A high-precision skill for retrieving and validating real-time Geekbench benchmark scores for the latest mobile flagship devices.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install geekbench
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 geekbench 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 Geekbench Benchmark Tracker?
The Geekbench Benchmark Tracker is a specialized tool within the Openclaw Skills ecosystem designed to overcome the limitations of static AI training data. By utilizing real-time search protocols, it enables users to identify the most recent mobile flagship releases, locate their internal manufacturer model numbers, and extract verified performance metrics directly from benchmark databases. This ensures that performance analysis is always based on the most current hardware available on the market.
This skill prioritizes data integrity by cross-referencing multiple information sources and verifying release dates. It prevents the use of outdated information or unverified rumors, providing developers and hardware analysts with a reliable pipeline for technical device evaluation.
Geekbench Benchmark Tracker Use Cases
- Identifying the most recent flagship releases from major manufacturers like Apple, Xiaomi, Honor, and OPPO.
- Verifying hardware performance claims with real-world, verified benchmark data.
- Comparing single-core and multi-core scores across different device generations using standardized versions.
- Filtering out rumor-based data or "leaks" to focus on officially released hardware performance.
How Geekbench Benchmark Tracker Works
- Execute real-time searches for the latest flagship announcements by brand and year to identify current models.
- Validate the product status to ensure the device is officially released, explicitly excluding prototypes and rumors.
- Search for specific Geekbench entries using both the commercial product name and the discovered internal model identifiers.
- Categorize and analyze results by Geekbench version (e.g., 6.5.0) to ensure statistical consistency and valid comparisons.
- Flag anomalies such as outlier scores or insufficient sample sizes to maintain data quality.
Geekbench Benchmark Tracker Setup
To integrate this capability into your Openclaw Skills workflow, ensure your environment has active search permissions enabled.
# Install the benchmark tracker via the Openclaw CLI
openclaw skill add geekbench-tracker
# Configure the skill to prioritize the latest Geekbench 6.5.0 data
openclaw config set benchmark_version "6.5.0"
Geekbench Benchmark Tracker Data Schema & Taxonomy
The skill organizes hardware performance data using the following taxonomy:
| Field | Description | Format |
|---|---|---|
| brand_name | The manufacturer (e.g., Apple, OPPO, Xiaomi) | String |
| product_name | Commercial name of the flagship device | String |
| model_id | Internal manufacturer identifier found via search | String |
| gb_version | Specific Geekbench version used (e.g., 6.5.0) | String |
| single_core | Validated single-core performance score | Number |
| multi_core | Validated multi-core performance score | Number |
| release_status | Official release vs. early testing sample | Enum |
Geekbench Benchmark Tracker Advanced Features
- Automated internal model number discovery for accurate identification of regional device variants.
- Performance threshold detection for high-end hardware (e.g., Single-Core scores > 3000).
- Version-locked comparisons to prevent invalid data mapping between different Geekbench iterations.
- Multi-source verification engine to filter out outlier results from non-retail hardware samples.
SKILL.md
Loading
Related Openclaw Skills
HabitChat
A personal AI habit coach that tracks daily routines, manages streaks, and provides motivational accountability through natural language.
Yuboto Omni API Integration
A robust integration for managing Yuboto Omni API messaging workflows including SMS, Viber, and account management.
Video Downloader (video-dl)
A high-performance media downloader leveraging yt-dlp to capture video and audio from over 1000 websites with automated processing.
Aviation Weather
Retrieve real-time aviation weather data including METAR, TAF, and PIREPs for flight planning and airport monitoring.
Cobo TSS Node Management
A specialized management toolkit for Cobo TSS Nodes, providing automated installation, service management, and secure MPC share maintenance.
Media News Digest
An automated pipeline for gathering and synthesizing media and entertainment industry news from 65+ specialized sources.
METADATA
Featured*
