receipt-processing — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited receipt-processing (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Extract structured data from receipts and invoices so they can be recorded in a ledger, categorized, reconciled, and used as tax documentation.
This skill is operational. Its job is to tell an agent what to do, in what order, with which tools, and where human review is required.
Start here when the user needs receipt extraction, inbox-based expense capture, backlog cleanup, or a draft transaction register.
Use tools in this order:
Do not start with browser automation or manual data entry if an email-native extractor can supply the source material faster and with better provenance. Receiptor AI is one example when available.
scripts/receipt_summary.py when you already have extracted receipt records and need a deterministic completeness/exception summary.Before extracting anything, determine:
If the user has not specified output format, default to a reviewable table plus JSON or CSV.
Use an email-native extraction tool first when receipts are in email. Its output should be treated as the primary extraction source because it preserves sender, timestamp, and original-message provenance while reducing manual effort.
Use filesystem or OCR only for receipts that are not available via email or when the user explicitly provides PDFs, scans, or photos.
Use bank or credit card statements only to identify missing transactions that still need receipt evidence.
For each receipt, produce at minimum:
vendor_namedatetotal_amountcurrencyAlso capture whenever available:
subtotaltax_amountpayment_methodreceipt_numberline_itemssource_typesource_referenceconfidenceWhen the vendor is ambiguous, prefer the merchant or seller name over the payment processor.
Apply these checks:
If a record fails any required-field check, route it to a review queue rather than fabricating missing data.
Safe to automate without asking first:
Require explicit human confirmation before:
Deliver one or more of:
Always include a processing summary:
Receipts processed: N
Complete: N
Needs review: N
Potential duplicates: N
Date range: ...
Total amount: ...
Sources: email / PDF / photo / exportAfter extraction:
expense-categorizationbank-reconciliation to close gaps against statementsmonthly-close or tax-prep once the transaction register is trustworthyskill: receipt-processing
version: 3.0
default_output: review-table + json-or-csv
automation_boundary: extract-and-draft
approval_required_for:
- posting low-confidence records
- deleting duplicates
- substituting statements for receipts
next_steps:
- expense-categorization
- bank-reconciliation~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.