An automated AI agent skill that aggregates and analyzes user feedback across platforms to extract actionable product improvement signals.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install user-feedback-claw
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 user-feedback-claw 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 User Feedback Claw?
User Feedback Claw is a professional-grade analytical tool designed for product managers and customer success teams to transform raw, fragmented customer voices into strategic insights. As a specialized component within the Openclaw Skills ecosystem, it bridges the gap between customer noise and product roadmaps by using advanced NLP to categorize issues, evaluate sentiment, and prioritize development tasks based on empirical evidence.
This skill excels at processing large volumes of data from App Stores, e-commerce platforms, and internal support systems. By synthesizing unstructured feedback into structured reports, it helps teams identify high-impact pain points and validate product decisions with actual user data, ensuring that every improvement is backed by the voice of the customer.
User Feedback Claw Use Cases
- Analyzing negative reviews from Excel or CSV exports to identify recurring product bugs and service failures.
- Processing high volumes of customer service tickets for automated sentiment scoring and topic classification.
- Evaluating open-ended responses from NPS surveys and satisfaction questionnaires to extract specific improvement suggestions.
- Prioritizing product backlog items based on a weighted priority score derived from sentiment intensity and frequency.
- Monitoring real-time sentiment spikes across social channels to detect emerging issues immediately after a new release.
How User Feedback Claw Works
- Data Ingestion: The skill accepts structured files like Excel/CSV or unstructured natural language descriptions, requiring at least the feedback content and source channel.
- Data Cleaning: It removes duplicate entries, filters out low-value content such as single emojis or generic words, and unifies various platform ratings into a standard 1-5 star scale.
- Sentiment & Theme Classification: Using specialized lexicons, it identifies sentiment intensity and maps feedback to specific categories like product quality, logistics, user experience, or pricing.
- Clustering & Prioritization: Similar feedback points are grouped, and a priority score is calculated based on frequency, sentiment weight, and the coefficient of rating impact.
- Report Generation: The agent generates a structured output including a Top Issues list, representative original quotes, trend observations, and actionable improvement recommendations.
User Feedback Claw Setup
To deploy this skill within your environment, ensure you have the necessary reference taxonomies configured.
# Navigate to your agent's skills directory
cd openclaw-agent/skills
# Clone or download the user-feedback-claw repository
git clone https://github.com/openclaw/user-feedback-claw.git
# Ensure your references folder contains the required lexicons
# references/sentiment-lexicon.md
# references/issue-taxonomy.md
# references/improvement-templates.md
Once installed, you can trigger the analysis by providing a data file and asking your agent to perform a feedback audit using Openclaw Skills capabilities.
User Feedback Claw Data Schema & Taxonomy
The skill organizes processed feedback into a structured taxonomy to ensure consistent reporting across different data sources.
| Attribute | Description | Data Type |
|---|---|---|
| feedback_content | The primary raw text provided by the user | String |
| sentiment_intensity | Calculated strength of positive or negative emotion | Float |
| issue_category | Classification (e.g., Quality, Logistics, CS, Price, UX) | Enum |
| priority_score | Weighted score (Frequency x Sentiment x Rating Impact) | Decimal |
| user_segment | Labels for high-value users, KOLs, or VIPs if available | String |
| action_item | Automatically generated improvement suggestion | Markdown |
User Feedback Claw Advanced Features
- Multi-agent collaboration with auto-customer-service-claw to automatically turn support logs into structured feedback data.
- Real-time threshold monitoring via process-data-monitor-claw to trigger deep-dive analysis when negative feedback rates exceed a set limit.
- Automated weighted priority scoring that objectively ranks product issues beyond simple frequency counts.
- Cross-platform rating normalization to allow for a unified view of customer satisfaction across fragmented Openclaw Skills data sources.
- Support for high-value user tagging to ensure feedback from critical accounts is always prioritized in the final report.
SKILL.md
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