Orkas is a desktop application for commanding a team of AI agents through one chat, with a commander model assigning work to specialist agents in parallel or in sequence. People use it to coordinate research, writing, presentations, and software tasks while keeping files on their computer. The catalogue includes skills for extending the agents available to Orkas.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Orkas-AI/Orkas --skill composition-design-reviewgit clone --depth 1 https://github.com/Orkas-AI/OrkasWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/orkas-ai/orkas/composition-design-review)<a href="https://agentmods.dev/skills/orkas-ai/orkas/composition-design-review"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas/composition-design-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/orkas-ai/orkas/composition-design-review"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas/composition-design-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00003 | $0.01585 |
| Opus 5 | $0.00002 | $0.00792 |
| Sonnet 5 | $0.00001 | $0.00317 |
| Haiku 4.5 | $0.00000 | $0.00159 |
Grade A, and why
composition-design-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
composition-design-review
Apply this checklist yourself after a successful composition.snapshot, before the preview is shown. It is advisory: nothing is submitted to the host, no operation records a verdict, and no gate waits on it — the host publishes the contact sheet with the passing snapshot. It is a design QA layer for your own authoring, not a renderer, line router, or generic video craft checklist.
Do not open a new user Gate. Native preflight/inspect/sampled-frame QA runs before this pass; a passing snapshot attaches the full-color contact sheet directly as model-visible evidence. Review every frame in that attached complete index, without reopening the sheet through generic read_files, and open at full scale only the frame-0 cover, frames named by QA findings, and frames whose sheet cell shows risk (dense or doubtful text, suspected overlap or blankness). Do not stop after the first defect. Collect all concrete visible blockers across the full frame set, make one batched localized repair to manifest.art_direction or affected HTML, and re-run inspect + snapshot; then re-check the complete new frame set.
Activation
Apply the checklist whenever snapshot evidence exists, and give it extra weight when:
- The approved brief is brand, product, promo, launch, version-update, portfolio, or other design-led COMPOSE work.
project/composition/composition-manifest.json::art_direction.style_sourceis present.- Sampled frames show 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 this review for non-COMPOSE edit/TTS/clip-selection work. Do not repeat the full pass after the draft renders — post-draft QA is native and render-specific.
Review Inputs
Read only the relevant artifacts:
project/composition/composition-manifest.json, especiallyart_directionand the affected canonical scene- Every composition-local image/video in
art_direction.references, including declared roles, reproduce/edit/guide intent, preserve/may-change boundaries, target scenes, and layout/temporal anchors project/composition/narration-map.jsonas read-only evidence when detailed narration-line alignment mattersproject/composition/qa/inspect.json, orproject/render/draft-report.jsononly for fallback review- For preview review, the snapshot result's
contact_sheetcovering everyframe_pathsitem: first frame, every scene midpoint, and payoff/closing frame. Open individual paths where the activation rule above points — cover, QA-named frames, risky cells. - For fallback review, sampled evidence frames from the draft report:
contact_sheet,frame_paths, first frame, one mid-frame per scene, and payoff/closing frame - The approved script only when a finding depends on message intent
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today Changed 9098a4fc5a67
- 12d ago First seen · 92 lines · 3 tokens per session scan A d736f383cd94
composition-design-review is a skill published in the GitHub repository Orkas-AI/Orkas (1,911 stars, last pushed today), licensed MIT. It adds 3 tokens to every session and 1,585 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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