Identify and cluster the root causes of product returns to drive actionable prevention strategies and preserve business margins.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install refund-reason-cluster
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 refund-reason-cluster 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 Refund Reason Cluster?
The Refund Reason Cluster skill is a specialized analytical tool designed for AI agents to parse through messy refund logs, return reason text, and support transcripts. By leveraging Openclaw Skills, developers and e-commerce operators can transform raw post-purchase data into structured clusters categorized by quality, fit, shipping, or expectation gaps. This skill goes beyond simple categorization by providing root-cause hypotheses and identifying which refunds are avoidable versus unavoidable.
This utility is essential for businesses looking to minimize margin erosion caused by returns. It synthesizes disparate data points into an actionable executive summary, allowing teams to prioritize high-impact product fixes and update pre-purchase marketing copy to better align with customer expectations.
Refund Reason Cluster Use Cases
- Identifying patterns in rising refund rates to protect profit margins.
- Analyzing customer support transcripts to find recurring product dissatisfaction signals.
- Distinguishing between genuine product issues and policy fraud or abuse.
- Generating data-driven evidence for product development teams to fix manufacturing or design flaws.
How Refund Reason Cluster Works
- Normalize diverse refund reason text and map it to specific order and product attributes.
- Group reasons into specific cause types such as product quality, sizing issues, shipping delays, or unmet expectations.
- Evaluate the share of avoidable refunds versus unavoidable ones, including confidence scoring for the analyzed data.
- Generate a prioritized action plan featuring P0 to P2 tasks and a detailed 7-day execution roadmap.
Refund Reason Cluster Setup
To integrate this skill into your AI agent workflow using Openclaw Skills, ensure your environment has access to order metadata and refund logs. Use the following commands to get started:
# Import the skill into your Openclaw environment
openclaw add refund-reason-cluster
# Configure data access for refund logs and support transcripts
openclaw config set REFUND_DATA_PATH="./data/returns.csv"
Refund Reason Cluster Data Schema & Taxonomy
The skill organizes data into a structured hierarchy to ensure clarity between signals and actions:
| Component | Description | Examples |
|---|---|---|
| Input Source | Raw unstructured and structured logs | Refund logs, Return text, Support transcripts |
| Cause Clusters | High-level grouping of issues | Quality, Fit, Shipping, Expectation, Misuse |
| Evidence Table | Validation metrics for each cluster | Signal strength, Confidence score, Financial risk |
| Execution Plan | Time-bound recovery steps | Short-term fixes (7 days), Long-term product roadmap |
Refund Reason Cluster Advanced Features
- Intelligent fraud detection that separates policy abuse from genuine product dissatisfaction.
- Automated handoff triggers to feed top reasons directly into product fix cycles via Openclaw Skills.
- Priority-weighted clustering that focuses on high-frequency and high-cost issues first.
- Support for multi-agent collaboration where one agent handles data normalization and another performs the strategic clustering.
SKILL.md
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