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 agent-creatorgit 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/agent-creator)<a href="https://agentmods.dev/skills/orkas-ai/orkas/agent-creator"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas/agent-creator/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/agent-creator"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas/agent-creator.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 | $0.01339 |
| Opus 5 | $0.00002 | $0.00669 |
| Sonnet 5 | $0.00001 | $0.00268 |
| Haiku 4.5 | $0.00000 | $0.00134 |
Grade A, and why
agent-creator 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
agent-creator
Create or edit a custom Agent by emitting one inline <agent>...</agent> container in the final reply. The host parses and applies the container; do not call a mutation tool or write agent.json directly.
Read only the reference needed for this request
The root file is the execution gate. Before emitting a container, read the matching reference through this Skill's read ref:
- New LLM-managed Agent, or an edit to its name, icon, description, workflow, guidance, Skills, tools, inputs, interaction mode, or category → llm-agent-fields.md.
- Creation from supplied files/prompts/specs, conversation crystallization, or an unbound edit of an existing Agent → also read source-and-editing.md. Current-turn requirements for inputs, checks, workflow, outputs, or non-goals count as a supplied spec, so this reference is required.
- CLI-backed Agent edit or a request about its external runtime boundary → read cli-and-prose.md.
Do not read unrelated references. A simple bound edit normally needs only llm-agent-fields.md; a CLI-backed edit normally needs only cli-and-prose.md.
Non-negotiable protocol
- Mutation only via the inline container. Never use
edit_file,write_file, orbashto changeagent.json. Never dump the container into a workspace file. - No container, no mutation, no success claim. Without a valid container, describe the result only as a proposal, clarification, or blocker; never say the Agent is ready, created, updated, installed, or available. A valid container only requests a mutation; only the host result proves it was applied. Until then, use pending wording such as “将创建” or “已提交,等待应用”.
- One container per Agent. Multiple containers are legal only when the user asked to create or edit multiple distinct Agents, and the number of valid containers must equal the number of Agents you claim to have created. End the turn after emitting them; do not dispatch the Agent.
- Validate every container independently before sending. Read its tags in order: every opening tag must have the exact matching closing tag, with no crossed, missing, or reused closer. For a multi-Agent reply, repeat this structural check for each container rather than copying an unchecked suffix.
- Read attachment sources. For an injected attachment, missing inline body text is not a missing source; call
read_fileswith the exact supplied path inpathsand ask for another upload only if that read fails. - Use the user's current UI language for display copy and user-visible prose. Keep XML tags, tool/Skill names, paths, JSON keys, and closed-set values unchanged. Use localized description tags only when the user explicitly requests multilingual descriptions.
- Keep user-visible prose outcome-based. State what the Agent does, when to use it, and the substantive change; do not expose XML/field/schema/id mechanics or source provenance by default.
- Agent roles are not model runtimes. Do not imitate or claim an unavailable provider by naming an ordinary role Agent after a model. Real external CLI runtimes are handled by their smaller editable contract.
- Do not hard-code other Agent display names in authored content; describe downstream roles by capability.
- Agent workflows invoke Skills by their visible names, not Skill files or installation paths. List every invoked Skill in
<skills>. Built-in tool names belong to<tools>, never<skills>. - Keep runtime guidance bounded. Use at most 5 items per field.
- Run the category sanity check. Do not accept an invalid source category or silently fall back to
generalwhen one domain clearly dominates.
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.
- 8d ago First seen · 59 lines · 3 tokens per session scan A d9df1fdcafb0
agent-creator is a skill published in the GitHub repository Orkas-AI/Orkas (1,885 stars, last pushed yesterday), licensed MIT. It adds 3 tokens to every session and 1,339 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 2026-09-03.
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