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 stage-plangit 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/stage-plan)<a href="https://agentmods.dev/skills/orkas-ai/orkas-videostudio/stage-plan"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas-videostudio/stage-plan/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/stage-plan"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas-videostudio/stage-plan.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 | $0.00000 | $0.03811 |
| Opus 5.5 | $0.00000 | $0.01524 |
| Sonnet 5.5 | $0.00000 | $0.00762 |
| Haiku 4.5 | $0.00000 | $0.00381 |
Grade A, and why
stage-plan 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
stage-plan
How to turn "here is my material + here's the video I want" into a single, inspectable plan spanning reference images/videos, deterministic or semantic editing, generation, and composition. The output is project/plan.json—a cross-modal EDL that the assembler walks. Ingest with the public ovs analysis commands, validate with ovs plan, and execute through the compose/generate/edit lines.
Use this line when the deliverable is NOT cleanly one axis — e.g. "trim my clip, add a title card and captions, and a voiceover", or "my footage for the middle, generate an opener, compose the stats". For a pure single-axis job, route to that single line instead (see video-router).
Step 1 — Ingest from evidence, never from assumption
You cannot plan against material you have not looked at. For EVERY supplied clip, before writing any segment:
- Probe it (
ovs edit probe) for real duration / resolution / fps / audio presence. A plan that cuts past the real duration breaks. - Read its content the cheapest way that fits:
- spoken audio →
ovs transcribe raw/clip.mp4 --out project/transcripts/clip.json(pass--model large-v3for non-English) → you now have timecoded words to cut on and a reusable transcript file for edit ops. - silent / screen-recording / slideshow → extract representative frames with
ovs edit extract-frameand inspect them with the current multimodal model. The audio being empty does NOT mean the screen is. If you cannot inspect images, ask the user for the on-screen beats. Never infer slide/screen content from the topic alone. - need to judge what a moment LOOKS like (is the hero shot usable? is the product right-side up?) → read frames:
ovs edit extract-framethen look at them. If you cannot see images, say so and plan on probe/transcript evidence alone — mark those judgments unverified, do not invent them.
- spoken audio →
- Record what each input is good for in
project/ingest.json:{input_id, duration, has_audio, content_summary, quality_risks:[...], usable_for:[...], planning_implications:[...]}. This is the factual basis the plan cites — segments referenceinput_ids from here. Rules:content_summaryis specific and from observation: "45 s of interview, no b-roll, mono audio" — never "user provided footage". An entry is only "reviewed" if a real probe, transcript, or inspected extracted frame supports it; never claim you looked at a clip you did not.- Usability heuristics: video > 10 s → hero footage; > 3 s → b-roll; has speech → dialogue source; audio-only → narration/music source, production must supply the visuals; image-only → motion must come from animation or generation.
- Quality risks to flag: width < 720 / height < 480 (will look soft), clip < 3 s (limited use), mono audio, a still where the brief wants motion. A flagged risk the plan ignores is a planning bug — resolve it during direction confirmation.
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 4eb9920788f6
- 1mo ago Changed · +2 lines fcf02c7cd57d
- 1mo ago Changed b22da90a2c1a
- 1mo ago First seen · 127 lines · 0 tokens per session scan A 6bbd5e10390e
stage-plan is a skill published in the GitHub repository Orkas-AI/Orkas-VideoStudio (329 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,811 tokens. 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.