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 Kokai-Data/japan-business-data --skill gbizinfo-company-searchgit clone --depth 1 https://github.com/Kokai-Data/japan-business-dataWrote 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/kokai-data/japan-business-data/gbizinfo-company-search)<a href="https://agentmods.dev/skills/kokai-data/japan-business-data/gbizinfo-company-search"><img src="https://agentmods.dev/badge/skills/kokai-data/japan-business-data/gbizinfo-company-search/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/kokai-data/japan-business-data/gbizinfo-company-search"><img src="https://agentmods.dev/badge/skills/kokai-data/japan-business-data/gbizinfo-company-search.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00080 | $0.00341 |
| Opus 5 | $0.00040 | $0.00170 |
| Sonnet 5 | $0.00016 | $0.00068 |
| Haiku 4.5 | $0.00008 | $0.00034 |
Grade A, and why
gbizinfo-company-search 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 12d 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
When to use
Use this skill when:
- The user provides a company name in any of these forms: Japanese kanji, katakana, hiragana, or Latin romanization (2+ characters).
- You need to identify the 13-digit 法人番号 before pulling detailed data.
How to invoke
Call the kokai MCP server's search_company tool:
{
"name": "search_company",
"arguments": {
"name": "<company name, 2+ chars>",
"limit": 5
}
}
The classifier accepts:
- Japanese kanji (株式会社…)
- Katakana (メルカリ など)
- Hiragana (かわぐち など)
- Latin (KAWAGUCHI / Mercari など)
Output format
For each candidate, present:
- Company name
- 13-digit 法人番号
- Location (本社所在地)
- Source URL (cite_required)
Confirm the candidate with the user before drilling deeper.
Boundary
- Output is signal / 確認材料 / context — NOT a decision.
- If multiple matches exist, present all and let the user disambiguate. Do not pick on their behalf.
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.
- 12d ago First seen · 49 lines · 0 tokens per session scan A 6d99965ae840
gbizinfo-company-search is a skill published in the GitHub repository Kokai-Data/japan-business-data (2 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 80 tokens to every session and 341 once invoked, about $0.0004 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 2026-08-31.
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