A comprehensive AI skill for researching company reputation and monitoring Google Maps and Shopping reviews with automated sentiment analysis.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install google-reviews
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 google-reviews 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 Google Reviews?
The Google Reviews skill is a powerful tool designed for the Openclaw Skills ecosystem that enables deep research into company reputations across Google Maps and Google Shopping. It goes beyond simple star ratings by providing evidence-based analysis of review signals, thematic trends, and customer sentiment. The skill is built to help developers and analysts make informed decisions based on high-fidelity review data, whether they are conducting one-off due diligence or setting up long-term brand monitoring.
By prioritizing a research-first approach, the skill ensures that users get immediate answers to specific questions before committing to recurring workflows. Once a baseline is established, it can transition into a sophisticated monitoring mode that utilizes heartbeat refreshes to track rating swings and critical topic triggers without unnecessary operational overhead.
Google Reviews Use Cases
- Conduct one-off reputation checks for a company across Google Maps and Shopping.
- Compare competitor review patterns and sentiment trends side-by-side.
- Automate brand health monitoring with heartbeat updates and proactive alerts.
- Generate decision-ready weekly reports for stakeholders based on review volume and tone.
- Perform due diligence by analyzing historical review signals and identifying recurring customer pain points.
How Google Reviews Works
- The skill starts in Research Mode, pulling current review evidence like ratings, volume, and theme mix to answer a specific query.
- It ingests data from authorized Google sources, including the Business Profile and Merchant APIs.
- All raw data is normalized into a canonical schema to ensure consistency across different platforms.
- Sentiment rules are applied to classify the tone of reviews and detect emerging issues with quote-based evidence.
- If the user opts for ongoing tracking, the skill activates Monitoring Mode with configurable heartbeat cadences.
- Lightweight heartbeat checks identify actionable changes, while deep analysis runs on a slower schedule to produce full thematic reports.
Google Reviews Setup
To get started with this skill for Openclaw Skills, you must first initialize the local directory structure for storing memory and snapshots. Run the following command in your terminal:
mkdir -p ~/google-reviews/{brands,snapshots,reports/daily,reports/weekly,heartbeat}
After creating the directories, refer to the setup.md file within the skill to define your brand targets and configure access methods for Google sources.
Google Reviews Data Schema & Taxonomy
The skill manages all monitoring state locally in the ~/google-reviews/ directory using a highly organized taxonomy:
| Path | Purpose |
|---|---|
memory.md |
Stores stable monitoring preferences and activation behavior. |
brands/ |
Contains Markdown files for each brand defining scope and sources. |
snapshots/ |
Stores normalized JSONL review data indexed by refresh cycle. |
reports/ |
Organized subdirectories for daily and weekly sentiment summaries. |
heartbeat/ |
Tracks the monitor-state.md including timestamps and alert cooldowns. |
Google Reviews Advanced Features
- Multi-brand monitoring with per-brand baselines and independent alert thresholds.
- Explainable AI sentiment classification that pairs tone detection with specific review snippets.
- Delta refresh logic that only processes new or edited reviews to save on API costs.
- Heartbeat recipes that allow for low-noise monitoring loops and escalation policies.
- Decision-ready reporting structures that highlight week-over-week movement and actionable priorities.
SKILL.md
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