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 image-generategit 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/image-generate)<a href="https://agentmods.dev/skills/orkas-ai/orkas/image-generate"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas/image-generate/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/image-generate"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas/image-generate.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 | $0.02186 |
| Opus 5 | $0.00002 | $0.01093 |
| Sonnet 5 | $0.00001 | $0.00437 |
| Haiku 4.5 | $0.00000 | $0.00219 |
Grade A, and why
image-generate 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 9d 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image Generate
Read this skill only after route lock when the task needs synthesized or content-aware reconstructed pixels: GENERATE, semantic EDIT, or the raster phase of HYBRID. Do not load it for COMPOSE, deterministic transforms, or overlays. It governs direct generate_image calls and the stable host workflow kernel; engine-specific request formats and reusable graphs remain private skill resources rather than new application tools.
This production skill is the earliest point at which a manifest for a direct GENERATE or EDIT task may be created. Initialize image-manifest.json from the Canonical image-manifest v1 block in image-router; it is the single structural source. Replace the locked route and budget, then add the reference and generation fields below. Do not reconstruct the base schema from memory.
Generation rules
- Respect
generation_budget.max_callswithin the current user turn. The host reapplies this limit independently for each new user turn while retaining older transactions only as audit history.COMPOSEhas a budget of zero;HYBRID,GENERATE, andEDITnormally use one call. Set a two-call budget only when the user explicitly requests iterative refinement or authorizes another billable attempt. A review finding alone never authorizes that second call. - Generate at the final aspect ratio whenever the provider supports it. Do not rely on destructive cropping to fix a mismatched composition.
- Use reference images only when they encode composition, identity, product fidelity, or edit continuity that the prompt cannot preserve reliably.
- Mirror manifest references into
generate_image.reference_bindingsusing the exact concatenated reference order. Include role, strength, preserve, may-change, and target region. Sendgeneration_contract.negative_promptseparately. - Compile
reference_intent.modeinto the request: reproduction asks for the declared protected attributes to match; guide limits influence to declared roles; editing uses the requirededit_sourceas the original. User-declared instructions and boundaries have priority over inferred defaults and provider convenience. - Treat every planned
output_pathas one stable artifact binding. Reuse that exact string byte-for-byte for the provider/workflow result, laterproject.inspect, optional review candidate, and export input; never retype, derive, or silently correct the path in a downstream handoff. - For edits, send the manifest instructions, protected regions, unchanged attributes, and allowed changes explicitly. Prefer a localized edit over regenerating the full scene, and never reinterpret an edit as a fresh variation.
- For reproduction, do not silently change subject, crop, layout, identity, typography, palette, or other preserved axes merely because regeneration is easier. Choose the provider/control path that can honor the declared fidelity floor.
- For hybrid work, generate only the difficult raster layer. Exclude final copy, logos, labels, diagrams, and UI text from the model request.
- Save every generated output inside the image project and record its relative path in
image-manifest.jsonbefore inspection.
What ships with it
1 file 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.
- 9d ago First seen · 85 lines · 3 tokens per session scan A bb269b81212b
image-generate is a skill published in the GitHub repository Orkas-AI/Orkas (1,808 stars, last pushed yesterday), licensed MIT. It adds 3 tokens to every session and 2,186 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…