Overview
- Skill Key
- andreolf/refund-radar
- Author
- andreolf
- Source Repo
- openclaw/skills
- Version
- -
- Source Path
- skills/andreolf/refund-radar
- Latest Commit SHA
- 2685dbdb87e34ef3e8443354d96cdf875af5958f
Official OpenClaw
rules
54%
refund-radar
Scan bank statements to detect recurring charges, flag suspicious transactions, and draft refund requests with interactive HTML reports.
Stars
0
Installs
0
Status
ACTIVE
Visibility
PUBLIC
安装方式
直接复制以下提示词，发送给你的 AI 助手即可完成安装。
请先检查是否已安装 SkillHub 商店，若未安装，请根据 https://skillhub-1388575217.cos.ap-guangzhou.myqcloud.com/install/skillhub.md 安装 SkillHub 商店，然后安装 refund-radar 技能。 若已安装，则直接安装 refund-radar 技能。
Extracted Content
SKILL.md excerpt
# refund-radar Scan bank statements to detect recurring charges, flag suspicious transactions, identify duplicates and fees, draft refund request templates, and generate an interactive HTML audit report. ## Triggers - "scan my bank statement for refunds" - "analyze my credit card transactions" - "find recurring charges in my statement" - "check for duplicate or suspicious charges" - "help me dispute a charge" - "generate a refund request" - "audit my subscriptions" ## Workflow ### 1. Get Transaction Data Ask user for bank/card CSV export or pasted text. Common sources: - Apple Card: Wallet → Card Balance → Export - Chase: Accounts → Download activity → CSV - Mint: Transactions → Export - Any bank: Download as CSV from transaction history Or accept pasted text format: ``` 2026-01-03 Spotify -11.99 USD 2026-01-15 Salary +4500 USD ``` ### 2. Parse and Normalize Run the parser on their data: ```bash python -m refund_radar analyze --csv statement.csv --month 2026-01 ``` Or for pasted text: ```bash python -m refund_radar analyze --stdin --month 2026-01 --default-currency USD ``` The parser auto-detects: - Delimiter (comma, semicolon, tab) - Date format (YYYY-MM-DD, DD/MM/YYYY, MM/DD/YYYY) - Amount format (single column or debit/credit) - Currency ### 3. Review Recurring Charges Tool identifies recurring subscriptions by: - Same merchant >= 2 times in 90 days - Similar amounts (within 5% or $2) - Consistent cadence (weekly, monthly, yearly) - Known subscription keywords (Netflix, Spotify, etc.) Output shows: - Merchant name - Average amount and cadence - Last charge date - Next expected charge ### 4. Flag Suspicious Charges Tool automatically flags: | Flag Type | Trigger | Severity | |-----------|---------|----------| | Duplicate | Same merchant + amount within 2 days | HIGH | | Amount Spike | > 1.8x baseline, delta > $25 | HIGH | | New Merchant | First time + amount > $30 | MEDIUM | | Fee-like | Keywords (FEE, ATM, OVERDRAFT) + > $3 | LOW | | Currency A...
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