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 michielinksee/bantou --skill monthly-closergit 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/skills/michielinksee/bantou/monthly-closer)<a href="https://agentmods.dev/skills/michielinksee/bantou/monthly-closer"><img src="https://agentmods.dev/badge/skills/michielinksee/bantou/monthly-closer/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/michielinksee/bantou/monthly-closer"><img src="https://agentmods.dev/badge/skills/michielinksee/bantou/monthly-closer.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 | $0.00025 | $0.00523 |
| Opus 5.5 | $0.00010 | $0.00209 |
| Sonnet 5.5 | $0.00005 | $0.00105 |
| Haiku 4.5 | $0.00003 | $0.00052 |
Grade A, and why
monthly-closer 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 2mo 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
Monthly Closer
Generate structured monthly closing reports for client companies. Produces a Markdown summary with transaction statistics, category breakdowns, and anomaly detection.
When to use
Invoke this skill when:
- End of month arrives and the tax accountant needs a closing summary
- A client requests a monthly financial overview
- The firm needs to review classification quality for the period
Report structure
The generate_monthly_report tool produces a report with these sections:
1. Summary header
- Company name, reporting period (year/month)
- Generation timestamp
- Data source (freee / Yayoi / MF / imported CSV)
2. Transaction statistics
- Total transaction count
- Total expenditure (支出合計)
- Total revenue (収入合計)
- Classification breakdown: auto-classified vs. manually reviewed
- Memory hit rate for the period
3. Category breakdown (勘定科目別集計)
Table showing each category with:
| 勘定科目 | 件数 | 金額 | 構成比 |
|---|---|---|---|
| 旅費交通費 | 45 | ¥234,500 | 18.2% |
| 会議費 | 32 | ¥128,000 | 9.9% |
| ... | ... | ... | ... |
4. Month-over-month anomaly detection
Compares each category against the previous month. Flags any category where the amount changed by plus or minus 50% or more.
Example flags:
- "広告宣伝費: ¥580,000 (前月比 +210%) — 要確認"
- "外注費: ¥45,000 (前月比 -65%) — 要確認"
Anomalies are not errors — they are prompts for the tax accountant to verify that large swings are expected.
5. Classification quality metrics
- Stage 0 exclusion count
- Stage 1 keyword/memory hit count
- Stage 2 AI classification count
- Items requiring manual review (low confidence)
Output format
Reports are generated as Markdown by default. The output can be:
- Displayed directly in the terminal
- Saved to a file for archival
- Shared with clients via email or converted to PDF
MCP tools used
generate_monthly_report— produce the full monthly summary
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.
- 2mo ago First seen · 77 lines · 25 tokens per session scan A 0a687de2acda
monthly-closer is a skill published in the GitHub repository michielinksee/bantou (2 stars, last pushed 2mo ago), licensed MIT. It adds 25 tokens to every session and 523 once invoked, about $0.0001 per session on Opus 5.5. 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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