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Expense Categorization

作者 samledger67-dotcom · GitHub ↗ · v1.0.1 · MIT-0
cross-platform ⚠ suspicious
299
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0
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1
当前安装
10
版本数
在 OpenClaw 中安装
/install expense-categorization
功能描述
Extract and categorize expenses from receipts or statements, map to GL codes, check compliance with policies, and flag anomalies for review.
使用说明 (SKILL.md)

Expense Categorization

Receipt OCR, GL mapping, policy compliance, and anomaly detection for business expenses.

Workflow

1. Receipt Extraction (OCR)

Use tesseract (local) or Vision API for image receipts; pdfplumber for PDF receipts.

Key fields to extract:

  • Vendor name, date, total amount, line items
  • Payment method (last 4 digits if visible)
  • Tax amount (HST/GST/sales tax)
  • Tips/gratuity (separate from subtotal)
# Tesseract OCR on receipt image
tesseract receipt.jpg stdout --psm 4 | python3 scripts/parse_receipt.py

# Or use Claude vision directly for complex layouts

For complex or handwritten receipts → use vision model with prompt in references/ocr-prompt.md.

2. GL Code Mapping

Map extracted expense category to chart of accounts. See references/gl-mapping.md for:

  • Standard QBO GL codes for common expense types
  • IRS-aligned categories (meals 50%, travel, home office, etc.)
  • Crypto/DeFi expense categories

Matching logic:

  1. Exact vendor name match (known vendor list)
  2. MCC code match (credit card transactions)
  3. Keyword match on description/line items
  4. Fallback: prompt user to select category

3. Policy Compliance Check

Apply policy rules before approval routing. See references/policy-rules.md for standard rules.

Core checks:

  • Per diem limits: Meals >$75 require itemized receipt; travel per diem by city
  • Receipt threshold: Receipt required for any expense ≥$25 (IRS standard)
  • Time limit: Receipts must be submitted within 30/60/90 days (configurable)
  • Duplicate detection: Same vendor + amount ± $1 within 7 days = flag
  • Split transactions: Same vendor, sequential dates, amounts just below approval threshold = flag

4. Anomaly Detection

Flag for human review:

  • Amount > 2× historical average for that vendor/category
  • Weekend or holiday transactions (especially travel/entertainment)
  • Round-number amounts (potential personal purchase)
  • Vendor in restricted list (casinos, adult entertainment, competitors)
  • Missing required fields (date, vendor, business purpose)
  • Out-of-state purchases for office supply categories

5. Output Format

{
  "receipt_id": "REC-20260315-001",
  "vendor": "Delta Air Lines",
  "date": "2026-03-15",
  "amount": 487.50,
  "currency": "USD",
  "gl_code": "6200",
  "category": "Travel - Air",
  "policy_status": "approved",
  "flags": [],
  "confidence": 0.94,
  "requires_review": false,
  "notes": "Business purpose required for reimbursement"
}

Batch Processing

For expense report batches:

# Process folder of receipts
import glob
receipts = glob.glob("receipts/*.{jpg,png,pdf}")
results = [categorize(r) for r in receipts]

# Summary stats
flagged = [r for r in results if r["requires_review"]]
total = sum(r["amount"] for r in results)
by_category = group_by(results, "category")

Output batch summary as CSV or feed directly to QBO via qbo-automation skill.

Common Patterns

Credit card statement import:

  1. Parse CSV/OFX from bank
  2. Match known vendors → auto-categorize
  3. Unknown vendors → ML classification or prompt
  4. Export mapped transactions to QBO

Expense report approval routing:

  • Auto-approve: policy-compliant, under $250, no flags
  • Manager approval: $250–$2,500 or single flag
  • Finance review: >$2,500, multiple flags, or restricted category

Mileage reimbursement:

  • Extract start/end locations + business purpose
  • Calculate at current IRS rate (check references/irs-rates.md)
  • Map to GL 6210 (Auto/Mileage)

Integration Points

  • qbo-automation: Push categorized transactions directly to QBO
  • crypto-tax-agent: Route DeFi/crypto expenses for cost basis tracking
  • kpi-alert-system: Trigger alerts when department spend exceeds budget
  • invoice-automation: Cross-reference receipts with vendor invoices

Negative Boundaries

  • Not for PTIN-backed tax work — categorization ≠ tax advice; defer to licensed preparer
  • Not for payroll — employee expense reimbursement != payroll processing
  • Not a real-time feed — batch review with human sign-off before posting to GL
  • Not for legal contracts — use contract-review-agent for vendor agreements
  • Confidence \x3C0.7 → always route to human review, never auto-post
安全使用建议
This skill appears to be what it says (expense OCR + GL mapping) but has gaps you should resolve before trusting it. Ask the publisher for the missing files referenced in SKILL.md (scripts/parse_receipt.py, references/ocr-prompt.md, references/irs-rates.md) and confirm whether any credentials are required to push transactions to QBO or other systems. If you plan to run it, ensure the environment has the expected OCR tools (tesseract, pdfplumber) and test with non-sensitive dummy receipts first. Require explicit human-review gates before any automatic posting to your accounting system, and verify how the skill handles and stores sensitive fields (card digits, tax IDs). If the author cannot provide the missing artifacts or a clear explanation of data flows and credential handling, treat the skill as unsafe to run in production.
功能分析
Type: OpenClaw Skill Name: expense-categorization Version: 1.0.1 The skill bundle facilitates expense categorization using high-risk capabilities such as shell command execution (Tesseract OCR) and file system access (glob) in SKILL.md. It references external components not included in the bundle, specifically 'scripts/parse_receipt.py' and 'references/ocr-prompt.md', which prevents a complete security review of the data processing logic. While the instructions appear aligned with a legitimate business purpose, the use of shell piping without explicit sanitization logic presents a potential command injection vulnerability if the agent processes untrusted filenames.
能力评估
Purpose & Capability
The skill's stated purpose (receipt OCR, GL mapping, policy checks, anomaly detection) matches the instructions. However, it references pushing results to other integrations (qbo-automation, crypto-tax-agent) which would normally require credentials and configuration; those credentials/configs are not declared in the package. Also some referenced helper assets (ocr-prompt.md, irs-rates.md, scripts/parse_receipt.py) are mentioned but not included.
Instruction Scope
SKILL.md tells the agent to run local commands (tesseract, pdfplumber, python scripts) and to use prompts in referenced files. The package contains no scripts and several referenced files are missing, so following the instructions would either fail or cause the agent to attempt to use system binaries and external vision models. The instructions also process sensitive data (payment digits, receipts) but do not specify secure handling or destinations beyond the other skills; that increases privacy risk if an agent were to send data to an unexpected endpoint.
Install Mechanism
No install spec (instruction-only) — low install risk. But the runtime steps depend on local binaries/libraries (tesseract, pdfplumber, python scripts) that are neither bundled nor declared, so the agent might attempt to call system tools that are absent or behave differently across environments.
Credentials
The skill requests no environment variables or credentials itself, which is proportionate for an instruction-only skill. However, it explicitly references integrations that would require credentials (QBO, crypto-tax-agent, bank/statement imports). Those are not declared here, leaving ambiguity about where and how sensitive credentials should be supplied and which skill is responsible for posting data.
Persistence & Privilege
No elevated privileges requested: always:false, no install writing to disk. The skill is user-invocable and can be invoked autonomously by the agent (platform default), which is expected for a skill of this type.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install expense-categorization
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /expense-categorization 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.1
Proper name and description (was published as TEST)
v9.9.9
test
v0.0.1
- Initial release of expense categorization skill. - Supports receipt OCR extraction, GL code mapping, expense policy compliance checks, and anomaly detection. - Enables batch processing of receipts and categorization of credit card transactions. - Integrates with QBO chart of accounts, custom GL structures, and external automation tools. - Outlines clear negative boundaries and review requirements for low-confidence categorizations.
v98.0.1
Corrected display name
v98.0.0
probe
v99.0.1
Corrected publish — restoring proper name
v99.0.0
test
v0.0.0-check
Initial release of the expense-categorization skill. - Provides receipt OCR, GL code mapping, expense policy compliance checks, and anomaly detection for business expenses. - Supports batch processing, output in structured format, and integration with QBO and related finance automation tools. - Includes detailed negative boundaries to clarify supported and unsupported use cases. - Outlines core workflow: OCR extraction, category/GL mapping, policy checks, anomaly flagging, and approval routing. - Suitable for various expense types including credit card transactions and mileage reimbursement.
v0.0.0-probe
Initial release (probe version) of the expense categorization skill: - Provides receipt OCR, GL code mapping, policy compliance checks, and anomaly detection for business expenses. - Supports batch-processing of receipts and expense reports, including summary output. - Enables integration with external systems such as QBO, crypto tracking, KPI alerts, and invoice reconciliation. - Defines clear boundaries: not for tax filing, payroll, or real-time uncategorized bank feeds. - Includes sample workflows, output formats, and routing logic for approvals and anomalies.
v1.0.0
Initial release
元数据
Slug expense-categorization
版本 1.0.1
许可证 MIT-0
累计安装 1
当前安装数 1
历史版本数 10
常见问题

Expense Categorization 是什么?

Extract and categorize expenses from receipts or statements, map to GL codes, check compliance with policies, and flag anomalies for review. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 299 次。

如何安装 Expense Categorization?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install expense-categorization」即可一键安装,无需额外配置。

Expense Categorization 是免费的吗?

是的,Expense Categorization 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Expense Categorization 支持哪些平台?

Expense Categorization 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Expense Categorization?

由 samledger67-dotcom(@samledger67-dotcom)开发并维护,当前版本 v1.0.1。

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