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 seo-schemagit 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/seo-schema)<a href="https://agentmods.dev/skills/orkas-ai/orkas/seo-schema"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas/seo-schema/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/seo-schema"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas/seo-schema.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 | $0.00704 |
| Opus 5 | $0.00001 | $0.00352 |
| Sonnet 5 | $0.00000 | $0.00141 |
| Haiku 4.5 | $0.00000 | $0.00070 |
Grade A, and why
seo-schema scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
seo-schema
Lint existing JSON-LD and generate templates. Pure analysis/templating — no network.
When to use
- The diagnose flow wants structured-data findings + which schema types the page should add.
- The apply/content flow needs a paste-ready JSON-LD snippet for a type.
When NOT to use
- Coarse "has any structured data?" —
seo-tech-auditalready flags that. This goes deeper (per-node lint, recommendations, generation). - Writing the JSON-LD into source — the agent does that (with this skill's generated snippet).
Preconditions
- For
validate: aseo-crawlJSON (uses each page's parsedstructured_data). Python 3.9+ stdlib only.
How to call
Validate existing JSON-LD + recommend types:
"$ORKAS_NODE" "$ORKAS_PC_DIR/bin/run-skill.cjs" seo-schema schema -- --op validate --input <crawl.json> [--out <schema.json>]
Generate a template (for apply/content mode):
"$ORKAS_NODE" "$ORKAS_PC_DIR/bin/run-skill.cjs" seo-schema schema -- --op generate --type Organization [--json '{"name":"Orkas","url":"https://orkas.ai"}']
Expected output
validate:
{ "ok": true, "data": {
"schema_score": 96, "present_types": ["Organization"], "recommended_types": ["WebSite"],
"findings": [ { "id": "schema_recommend", "dimension": "schema", "severity": "low", ... } ],
"summary": { "total": 1 }, "meta": { "url": "..." } } }
Findings use dimension: "schema" and feed seo-report --add.
generate: { "ok": true, "data": { "jsonld": { "@context": "https://schema.org", "@type": "Organization", ... } } }. Emit the jsonld object as a <script type="application/ld+json"> block; the JSON-LD must match the visible page one-to-one (esp. FAQ Q&A). Failure: {"ok": false, "error": "..."}, non-zero exit.
Lint coverage
Missing @type; missing required fields (Organization/WebSite/SoftwareApplication/Article/FAQPage/BreadcrumbList/Product/HowTo); deprecated rich-result types (FAQPage/HowTo still valid markup but no rich result for most sites); recommended types by page role (home → Organization+WebSite; deep page → BreadcrumbList). @graph is expanded.
What ships with it
2 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 · 54 lines · 2 tokens per session scan A 2c12100e17bb
seo-schema is a skill published in the GitHub repository Orkas-AI/Orkas (1,848 stars, last pushed yesterday), licensed MIT. It adds 2 tokens to every session and 704 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.
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