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 agentmods add commands/sophymarine/openregistry/namegit clone --depth 1 https://github.com/sophymarine/openregistryWrote 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/commands/sophymarine/openregistry/name)<a href="https://agentmods.dev/commands/sophymarine/openregistry/name"><img src="https://agentmods.dev/badge/commands/sophymarine/openregistry/name.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00036 | $0.00385 |
| Opus 5 | $0.00018 | $0.00192 |
| Sonnet 5 | $0.00007 | $0.00077 |
| Haiku 4.5 | $0.00004 | $0.00038 |
Grade A, and why
name 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 4d 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.
What it actually says
Check whether "$1" is free to register across jurisdictions [$2] using OpenRegistry's live government registry searches.
Step 0 — Target list
Parse comma-separated ISO codes. "all" = [GB, NO, IE, FR, DE, FI, CZ, PL, CH, US-NY] default top-10. Respect caller's tier fan-out cap (call about to check).
Step 1 — IoM native probe (if IM in list)
check_name_availability({ jurisdiction: "IM", query: "$1" }) — authoritative.
Step 2 — Pattern match every other jurisdiction
search_companies({ jurisdiction, query: "$1" }) per country. Classify:
- exact-match active → TAKEN
- exact-match dissolved → COOLING-OFF (GB 20yr, DE case-by-case, FR no wait)
- fuzzy substring → CONFUSINGLY-SIMILAR RISK
Step 3 — Sensitive-word flags "British"/"National"/"Royal" in GB (ministerial approval); "Bank"/"Bundes" in DE (BaFin gate); sector-specific regulated terms. Note: company-name availability ≠ trademark availability — recommend WIPO + national IPO searches as follow-up.
Step 4 — Per-jurisdiction report Status · Evidence (conflicting company_id if any) · Follow-ups (legal-form suffix, sensitive-word flag, TM check). Flag which countries had structured search (reliable) vs name-only (noisy).
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.
- 4d ago First seen · 26 lines · 36 tokens per session scan A 108e7036f248
name is a command published in the GitHub repository sophymarine/openregistry (17 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 36 tokens to every session and 385 once invoked, about $0.0002 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 commands, from other repositories
onboard
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onboard-interactive
Interactive customer onboarding with Step 0 independent verification (dialog-based, 17 stagegates).
monitor
AML transaction monitoring and suspicious activity reporting.
refresh
Periodic customer review with independent verification.
risk
Risk reassessment with updated due diligence.
screen
Comprehensive sanctions and PEP screening.