An AI-powered research tool that converts App Store and Google Play reviews into structured reports on pain points, user segments, and market opportunities.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install userlayer
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 userlayer 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 UserLayer?
UserLayer is a comprehensive market research skill designed to automate the extraction of high-value insights from mobile app marketplaces. By leveraging Openclaw Skills, this tool navigates the complexities of App Store and Google Play feedback to provide developers and product managers with a structured understanding of user sentiment. It goes beyond simple sentiment analysis by synthesizing thousands of reviews into actionable intelligence, identifying exactly where a product excels or fails compared to its competition.
Integrating this skill allows teams to ground their product strategy in real-world evidence. The system identifies specific user segments and validates market opportunities by citing actual reviews as the source of truth. Whether you are performing competitive research or looking for your next big feature idea, UserLayer through Openclaw Skills provides the technical bridge between raw user feedback and strategic product direction.
UserLayer Use Cases
- Perform deep competitor review analysis to find underserved market gaps.
- Extract specific user pain points to prioritize engineering roadmaps.
- Discover hidden user segments based on review behavior and feedback patterns.
- Validate new product opportunities with data-backed evidence from existing app reviews.
- Conduct grounded Q&A sessions to answer specific business questions using cited review data.
How UserLayer Works
- The user initiates an analysis by providing a mobile app URL to the analyze function.
- The skill triggers an asynchronous background job to fetch and process the latest reviews.
- The agent polls the status of the job using the check_status function until the analysis is complete.
- Once finalized, the skill returns a structured payload containing pain points, user segments, and market opportunities.
- Users can then perform targeted follow-up queries against specific pain points to dive deeper into the data.
UserLayer Setup
To integrate UserLayer with your Openclaw Skills environment, ensure you have the required API credentials and scripts configured.
# Set your required UserLayer API Key
export API_KEY="your_api_key_here"
# Optional: Override the default API URL if necessary
export LAUNCHBASE_API_URL="https://lb-api.workflowhunt.com"
The skill relies on the presence of scripts/main.py and scripts/call_api.py to handle the interaction with the UserLayer backend.
UserLayer Data Schema & Taxonomy
The skill organizes its output into structured JSON objects that emphasize traceability and clarity. The core data entities include:
| Entity | Description |
|---|---|
pain_points |
Collection of specific user issues, each with a unique ID for follow-up querying. |
user_segments |
Identified groups of users based on their feedback and usage patterns. |
opportunities |
Strategic areas identified for product growth or improvement. |
sources |
The primary review data used as evidence for the analysis. |
usage |
Information regarding tokens used and billing impact for the run. |
UserLayer Advanced Features
- Asynchronous processing support for handling large-scale review data without timeouts.
- Grounded Q&A capabilities through the query function, ensuring answers are backed by specific review citations.
- Scalable review depth allowing for more than the default 100 reviews for deep-dive historical research.
- Automated status polling logic to manage the lifecycle of long-running analysis jobs.
- Built-in error handling for data indexing and API synchronization.
SKILL.md
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