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 seb1n/awesome-ai-agent-skills --skill invoice-processinggit clone --depth 1 https://github.com/seb1n/awesome-ai-agent-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/seb1n/awesome-ai-agent-skills/invoice-processing)<a href="https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/invoice-processing"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/invoice-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/seb1n/awesome-ai-agent-skills/invoice-processing"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/invoice-processing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00033 | $0.01792 |
| Opus 5 | $0.00016 | $0.00896 |
| Sonnet 5 | $0.00007 | $0.00358 |
| Haiku 4.5 | $0.00003 | $0.00179 |
Grade A, and why
invoice-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 8d 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 — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Invoice Processing
Extract structured data from invoices, validate fields against purchase orders and vendor records, categorize line items by general ledger (GL) codes, and flag discrepancies for review. This skill handles both single invoice processing and batch operations, producing clean, auditable output ready for import into accounting systems.
Workflow
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Receive and Parse Invoice Accept invoice data in any format — raw text, OCR output, structured JSON, or CSV. Identify the document type (standard invoice, credit memo, debit note, proforma) and extract the header block: invoice number, date, due date, vendor name, vendor address, and payment terms.
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Extract Line Items and Totals Parse each line item for description, quantity, unit price, extended amount, and tax. Validate that line item totals sum to the stated subtotal. Extract tax amounts, shipping charges, discounts, and the grand total. Flag any arithmetic inconsistencies between line items and totals.
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Validate Against Purchase Order Match the invoice to its corresponding PO by PO number, vendor, or line item descriptions. Compare quantities and unit prices. Compute a three-way match score across PO, goods receipt, and invoice. Flag invoices where price variance exceeds a configurable threshold (default: 2%) or quantities don't match.
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Categorize by GL Code Assign each line item to the appropriate general ledger account based on item description, vendor category, and historical patterns. Common mappings include office supplies → 6200, software subscriptions → 6500, professional services → 6300, raw materials → 5100. Apply department cost center codes where applicable.
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Flag Discrepancies and Generate Output Produce a structured record for each invoice with all extracted fields, validation status, GL assignments, and any flags. Group flags by severity: blocking errors (missing PO, total mismatch > 5%), warnings (minor price variance, missing fields), and informational (new vendor, first invoice from vendor).
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.
- 8d ago First seen · 146 lines · 33 tokens per session scan A a579104f1ee8
invoice-processing is a skill published in the GitHub repository seb1n/awesome-ai-agent-skills (179 stars, last pushed 1mo ago), licensed MIT. It adds 33 tokens to every session and 1,792 once invoked, about $0.0002 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 2026-09-03.
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