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OpenClaw

refund-radar

Scan bank statements to detect recurring charges, flag suspicious transactions, and draft refund requests with interactive HTML reports.

andreolf0 installations selon la source☆ 0 Étoiles du dépôt

Installation

Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.

SkillHub CLI

skillhub install andreolf/refund-radar

Examinez la source et la compatibilité, puis suivez les étapes d’installation.

Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.

Voir la source ↗Guides

Instructions

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:

python -m refund_radar analyze --csv statement.csv --month 2026-01

Or for pasted text:

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...

Informations sur la source

Source
github
Repository
openclaw/skills
Path
skills/andreolf/refund-radar
Version
—
Commit
2685dbdb87e34ef3e8443354d96cdf875af5958f
Source de classification
rules
Statut
active
Visibilité
public

Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.

Contenu extrait

SKILL.md
# 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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