Hyperframes Cli logo

Hyperframes Cli

Organization
skillmds
hyperframes-cli

Use the HyperFrames CLI development loop: init, add, catalog, capture, lint, check, snapshot, compare, grade-compare, preview, play, present, beats, keyframes, single or batch render, publish, cloud, cloudrun, feedback, lambda, doctor, browser, info, upgrade, skills, compositions, docs, benchmark, telemetry, transcribe, auth, tts, and remove-background. Also use when diagnosing build or render failures. validate, inspect, and layout are deprecated aliases; use check. Covers local, HeyGen-hosted cloud, AWS Lambda, and Google Cloud Run rendering.

Overview

Publisherskillmds
Repositoryskillmd
Skill namehyperframes-cli
Stars
249
Forks
3
Bundled files
10
LicenseMIT
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.

  • 10 bundled files

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

  • Open source

    Published by skillmds 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/skillmds/skillmd/tree/main/plugins/cloud-devops/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/skillmds/skillmd.git /tmp/skillmd
mkdir -p .claude/skills
cp -r /tmp/skillmd/plugins/cloud-devops/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

Run commands as npx hyperframes ... unless project instructions provide a wrapper. Obey the wrapper when present. The CLI requires Node.js 22 or newer and FFmpeg.

Development loop

  1. Scaffold: npx hyperframes init <project> or capture a site. In non-TTY mode, pass --non-interactive --example=<name>.
  2. Author: write the composition using /hyperframes-core.
  3. Get fast feedback while editing: run npx hyperframes lint after the first HTML pass and after structural changes.
  4. Run the final gate: run npx hyperframes check; it reruns lint before opening the browser. Do not prepend a redundant standalone lint invocation. Add --snapshots for annotated overview frames and finding crops.
  5. Inspect sub-compositions: when index.html mounts data-composition-src, capture midpoint snapshots and inspect each mounted scene.
  6. Open the final Studio preview: run npx hyperframes preview, hand the timeline project URL to the user, and ask whether to revise or render.
  7. Render only after approval: use draft quality for iteration and high quality for delivery.
  8. Verify the output: confirm the file exists, is non-empty, and has a plausible duration.
bash
# Fast iteration check; repeat while authoring as needed.
npx hyperframes lint

# Required final gate; includes lint.
npx hyperframes check
npx hyperframes preview
npx hyperframes render --quality high --output out.mp4
test -s out.mp4
ffprobe -v error -show_format out.mp4

check runs lint first, then uses one browser session and one seek pass to audit runtime errors, failed requests, layout, *.motion.json assertions, and WCAG contrast. Persistent findings gate the exit code; transient entrance or exit findings are informational. Use --strict to gate warnings. validate, inspect, and layout remain aliases for compatibility but must not appear in new instructions or scripts.

Two different preview surfaces

Do not confuse these states:

SurfaceWhen it may openPurpose
Storyboard boardBefore composition checks, only when storyboard: yesReview plan cards and wireframe sketches. Open ?view=storyboard#project/<name>.
Final composition previewAfter check passesReview the assembled timeline before render. Open #project/<name>.

The early board is not approval of the final video. Rendering always requires the final approval defined by hyperframes-core/references/review-loop.md.

Sub-composition smoke test

Static audits cannot catch every mount failure. When the project uses sub-compositions, capture at least one visible midpoint for each host slot:

bash
npx hyperframes snapshot --at <t1>,<t2>,<t3>

Treat tiny unstyled content, canvas-sized icons, missing hero elements, or timeline-registration timeouts as render-blocking mount defects. See hyperframes-core/references/sub-compositions.md for the corresponding fixes.

Agent conventions

  • Prefer --json for agent and CI calls. Server-mode render, preview, and play do not provide ordinary JSON output; preview --selection --json and preview --context --json are query-mode exceptions.

  • doctor --json always exits zero. Gate on its payload:

    bash
    npx hyperframes doctor --json | jq -e '.ok' >/dev/null
  • Non-TTY mode is automatic. init requires --example there; use --non-interactive to force deterministic behavior on a TTY.

  • Use one HYPERFRAMES_RUN_ID for all commands in the same verification loop.

  • Use --strict, --strict-all, and --strict-variables when the corresponding warnings, variables, or CI conditions must gate the render.

  • JSON paths redact the home directory as $HOME; do not try to reverse the redaction.

  • When a hosted cloud project approaches or exceeds the 200 MB upload limit, use cloud render --dry-run --json and follow the .hyperframesignore investigation in references/cloud.md. Never ignore an asset merely because it is large.

  • Never render merely because checks pass. Pause at the final preview and wait for approval.

Studio-directed edits

When the user refers to “this element” or the current selection, query Studio instead of guessing:

bash
npx hyperframes preview --context --json --context-fields selection

Use selection.target.hfId when available, otherwise its selector and source file. If the result reports no-selection, ask the user to click the element and rerun. Request only the context slices you need; use --context-detail full only for computed styles or editable text metadata. Full behavior and failure codes live in references/preview-render.md.

Render choices

NeedCommand
Fast local iterationnpx hyperframes render --quality draft
Final local deliverynpx hyperframes render --quality high --output out.mp4
Reproducible container rendernpx hyperframes render --docker --strict --output out.mp4
Local variable-driven batch rendernpx hyperframes render --batch rows.json --output "renders/{name}.mp4"
HeyGen-hosted zero-infrastructure rendernpx hyperframes cloud render
Self-managed distributed AWS rendernpx hyperframes lambda render <project> --width 1920 --height 1080 --wait
Self-managed distributed GCP rendernpx hyperframes cloudrun render <project> --width 1920 --height 1080 --wait

Use cloud rendering when the user wants hosted rendering without local Chrome, FFmpeg, or AWS. Use Lambda only when AWS ownership is a requirement. Use Cloud Run only when GCP ownership is a requirement. Read the matching reference before running any cloud path.

After verifying a successful render, send one feedback report unless telemetry is disabled or the user opted out:

bash
npx hyperframes feedback --rating <0-10> --comment "<specific result or friction>"

Keep clean-run feedback concise. For any bug or friction, capture a reproduction packet before submitting; do not send only a symptom summary. Include the rerunnable command and working directory, expected versus actual behavior, exact error, whether output completed/fell back/failed, workaround, and repro-project status. For a rating ≤ 7 that describes a visual defect (black frame, flicker, corrupt output, wrong frame, blank output, other visual anomaly), also include a COMPOSITION_STRUCTURE: block — a privacy-preserving structural anatomy (element census + attribute presence + timeline shape) so maintainers can pattern-match against known bug families without the composition ZIP. Agents auto-fill this via the composition-census helper; the human user does not fill it by hand. If the issue did not reproduce again, say so and still include the last failing command and logs. Use --file-issue only with consent: it publishes a minimal reproduction to a public URL. The required packet format and privacy warning live in references/preview-render.md.

Read the matching reference before running a command

The following references and owning skills are mandatory command contracts, not optional background reading. Before running a command in the table, read its matching row.

NeedReference
init, capture, skillsreferences/init-and-scaffold.md
lint, check, motion sidecars, snapshotreferences/lint-validate-inspect.md
compare, grade-compare, variable-driven render --batchreferences/compare-and-batch.md
beats for an existing project's Studio beat gridreferences/beats.md
preview, play, render, publish, Studio context, feedbackreferences/preview-render.md
doctor, browser managementreferences/doctor-browser.md
auth, HeyGen-hosted cloud rendering, and template variablesreferences/cloud.md
AWS Lambda deployment and renderingreferences/lambda.md
Google Cloud Run deployment and renderingreferences/cloudrun.md
info, upgrade, compositions, docs, benchmark, telemetry, media preprocessingreferences/upgrade-info-misc.md

For composition variables, also read /hyperframes-core → references/variables-and-media.md. For hyperframes add and hyperframes catalog, use /hyperframes-registry. Before hyperframes present, read /slideshow; before hyperframes keyframes, read /hyperframes-keyframes. For TTS, transcription, captions, or background removal choices, use /media-use.

The specialized commands are deliberately documented by their owning workflows:

bash
npx hyperframes present <project-dir> --port 3004 --no-open
npx hyperframes beats <project-dir> --json
npx hyperframes keyframes <project-dir> --json

present serves a navigable deck with presenter and audience synchronization. beats is the standalone Studio beat-grid utility defined in references/beats.md. keyframes surfaces seek-safe animation and motion-path diagnostics.

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?

Use the HyperFrames CLI development loop: init, add, catalog, capture, lint, check, snapshot, compare, grade-compare, preview, play, present, beats, keyframes, single or batch render, publish, cloud, cloudrun, feedback, lambda, doctor, browser, info, upgrade, skills, compositions, docs, benchmark, telemetry, transcribe, auth, tts, and remove-background. Also use when diagnosing build or render failures. validate, inspect, and layout are deprecated aliases; use check. Covers local, HeyGen-hosted cloud, AWS Lambda, and Google Cloud Run rendering.

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/skillmds/skillmd/tree/main/plugins/cloud-devops/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 skillmds under the MIT 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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