A local query and search utility for NASA MODIS satellite products with bilingual metadata, Google Earth Engine integrations, and download instructions.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install modis-product-skill
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 modis-product-skill 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 MODIS Product Data Query Skill?
The MODIS Product Data Query Skill is a local metadata search and helper utility designed for researchers and GIS developers working with Earth observation data. Operating entirely via the Python standard library, this tool indexes 46 distinct NASA MODIS satellite products across 13 categories, providing quick access to algorithm details, band information, scale factors, and official download links directly from your terminal.
By integrating this utility as part of your Openclaw Skills workflow, you can streamline the discovery and analysis of remote sensing data. It delivers bilingual (Chinese/English) metadata alongside pre-configured Google Earth Engine code snippets, eliminating the friction of manual product catalog lookups.
MODIS Product Data Query Skill Use Cases
- Querying metadata and scale factors for specific MODIS products like MOD13Q1 or MOD11A1 without opening a web browser.
- Generating ready-to-use Google Earth Engine JavaScript boilerplates for satellite image collections.
- Comparing spectral bands, resolutions, and temporal coverages between different MODIS products side-by-side.
- Locating direct download sources and data citation instructions for NASA Earthdata and LP DAAC pools.
How MODIS Product Data Query Skill Works
- Initialization: The user calls the script via CLI with specific operational commands such as search, show, or gee.
- Local Processing: The lightweight Python script queries its internal bilingual database containing 46 NASA MODIS products.
- Filtering and Matching: The engine processes user-specified parameters, including platforms (Terra, Aqua, Combined) or spatial resolutions (250m, 500m, 1km).
- Output Generation: The command-line interface prints structured data, scale factor requirements, or ready-to-copy Google Earth Engine integration blocks.
MODIS Product Data Query Skill Setup
The tool runs exclusively on the Python standard library and does not require external package installations. Ensure you have Python 3.x installed on your environment.
To use this with your Openclaw Skills platform, configure the script in your local workspace:
# Navigate to your workspace directory
cd path/to/your/workspace
# Run database statistics to verify setup
python scripts/modis_products.py stats
MODIS Product Data Query Skill Data Schema & Taxonomy
The skill processes commands through a specific parameter schema, allowing easy parsing by autonomous tools:
| Parameter | Type | Required | Description |
|---|---|---|---|
command |
string | Yes | The execution command (e.g., search, show, gee, download, compare, stats) |
query |
string | Conditional | The search keyword or product ID query |
--limit |
int | No | Maximum number of results to display (default: 10) |
Database Scope
The underlying metadata covers 13 core categories, cataloged as follows:
- Vegetation Indices: 12 products (MOD13 series)
- Surface Reflectance: 5 products (MOD09 series)
- Land Surface Temperature: 4 products (MOD11 series)
- Land Cover: 2 products (MCD12 series)
- Thermal Anomalies: 4 products (MOD14 series)
- Other categories: LAI/FPAR, Evapotranspiration, GPP/NPP, BRDF/Albedo, Snow Cover, and Burned Area.
MODIS Product Data Query Skill Advanced Features
- Bilingual Interface: Offers complete, structured output in both English and Chinese for global development environments.
- Direct GEE Code Export: Instantly prints functional Google Earth Engine JavaScript snippets with appropriate scale factors applied.
- Side-by-Side Product Comparison: Allows swift evaluation of two separate product IDs to distinguish differences in temporal or spatial resolution.
- Zero-Dependency CLI: Executes using standard Python distributions without virtual environment overhead, optimizing execution within Openclaw Skills environments.
SKILL.md
Loading
Related Openclaw Skills
Emoji Translator
A smart, zero-dependency translation engine that converts text into contextually relevant emojis and vice versa.
Focus Toolkit
A zero-dependency productivity suite designed to manage focus sessions through natural language alarms, ambient sound generation, and detailed analytics.
YouTube Transcript Speaker Diarization
An automated tool that generates speaker-attributed transcripts and subtitles from YouTube URLs with support for multiple file formats.
Zhenghe Vault
Zhenghe Vault is a serverless, ERC-4626 yield vault on Base that allows AI agents to automatically grow USDC trading profits through network consumption rather than debt-based lending.
Vegetation Indices Query Skill
A Python command-line utility for querying 519 vegetation indices, 167 sensors, and 43 application fields from IndexDatabase.de.
ib-suite
A read-only Interactive Brokers diagnostics toolchain and indexing skill that pulls account data into a local data lake and generates graded portfolio reports.
Featured*
