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 geo-probegit 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/geo-probe)<a href="https://agentmods.dev/skills/orkas-ai/orkas/geo-probe"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas/geo-probe/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/geo-probe"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas/geo-probe.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 | $0.01647 |
| Opus 5 | $0.00002 | $0.00823 |
| Sonnet 5 | $0.00001 | $0.00329 |
| Haiku 4.5 | $0.00000 | $0.00165 |
Grade A, and why
geo-probe scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
geo-probe
Measure whether AI answer engines surface a brand. Split because a skill can't reach the model providers: this skill generates the queries and scores the answers; the agent calls the model / web_search for each query and feeds the answers back.
When to use
- A GEO/visibility pass: "do AI engines mention or cite us, and how do we compare to competitors?"
- After GEO fixes, re-probe to see if mention/citation rates moved.
When NOT to use
- On-page GEO readiness (citability/structure/entity) — that is
geo-score, which needs no model calls. - When you can't make model calls — without answers,
scorehas nothing to measure.
Preconditions
queries: aseo-crawlJSON.score: an answers payload (below). Python 3.9+ stdlib only. The agent provides model answers between the two ops.
How to call
- Generate queries:
"$ORKAS_NODE" "$ORKAS_PC_DIR/bin/run-skill.cjs" geo-probe geo_probe -- --op queries --input <crawl.json> [--brand X] [--domain x.com] [--competitors "A,B"]
→ { ok, data: { brand, domain, competitors, context_terms, queries:[{query, kind, intent}] } }
kind is unbranded (the measurement set) or branded (control). A branded
query cannot measure visibility — asked "What is ?" a model names the
brand by construction, so counting those rows reports a share of voice the probe
never tested. Branded rows are kept only to separate "nobody recommends us" from
"the model does not know we exist"; keep their kind when you feed answers back.
1b) Validate agent- or user-supplied candidates before probing with them:
echo '{"brand":"Orkas","domain":"orkas.ai","candidates":["best ai agent tools for teams","..."]}' | "$ORKAS_NODE" "$ORKAS_PC_DIR/bin/run-skill.cjs" geo-probe geo_probe -- --op filter
→ { ok, data: { kept:[...], rejected:[{query, reason}] } }. Rejects a query that
carries the brand or domain core, duplicates, is under 3 or over 12 words, uses a
bare ambiguous acronym (GEO, AEO, CRM… without its expansion — an answer
engine will answer for the wrong industry), or compares AI answer engines rather
than vendors. Every drop names its reason; never discard one silently.
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.
- today Changed · +19 lines 6d875a5d6b13
- 12d ago First seen · 72 lines · 3 tokens per session scan A 57ffd098fd52
geo-probe is a skill published in the GitHub repository Orkas-AI/Orkas (1,911 stars, last pushed yesterday), licensed MIT. It adds 3 tokens to every session and 1,647 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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