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 gokulb20/crewm8-cfo-skills --skill transaction-processinggit clone --depth 1 https://github.com/gokulb20/crewm8-cfo-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/gokulb20/crewm8-cfo-skills/transaction-processing)<a href="https://agentmods.dev/skills/gokulb20/crewm8-cfo-skills/transaction-processing"><img src="https://agentmods.dev/badge/skills/gokulb20/crewm8-cfo-skills/transaction-processing/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/gokulb20/crewm8-cfo-skills/transaction-processing"><img src="https://agentmods.dev/badge/skills/gokulb20/crewm8-cfo-skills/transaction-processing.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 | $0.00075 | $0.01633 |
| Opus 5 | $0.00037 | $0.00816 |
| Sonnet 5 | $0.00015 | $0.00327 |
| Haiku 4.5 | $0.00007 | $0.00163 |
Grade A, and why
transaction-processing 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 9d 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Transaction Processing
Purpose
Every financial transaction that hits the business — invoices received, expense reports, credit card charges, reimbursements — must be categorized, deduplicated, and flagged for anomalies before it reaches the general ledger. This skill ensures every dollar in and out is accounted for, properly categorized, and ready for posting, reducing bookkeeping errors and providing clean financial data for reporting.
When to Use
- "Process these transactions / receipts / invoices"
- "Categorize these expenses"
- "Enter these into the books"
- Upload of receipt images, CSV exports from bank/Stripe/QuickBooks, or raw transaction lists.
- "Reconcile and categorize this month's transactions"
Inputs Required
You must gather or receive:
- Transaction source data — CSV export, bank feed, API dump, or manual list. Must include: date, amount, counterparty, description.
- Chart of accounts — if not already known, load via
ledger-managementskill. - Previous period categorization — optional, for consistency checking.
Quick Reference
| Concept | What It Does | Why It Matters |
|---|---|---|
| Auto-categorization | Maps transaction descriptions to COA categories using rules | Ensures every transaction has a home without manual effort |
| Deduplication | Scans for duplicate amounts/vendor/date combinations | Prevents double-counting, the most common bookkeeping error |
| Anomaly detection | Flags outlier amounts, missing fields, round numbers | Catches errors and potential fraud before ledger posting |
| Confidence scoring | HIGH / MEDIUM / LOW per categorization | Surfaces items that need human review |
| Multi-currency handling | Records original + USD-equivalent | Required for accurate P&L and cash reporting |
Procedure
- Parse the transaction file: detect format (CSV, JSON, PDF table). Extract: date, amount, counterparty, description, payment method.
- Deduplicate: Check for duplicates against the existing transaction log. Flag any transaction that already appears.
- Auto-categorize each transaction using these rules:
- Software/subscriptions →
Software & Subscriptions - Payroll/contractors →
Payroll & Contractors - Rent/office →
Facilities - Marketing/ads →
Marketing - Travel/meals →
Travel & Entertainment - Legal/professional services →
Professional Services - Loan payments/interest →
Debt Service - Revenue from customers →
Revenue - Investments/grants →
Other Income - Everything else → assign best-guess category and flag for review.
- Software/subscriptions →
- Flag anomalies:
- Duplicate amounts to the same vendor in the same week.
- Transactions over an outlier threshold (3x the median of the same category).
- Round numbers that look like estimates rather than actuals.
- Missing counterparty or description.
- Prepare the output — a categorized transaction log.
- (Optional) Post to ledger — hand off to
ledger-management.
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.
- 9d ago First seen · 129 lines · 75 tokens per session scan A d355e6de51fd
transaction-processing is a skill published in the GitHub repository gokulb20/crewm8-cfo-skills (2 stars, last pushed 4mo ago), licensed MIT. It adds 75 tokens to every session and 1,633 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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