Automate Hinge dating profile interactions using Android emulators and Gemini vision AI to like and comment intelligently.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install hinge-liker
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 hinge-liker 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 Hinge Auto-Liker?
The Hinge Auto-Liker is a sophisticated automation tool designed to streamline the dating experience using cutting-edge vision models. As a prominent entry in the ecosystem of Openclaw Skills, it bridges the gap between mobile applications and AI reasoning. By utilizing an Android emulator and ADB commands, the skill navigates the Hinge interface, captures profile content, and uses Gemini 1.5 Flash to make qualitative decisions on whether to like or skip a profile based on user-provided preferences.
This tool is specifically built for developers and power users who want to apply AI to social discovery. It doesn't just swipe blindly; it analyzes prompts and photos to craft contextually relevant and witty comments, significantly increasing the quality of interactions. By integrating this into your Openclaw Skills workflow, you can maintain an active presence on dating platforms while ensuring that every engagement is thoughtful and data-driven.
Hinge Auto-Liker Use Cases
- Automate the time-consuming process of daily swiping on Hinge.
- Filter potential matches using AI vision to identify specific hobbies or lifestyle traits.
- Generate high-quality opening lines automatically based on a profile's unique prompts.
- Maintain dating app activity consistently using scheduled automated sessions.
- Analyze dating profile trends and session outcomes through detailed JSON logging.
How Hinge Auto-Liker Works
- Initializes an Android Virtual Device (AVD) and ensures the Hinge application is active via ADB.
- Captures high-resolution screenshots of the profile currently displayed on the emulator.
- Processes visual data using Gemini AI to evaluate attractiveness and profile compatibility.
- Selects the most impactful photo or prompt to interact with based on AI analysis.
- Generates a witty, customized comment and executes the 'Like' action through automated screen coordinates.
- Compiles a comprehensive session report detailing every profile viewed, action taken, and reason for the decision.
Hinge Auto-Liker Setup
To get started with this skill within your Openclaw Skills environment, follow these steps:
# Install required Android command line tools
brew install --cask android-commandlinetools
# Create a dedicated Android Virtual Device
avdmanager create avd -n HingePhone -k "system-images;android-34;google_apis;arm64-v8a" -d pixel_6
# Configure your environment variables
export GEMINI_API_KEY="your_api_key_here"
export PATH="<android-tools-path>/platform-tools:<android-tools-path>/emulator:$PATH"
# Execute the automation script
python3 scripts/hinge_android.py --likes 8 --user-desc "your personalized bio here"
Hinge Auto-Liker Data Schema & Taxonomy
The skill maintains organized data structures for every session to ensure transparency and easy auditing:
| Data Type | Format | Description |
|---|---|---|
| Session Report | Markdown/Text | A human-readable summary of likes, skips, and AI reasoning. |
| Activity Logs | JSON | Structured data saved in the logs/ directory for every interaction. |
| Screen Recordings | MP4 | Full video captures of the automation process stored in recordings/. |
| Metadata | Env Vars | Configuration for model selection, ADB paths, and working directories. |
Hinge Auto-Liker Advanced Features
- Multi-agent support for running concurrent automation sessions.
- Headless mode configuration for running emulators on cloud servers or home labs.
- Automated video compression using FFmpeg for easy sharing of session recordings.
- Integration with Openclaw Skills cron scheduling for fully autonomous daily operations.
- Dynamic UI element detection to adapt to Hinge application updates.
SKILL.md
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