An AI-powered scanner that analyzes emails and bills to identify subscription traps, hidden refunds, and savings opportunities.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install claimback-radar
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install claimback-radar using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Claimback Radar?
Claimback Radar is a specialized utility within the Openclaw Skills library designed to scan user emails and billing statements for hidden financial opportunities. It uses advanced language models to parse unstructured communication, identifying subscription traps and potential refunds that would otherwise go unnoticed by the average consumer. By integrating this skill into your workflow, you can automate the process of financial auditing and ensure you never miss a chance to reclaim your money or optimize your recurring expenses.
This skill serves as a bridge between messy, unstructured financial data and structured, actionable intelligence. Whether you are dealing with obscure service fees or forgotten trial periods, Claimback Radar provides the technical foundation to regain control over your digital subscriptions and financial commitments through the power of Openclaw Skills.
Claimback Radar Use Cases
- Scanning high volumes of email archives to find eligibility for service-outage refunds.
- Analyzing monthly credit card statements to detect and flag recurring subscription traps.
- Generating structured JSON billing data from messy, unstructured text or PDF exports.
- Identifying high-risk financial commitments based on specific user context and historical data.
How Claimback Radar Works
- The user provides raw text input, such as the body of a confirmation email or a digital bill, to the skill.
- The extract function processes the unstructured text to generate a structured confirmation card.
- The detect_and_recommend function evaluates the extracted data against the user's specific context to identify risks or savings.
- The system generates actionable receipts and risk flags, providing a clear path for the user to claim refunds or cancel services.
Claimback Radar Setup
To get started with this skill, you must ensure your environment is configured correctly. Follow these steps to set up Claimback Radar within your Openclaw Skills environment:
# Export your OpenAI API key as an environment variable
export OPENAI_API_KEY="your_api_key_here"
# Alternatively, create a .env file in the skill directory
echo "OPENAI_API_KEY=your_api_key_here" > .env
# Run the main entrypoint to begin scanning
python main.py
Claimback Radar Data Schema & Taxonomy
Claimback Radar organizes its output into structured formats to ensure compatibility with other Openclaw Skills. Below is the metadata taxonomy used:
| Data Component | Description | Format |
|---|---|---|
| Raw Input | The unstructured text from emails or bills | String |
| confirmation_card | Extracted subscription and billing data | JSON |
| action_receipts | Specific steps or recommendations for the user | Markdown/JSON |
| risk_flags | Automated indicators of traps or high costs | List |
Claimback Radar Advanced Features
- Direct integration with OpenAI's API for high-accuracy extraction of complex financial data.
- Secure API key management supporting both environment variables and direct injection for production environments.
- Modular function design allowing developers to run extraction and recommendation steps independently.
- Built-in security warnings to prevent the accidental leakage of sensitive credentials in version control.
- Seamless interoperability with the broader ecosystem of Openclaw Skills for multi-agent financial management.
SKILL.md
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