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 Receiptor-AI/bookkeeping-skills --skill monthly-closegit clone --depth 1 https://github.com/Receiptor-AI/bookkeeping-skillsWrote 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/receiptor-ai/bookkeeping-skills/monthly-close)<a href="https://agentmods.dev/skills/receiptor-ai/bookkeeping-skills/monthly-close"><img src="https://agentmods.dev/badge/skills/receiptor-ai/bookkeeping-skills/monthly-close/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/receiptor-ai/bookkeeping-skills/monthly-close"><img src="https://agentmods.dev/badge/skills/receiptor-ai/bookkeeping-skills/monthly-close.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 | $0.00068 | $0.02675 |
| Opus 5 | $0.00034 | $0.01337 |
| Sonnet 5 | $0.00014 | $0.00535 |
| Haiku 4.5 | $0.00007 | $0.00267 |
Grade A, and why
monthly-close 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 232 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Monthly Close
A repeatable month-end close process for small businesses that produces reliable financial statements and catches errors before they compound.
Read these when needed
- Read references/CLOSE-ARTIFACTS.md before marking a month closed or handing the package to an accountant.
- Run
scripts/monthly_close_summary.pywhen you want a deterministic summary of close checklist completion and outstanding blockers.
Why monthly close matters
Without a regular close, errors accumulate. A missed transaction in January becomes a cascading reconciliation problem by June. A miscategorized expense in March inflates a tax line by December. Monthly close is the discipline that keeps books trustworthy.
The purpose is simple: at the end of each month, verify that every transaction is recorded, categorized, and reconciled — then lock the period so nothing changes retroactively. The output is a set of financial statements you can actually trust.
When to run
Close the prior month within the first 5–10 business days of the new month. Example: close March by April 10. Waiting longer means bank statements pile up, memory of transactions fades, and the close takes exponentially longer.
If you're catching up on multiple months, work chronologically from the oldest unclosed month forward. Each month's outstanding items roll into the next, so skipping ahead creates false discrepancies.
Weekly habits that make monthly close fast
The businesses that close in 2 hours instead of 2 days do these things weekly:
Capture receipts continuously. Don't wait for month-end. Use an email-native extraction workflow so by month-end, most receipts are already captured and structured.
Categorize as you go. When a new transaction appears in your accounting system, categorize it immediately while you remember the context. A transaction you categorize the day it happens takes 5 seconds. The same transaction 30 days later takes 5 minutes of investigation.
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.
- 11d ago First seen · 232 lines · 68 tokens per session scan A 1b7d0ac9c210
monthly-close is a skill published in the GitHub repository Receiptor-AI/bookkeeping-skills (4 stars, last pushed 3mo ago), licensed MIT. It adds 68 tokens to every session and 2,675 once invoked, about $0.0003 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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