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-VideoStudio --skill orchestrationgit clone --depth 1 https://github.com/Orkas-AI/Orkas-VideoStudioWrote 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-videostudio/orchestration)<a href="https://agentmods.dev/skills/orkas-ai/orkas-videostudio/orchestration"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas-videostudio/orchestration/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-videostudio/orchestration"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas-videostudio/orchestration.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 | $0.00099 | $0.03140 |
| Opus 5.5 | $0.00040 | $0.01256 |
| Sonnet 5.5 | $0.00020 | $0.00628 |
| Haiku 4.5 | $0.00010 | $0.00314 |
Grade A, and why
orchestration 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 1mo ago.
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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
orchestration
You are producing a short video. Run this as a TIGHT program — lean turns — but STOP at every GATE so the creative decisions stay the user's. Only the technical/assembly steps between gates run unattended. The CLI surface is ovs ... (an MCP server mirrors it 1:1; use whichever your host exposes). All work for one deliverable lives under a single project dir, e.g. project/.
Read gate-control once before the first user gate. It is the single authorization policy across all lines. After every gate reply, resumed approval, post-gate revision, or exhausted visual-QA result, run ovs gate transition and follow its one returned action; line sections below define artifacts and production steps, not a competing approval state machine.
Checkpoint protocol — how every GATE works (there is no special form UI)
- Show the artifact in chat so the user can actually see it — script/plan as markdown, images inline, a draft video as its output file path — plus one line of "what I'll do next" and any cost/QA note.
- State the options for that gate and WAIT for the user to reply. Do not run the next production step in the same turn as the gate.
- On reply, resolve the choice with
ovs gate transition: approve → continue only with the returned operation; revise → redo only the authorized scope, re-show, and re-gate only when the resolver says so; abort → stop. Never pass a gate without explicit user confirmation, and never ask again for an unchanged artifact whose approval is already recorded.
1. Route + lock (read video-router)
Classify and LOCK the line (no silent switching):
- COMPOSE — explain / teach / animate / motion-graphics / kinetic text, no source footage →
ovs draft(+ optionalovs image/ovs videoimagery, optionalovs speaknarration). - GENERATE — "footage of / a scene of / cinematic / a presenter or avatar speaking / talking-head" → AI footage via
ovs video(+ovs imagefor the subject,ovs speakfor voice), assembled withovs edit. - EDIT — the user supplied real clips to cut / join / subtitle / localize →
ovs edit(+ovs transcribefor transcript-driven work). - AUTO (end-to-end) — the primary timeline weaves MORE THAN ONE axis; adding audio/captions to an existing video remains EDIT. Run the cross-modal orchestration (read
stage-plan, thenstage-assemble); the lock is the plan'sdelivery_promise.
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.
- 1mo ago Changed 79226f8b5920
- 1mo ago Changed 439e85c6896d
- 1mo ago First seen · 90 lines · 99 tokens per session scan A 58a97a74fa6d
orchestration is a skill published in the GitHub repository Orkas-AI/Orkas-VideoStudio (329 stars, last pushed today), licensed MIT. It adds 99 tokens to every session and 3,140 once invoked, about $0.0004 per session on Opus 5.5. 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.
Other skills, from other repositories
claude-real-video
Watch a video for the user. Use when the user shares a video URL (YouTube etc.) or local video file and wants it summarized, analyzed, or discussed — Claude can't ingest video directly, so this skill extracts scene-aware keyframes + transcript first, then reads those.
brain-ingest
The process for digesting a conversation, document, or research result, classifying it, and writing it down as brain content (a root-page update or a new/updated page) through the brain CLI.
issue-resolve
Use when an assessed gflow-cli issue (verdict CONFIRMED-BUG or LIKELY-BUG) has localized, verifiable scope and should be driven to a fix. Mutating and gated: it works in an isolated worktree, fixes test-first, and opens a DRAFT PR for human review. Built to run autonomously (hermes-ops) within a strict action envelope…
issue-assessment
Use when triaging a GitHub issue for gflow-cli — a reporter's bug claim, a freshly-filed issue, or deciding whether and how to act on one. Also use when an autonomous agent (hermes-ops) picks up a labelled issue. Read-only: produces a verdict, an end-to-end-verifiability judgment, and a reporter-facing reply. Does not…
live-verify
Two-part gate for gflow-cli feature/fix work. Part 1 (Pre-flight): use when starting work on a gflow-cli feature or fix — confirms the checkout reflects current develop before investing effort. Part 2 (Live-verify): use before claiming gflow-cli work done, especially anything touching a generation code path…
sonar
Check the SonarCloud quality gate for a PR (or the current branch) and drive it to zero. Reports GREEN, or the exact PR-scoped new issues/hotspots/coverage gaps to fix.