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 gate-controlgit 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/gate-control)<a href="https://agentmods.dev/skills/orkas-ai/orkas/gate-control"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas/gate-control.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 122 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 155 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.02575 |
| Opus 5 | $0.00001 | $0.01288 |
| Sonnet 5 | $0.00000 | $0.00515 |
| Haiku 4.5 | $0.00000 | $0.00258 |
Grade A, and why
gate-control 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 8d 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 — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
gate-control
This is the single canonical policy for VideoStudio gate decisions, post-gate edits, and recovery across COMPOSE, AUTO, GENERATE, and EDIT. Line Skills describe artifacts and production operations but never create a competing authorization policy.
Decision kernel — apply this before the detailed rules
Resolve every turn from the latest durable facts:
- Identify the current candidate and pending user decision. A pending decision is status, not permission to ask again.
- Classify the dependency. Only a genuine user decision about a creative or delivery choice, or a new billable attempt, is a user dependency. Missing files, stale locators, parameter-shape errors, interrupted operations, equivalent metadata changes, and QA failures with passes remaining are system work and do not justify stopping. An exhausted visual-QA cycle is a creative fork: show its evidence/options and wait.
- Choose one result-aware execution horizon. Execute only operations whose preconditions are true. If one result decides the branch, stop the horizon at that operation, inspect it, and select the next operation in the same turn.
- Use exact interfaces. In
calls, keep the exact native operation token or named Skill/file operation. Do not add avideo_studio.prefix, invent generic recovery/status, or replace an operation with prose. Include mandatory bindings such as AUTO child'scomposition.approve_plan,plan_path, andsegment_id. - End in exactly one place: another immediately executable operation, one of the five stopping decisions with its current artifact, completed delivery, or an external uncertainty boundary with the current artifact and concrete options. Showing an artifact is not an ending except at those five stops.
Direction-to-plan handoff
Keep the first two stops mechanically distinct:
- Direction stop: route and lock facts already settled by the brief, then
show two or three concepts without writing a manifest, script, narration copy,
or art direction. Use the exact localized name from the table below; in
Chinese this is
制作方向确认, never a renamed label. - COMPOSE plan stop after direction choice: call
speech.capabilities, write the canonicalproject/composition/composition-manifest.jsonfrom the chosen concept, then run the freecomposition.check_narration_fit; only after those concrete results present the one制作方案确认/Production plan confirmation.
What ships with it
5 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.
- 8d ago First seen · 216 lines · 2 tokens per session scan A 6cd5a2f54cb6
gate-control is a skill published in the GitHub repository Orkas-AI/Orkas (1,776 stars, last pushed today), licensed MIT. It adds 2 tokens to every session and 2,575 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.
infographic-builder
Turns textual content into structured infographic formats suitable for reports, presentations, and educational materials.
caption-subtitle-formatter
Formats captions and subtitles for readability, timing, and accessibility across videos.
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…