
Cloud render on AWS Lambda (lambda deploy / sites / render / progress / destroy / policies)references/lambda.md
Everything else (info, upgrade, compositions, docs, benchmark, telemetry, asset preprocessing)references/upgrade-info-misc.md

Cross-Skill Hand-Offs

  • Tailwind projects (init --tailwind) → use hyperframes-core (Tailwind reference) before editing classes or theme tokens.
  • Registry blocks/components (hyperframes add, hyperframes catalog) → use hyperframes-registry for install paths, sub-composition wiring, and snippet merging.
  • Asset preprocessing (tts, transcribe, remove-background) → use hyperframes-media for voice selection, Whisper model rules, captions, and TTS-to-captions chain.
  • Parametrized renders (--variables) → declared via data-composition-variables on <html>; see hyperframes-core for the full schema.

Lambda (Cloud Rendering)

hyperframes lambda deploys distributed rendering to AWS Lambda and drives renders from your laptop or CI. End-to-end is three commands:

bash
npx hyperframes lambda deploy                                             # provision SAM stack (Lambda + Step Functions + S3)
npx hyperframes lambda render ./my-project --width 1920 --height 1080 --wait
npx hyperframes lambda destroy                                            # tear down (S3 bucket is retained)

Use Lambda when a render is too long / too large for one host (multi-minute videos, 4K, large parallel batches) and you have AWS credentials configured. For dev-loop iteration stay on local render.

See references/lambda.md for prerequisites, all 6 subcommands (deploy, sites create, render, progress, destroy, policies), IAM policy validation, state files, and cost / cleanup rules.

Minimum Completion Gate

Static gates

bash
npx hyperframes lint
npx hyperframes validate

Add inspect for layout-sensitive work and render --strict in CI to fail on lint errors.

Visual smoke test — required when the project uses sub-compositions

lint / validate / inspect evaluate each composition in isolation. They never load index.html and mount sub-compositions via data-composition-src, so they cannot catch cross-file mount failures (see hyperframes-core → references/sub-compositions.md, "Common pitfalls"). The only gate that catches them is one that actually loads index.html and seeks the timeline.

Use hyperframes snapshot — it loads the project the same way render does (so it exercises the same mount path) but only captures the timestamps you request, so it's seconds instead of a full render:

bash
# Capture one frame at the midpoint of every sub-composition.
# Midpoints = data-start + data-duration/2 for each host slot in index.html.
npx hyperframes snapshot --at <t1>,<t2>,<t3>,...

# Or, if you don't need per-scene targeting, an evenly-spaced sample:
npx hyperframes snapshot --frames 9

Output lands in snapshots/frame-NN-at-Xs.png. Eyeball each frame against the scene plan.

Per-frame red flags (each maps to a specific failure mode the static gates miss):

What you seeRoot cause
Text shows up tiny + unstyled in the top-left corner<style> block left in <head> outside <template> (Pitfall 1) — no CSS reached live DOM
SVG/icon elements blown up to canvas-sizeSame as above — no width/height constraints applied
Hero element of the scene is missing entirely; only background + watermark visibleHost-id ≠ template id (Pitfall 2) — timeline never ran, frame captured at initial state
Snapshot command logs Sub-composition timelines not registered after 45000msPitfall 2 — direct confirmation

snapshots/ can be deleted after eyeballing; the user-facing final render is a separate pass with npx hyperframes render.

Bundled files

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

Frequently asked questions

What does the Hyperframes Cli AI skill do?

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`).

Why use Hyperframes Cli on TypingMind?

Because you install it once and use it with any model. Hyperframes Cli 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 Hyperframes Cli in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/calesthio/OpenMontage/tree/main/.agents/skills/hyperframes-cli. 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 Hyperframes Cli?

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 Hyperframes Cli?

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

Is the Hyperframes Cli AI skill free?

Yes. It is published on GitHub by calesthio under the AGPL-3.0 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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