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 stage-plangit 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/stage-plan)<a href="https://agentmods.dev/skills/orkas-ai/orkas/stage-plan"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas/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/stage-plan"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas/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 | $0.00002 | $0.06170 |
| Opus 5 | $0.00001 | $0.03085 |
| Sonnet 5 | $0.00000 | $0.01234 |
| Haiku 4.5 | $0.00000 | $0.00617 |
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 10d 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 — 208 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 that spans reference images, reference videos, deterministic editing, semantic editing, generation, and composition. The output is project/plan.json — a cross-modal Edit Decision List (EDL) — which the assembler walks. Host-neutral: ingest evidence comes from stage-edit skill scripts (probe / silence / ocr / scenes / quality / extract_frame) plus video_studio transcription; this skill provides the plan validator, and line producers execute the signed decisions.
Where the material comes from. User-uploaded clips arrive as chat attachments marked model_readable="false" with a path (see the attachment list). That flag means "not vision input", NOT "unusable" — it is source material to ingest with the scripts below. Copy each into raw/ (or pass its attachment path as --input) before probing; never skip a model_readable="false" clip or plan around material you have not actually ingested.
How to call ingest scripts
Use stage-edit scripts for factual ingest before writing the plan, except transcription, which runs through the required built-in video_studio tool.
"$ORKAS_NODE" "$ORKAS_PC_DIR/bin/run-skill.cjs" stage-edit edit_video -- --op probe --input raw/clip.mp4
"$ORKAS_NODE" "$ORKAS_PC_DIR/bin/run-skill.cjs" stage-edit analyze_media -- --op ocr --input raw/screen-recording.mp4
"$ORKAS_NODE" "$ORKAS_PC_DIR/bin/run-skill.cjs" stage-edit edit_video -- --op extract_frame --input raw/clip.mp4 --start 3 --output project/frames/clip-3s.png
Call transcription directly as:
{"op":"speech.transcribe","input_path":"raw/clip.mp4","transcript_path":"project/transcripts/clip.json","timestamps":"word"}
These script/tool calls return JSON. Their output is the evidence for project/ingest.json.
How to call the plan validator
Use the skill script, not a deprecated direct video_plan tool:
"$ORKAS_NODE" "$ORKAS_PC_DIR/bin/run-skill.cjs" stage-plan video_plan -- --op validate --plan project/plan.json
"$ORKAS_NODE" "$ORKAS_PC_DIR/bin/run-skill.cjs" stage-plan video_plan -- --op promise_check --plan project/plan.json
"$ORKAS_NODE" "$ORKAS_PC_DIR/bin/run-skill.cjs" stage-plan video_plan -- --op summarize --plan project/plan.json
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 208 lines · 2 tokens per session scan A 391cf257f740
stage-plan is a skill published in the GitHub repository Orkas-AI/Orkas (1,848 stars, last pushed today), licensed MIT. It adds 2 tokens to every session and 6,170 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.
Other skills, from other repositories
video-editing-planner
Suggests editing structure, scene cuts, transitions, and pacing for improved video content quality and engagement.
video-script-generator
Generates video scripts with hooks, structured sections, pacing, and call-to-actions optimized for engagement and retention.
caption-subtitle-formatter
Formats captions and subtitles for readability, timing, and accessibility across videos.
infographic-builder
Turns textual content into structured infographic formats suitable for reports, presentations, and educational materials.
recording
Capture screen recordings and screenshots on any registered computer (macOS, Windows, Linux, HarmonyOS) and manage the recording library.
deepchat-cli
Use DeepChat's bundled CLI control plane for model inference, image/video/speech generation, transcription, OCR, artifact inspection, public configuration, Skills, and MCP operations. Activate when a user asks to invoke DeepChat capabilities that are not already exposed as a more specific tool, compare models, run a…