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Composition Design Review

Community
Orkas-AI
composition-design-review

Design review layer for OrkasVideoStudio COMPOSE previews and drafts. Review the complete snapshot index and inspect the cover and risky frames at full size before showing a visual preview. Return one complete, actionable repair set without opening another user gate.

Overview

PublisherOrkas-AI
RepositoryOrkas-VideoStudio
Skill namecomposition-design-review
Stars
500
Forks
25
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Orkas-AI on GitHub. Read the source before you install it.

Installation

Install the Composition Design Review 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/Orkas-AI/Orkas-VideoStudio/tree/main/packages/skills/composition-design-review
  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/Orkas-AI/Orkas-VideoStudio.git /tmp/Orkas-VideoStudio
mkdir -p .claude/skills
cp -r /tmp/Orkas-VideoStudio/packages/skills/composition-design-review .claude/skills/composition-design-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Composition Design Review 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 Composition Design Review 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 Composition Design Review 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.

composition-design-review

Use this after stage-compose has run ovs snapshot, before showing the visual preview. The snapshot's frame_paths are the complete review set. Read every cell of the contact-sheet index, then open frame zero, QA-named frames and cells with doubtful text, overlap or blankness at full size. Never infer a clean unseen frame.

If the visual preview is intentionally skipped, run the same review against representative draft frames after an ok ovs draft report. This is a design QA layer, not a renderer, line router, or generic video craft checklist. It does not create a new user gate or approval field.

Activation

  • Run for every visual preview before that preview is shown.
  • Use the post-draft fallback for design-sensitive COMPOSE work when no preview was shown, including brand, product, promo, launch, version-update, portfolio, or other design-led work.
  • Also use the fallback when the draft shows a visible design risk that deterministic QA cannot judge, such as a weak first frame, flat hierarchy, repeated scene grammar, or motion that hides the message.

Do not run the post-draft fallback for ordinary edit/TTS/clip-selection work, simple caption cards, or generic "make it polished" wording without a visible design risk.

Review Inputs

Read only the relevant artifacts:

  • project/composition/composition-manifest.json
  • Every composition-local image/video in art_direction.references, including its intent, roles, preserve/may-change boundary, target scenes, and spatial/temporal anchors
  • Source frames extracted at the declared reference times and paired with their target snapshot/draft frames
  • Every immutable path in the latest successful snapshot's frame_paths
  • project/composition/qa/check.json
  • For the fallback only: project/render/draft-report.json and representative draft frames
  • Legacy approved script/shotlist only when a finding depends on message intent

Do not review mutable aliases as if they were frozen evidence. Preserve the reviewed frame_paths in the review result so the exact revision is auditable.

Findings Rubric

Tag each finding as blocker, fix, or polish. Inspect the complete frame set before repairing anything, then return all blockers in one batch.

Blockers must identify a specific scene/frame, the visible evidence, and the smallest repair. A finding is not a blocker just because the design could be more distinctive, or because check reported a visual advisory that does not break the approved promise.

Blockers:

  • First frame is blank, unreadable, or fails the dedicated cover contract: approved promise, dominant hero, and at least two recognizable signals of the actual video content.
  • Text is unreadable, hides the approved promise/CTA, or materially blocks comprehension because of size, safe-zone, overlap, occlusion, or contrast.
  • The contract/source/audio/media/video QA says approved scene copy, canvas, assets, runtime dependencies, narration mapping, or sampled frames do not match the model-authored HTML/contract.
  • Visual language contradicts an explicit style source or ignores required brand tokens.
  • A reference image or video visibly loses a required preserve axis, changes something outside may_change, violates an anchor, or misses the requested edit.
  • The piece reads as a slideshow when the approved promise was motion graphics.
  • Motion hides the message, distracts from the focal point, or breaks narration timing.
  • A source asset outside the authorized preserve boundary was reused despite an explicit exclusion or ownership constraint.

Fix:

  • First frame is truthful and readable but its topic signals are too generic or weak to work as a strong cover.
  • Text has a visible safe-zone, size, overlap, occlusion, or contrast advisory, but the main message remains readable.
  • Repeated layout, transition, or card pattern three or more times in a row.
  • Palette uses extra chromatic colors beyond the contract.
  • Type hierarchy is flat or labels feel like UI residue instead of video graphics.
  • English titles, body copy, captions, subtitles, or CTAs are forced to all caps. Restore approved natural casing and use scale, weight, width, color, or spacing for hierarchy. Preserve all caps only when the user supplied that exact casing or an external brand requires it; model-authored art direction is not authorization.
  • Scene density is too high for phone viewing.
  • Style-source adaptation is vague: it borrows mood words but no concrete tokens.
  • Reference comparison is based on provenance or mood rather than the declared intent, roles, protected attributes, allowed changes, and anchors.

Polish:

  • Easing, stagger, spacing, shadow, stroke, or texture could better support the tone.
  • A stronger thumbnail frame or payoff hold would improve memorability.
  • A minor token mismatch that does not hurt comprehension.

Repair Preference

Fix the highest-level artifact that caused the issue:

  1. composition-manifest.json#art_direction when the thesis, tokens, layout budget, timing, narration mapping, or source-shot mapping is wrong; reconcile after structural changes.
  2. index.html for visual hierarchy, typography, layout, motion, asset, or scene variation fixes.

Do not solve design problems by only nudging pixels. If the issue is "too generic", change the signature device or scene grammar. If the issue is "too dense", remove or split content.

After the full review, apply at most one localized repair pass containing the complete blocker set. Then run reconcile when needed, ovs check, and ovs snapshot again. Review every frame in the new frame_paths; never show a partially reviewed revision.

Output

Keep a compact internal record of the complete reviewed frame set and any concrete blockers, fixes applied, and optional polish. This is the author's advisory checklist: no numeric score or separate verdict submission blocks the next operation. Deterministic QA still owns structural and media failures. Hand current readiness and evidence to gate-control; never repeat a full static design review after a visually unchanged draft.

Frequently asked questions

What does the Composition Design Review AI skill do?

Design review layer for OrkasVideoStudio COMPOSE previews and drafts. Review the complete snapshot index and inspect the cover and risky frames at full size before showing a visual preview. Return one complete, actionable repair set without opening another user gate.

Why use Composition Design Review on TypingMind?

Because you install it once and use it with any model. Composition Design Review 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 Composition Design Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Orkas-AI/Orkas-VideoStudio/tree/main/packages/skills/composition-design-review. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Composition Design Review?

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 Composition Design Review?

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

Is the Composition Design Review AI skill free?

Yes. It is published on GitHub by Orkas-AI 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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