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Agent Skill · 效率

expense-categorization(费用分类)

expense-categorization 用于补充效率相关能力,适合在 OpenClaw 中需要让 Agent 承接效率相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

expense-categorization 是效率类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描有提醒项,按安装量在效率类的 21,003 个 Skill 里排第 6,525。

总安装 9,221周安装 373GitHub Stars 公开资料未说明下载 2,894核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.1

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:expense-categorization(费用分类)
来源仓库:https://github.com/samledger67-dotcom/expense-categorization
安装命令:
openclaw skills install expense-categorization
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install expense-categorization

这个 Skill 是做什么的

效率类 Skill 把重复的整理、汇总、转换工作交给助手完成,减少手工复制粘贴。

从收据或报表中提取费用并对其进行分类,映射到 GL 代码,检查是否符合政策,并标记异常情况以供审查。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install expense-categorization;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

expense-categorization 怎么安装?

在终端执行 openclaw skills install expense-categorization。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

expense-categorization 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

expense-categorization 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

expense-categorization 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

expense-categorization 用的人多吗?

公开数据是累计安装 9,221 次,近一周安装 373 次,下载 2,894 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

expense-categorization 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理效率类任务、又不想每次都手写提示词的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

效率类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
github

GitHub 代码协作 · OpenClaw

7,632,918545需要联网MIT-0通过
nano-pdf

Nano PDF 文档 · OpenClaw

4,249,476231需要联网MIT-0通过
auto-updater

自动更新程序 · OpenClaw

2,964,063386操作浏览器MIT-0通过
video-frames

视频帧 · OpenClaw

2,621,304112需要联网MIT-0通过
gemini

Gemini 助手 · OpenClaw

2,547,39648需要联网MIT-0有提醒
expense-categorization

费用分类 · 本页

9,221—需要联网MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
expense-categorization
description
Receipt OCR, GL code mapping, policy compliance checking, and anomaly detection for business expenses. Use when you need to: (1) extract data from receipt images or PDFs via OCR, (2) map expenses to chart of accounts or GL codes, (3) check receipts against expense policies (per diem limits, category restrictions, required fields), (4) detect anomalies like duplicates, out-of-policy amounts, missing receipts, or unusual vendors, (5) batch-process expense reports for approval routing, (6) categorize credit card transactions into accounting categories. Works with QBO chart of accounts, generic GL structures, or custom category lists. NOT for: tax filing or PTIN-backed services, payroll processing, or real-time bank feed categorization without human review.

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 <0.7 → always route to human review, never auto-post

平台分布

OpenClaw

74.92%
按下载量换算2,168

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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