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/michielinksee/bantou/classifygit clone --depth 1 https://github.com/michielinksee/bantouWrote 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/michielinksee/bantou/classify)<a href="https://agentmods.dev/commands/michielinksee/bantou/classify"><img src="https://agentmods.dev/badge/commands/michielinksee/bantou/classify.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 | $0.00016 | $0.00501 |
| Opus 5 | $0.00008 | $0.00251 |
| Sonnet 5 | $0.00003 | $0.00100 |
| Haiku 4.5 | $0.00002 | $0.00050 |
Grade A, and why
classify 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
/bantou:classify
Classify a single Japanese business transaction through the full 3-stage pipeline.
Usage
/jp-accounting:classify <description>
Examples
/jp-accounting:classify スタバ 渋谷店 ¥580
/jp-accounting:classify Amazon Web Services ¥45,000
/jp-accounting:classify タクシー 新宿→品川 ¥3,200
/jp-accounting:classify 取引先接待 居酒屋 6名 ¥48,000
Input
A free-text transaction description. Can include:
- Vendor/store name
- Amount (optional but improves accuracy)
- Location or context details
- Number of participants (relevant for 会議費 vs 交際費)
Pipeline execution
The command runs the full classification pipeline:
- Exclusion check — Is this a classifiable transaction?
- Memory recall — Has this vendor been corrected before?
- Keyword match — Does it match a known category pattern?
- AI classification — Claude analyzes the description (if needed)
Output
Returns a structured result:
勘定科目: 会議費
税区分: 課税仕入 10%
信頼度: 92%
判定方法: Memory (過去の修正パターン一致)
If confidence is below 70%, the output shows the top-2 candidates:
勘定科目: 会議費 or 交際費 (要確認)
税区分: 課税仕入 10%
信頼度: 58%
判定方法: AI (低信頼度 — 税理士確認推奨)
候補1: 会議費 (58%) — 1人あたり¥8,000、5,000円基準超過の可能性
候補2: 交際費 (35%) — 接待目的の場合
Notes
- If the transaction is excluded (Stage 0), the exclusion reason is shown
- Classification results are not automatically saved; use
/jp-accounting:correctto submit corrections if the result is wrong
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 · 70 lines · 16 tokens per session scan A aa25a55fe925
classify is a command published in the GitHub repository michielinksee/bantou (2 stars, last pushed 13d ago), licensed MIT. It adds 16 tokens to every session and 501 once invoked, about $0.0001 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.
Other commands, from other repositories
jaz-migrate
Migrate accounting data to Jaz from Xero, QuickBooks, Sage, MYOB, or Excel — guided multi-step conversion workflow.
jaz-tax-sg
Run Singapore Form C-S / C-S Lite corporate income tax computation using Jaz data — pull P&L, classify add-backs, compute capital allowances, apply exemptions.
jaz-audit-prep
Compile an audit preparation pack in Jaz — generate reports, schedules, and reconciliations for auditor or tax agent.
jaz-gst
Prepare GST/VAT filing in Jaz — review tax ledger, identify discrepancies, generate filing summary.
jaz-payment-run
Run a batch payment processing workflow in Jaz — select outstanding bills by due date, process payments in bulk.
ingest
Ingest financial data (bank/credit-card statements, CSV exports, receipts, or a plain-text description) into the book as validated double-entry transactions.