A high-precision tool for querying the positions and trajectories of spacecraft, planets, and moons using SPICE kernels.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install heliospice
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 heliospice 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 Heliospice?
Heliospice is a sophisticated utility designed to interface with SPICE kernels, providing users with the ability to query precise ephemeris data for a vast array of celestial bodies and man-made spacecraft. As a specialized addition to the Openclaw Skills ecosystem, it allows developers and researchers to programmatically access the same high-fidelity navigation data used by NASA and other space agencies for mission planning and scientific discovery.
The skill supports over 36 spacecraft missions and all major planetary bodies, offering tools for position tracking, distance calculation, and complex coordinate transformations. By integrating this into your agentic workflows, you can automate the retrieval of orbital mechanics data without needing deep expertise in the SPICE toolkit architecture.
Heliospice Use Cases
- Tracking the real-time or historical trajectory of deep-space missions like Parker Solar Probe, Voyager, or Europa Clipper within Openclaw Skills.
- Calculating the distance and closest approach between two planetary bodies or a spacecraft and its target moon.
- Generating time-series data for orbital analysis and scientific visualization of solar system dynamics.
- Converting positional vectors between specialized coordinate frames such as J2000, RTN, and ECLIPJ2000.
- Automating astronomical research tasks that require precise ephemeris data for 36+ missions and all major planets.
How Heliospice Works
- The user provides a query regarding a spacecraft or celestial body's position or distance via an Openclaw Skills interface.
- Heliospice checks the local kernel cache using the management tool to ensure the required SPICE data files are available.
- The skill downloads or updates necessary kernels from remote repositories if they are missing or outdated.
- It executes mathematical computations using the loaded kernels to determine the state vector (position and velocity) at the requested timestamp.
- The result is transformed into the desired coordinate frame and returned as a structured data object for the AI agent to interpret.
Heliospice Setup
To get started with this skill in your environment, you need to install the package using pip:
pip install heliospice
Once installed, your Openclaw Skills agent can begin invoking tools like get_spacecraft_ephemeris or manage_kernels. No external API keys are required, as the skill fetches public domain SPICE kernels directly.
Heliospice Data Schema & Taxonomy
| Data Type | Description |
|---|---|
| Spacecraft | Support for 36+ missions including Juno, Cassini, and BepiColombo. |
| Planetary Bodies | Full ephemeris for all 8 planets, Pluto, and major moons like Titan and Europa. |
| Frames | Standardized support for RTN, J2000, and ECLIPJ2000 coordinate systems. |
| Kernels | Local cache management for SPICE .bsp, .tpc, and .tls files. |
| Vectors | Positional data returned as (x, y, z) coordinates with optional velocity components. |
Heliospice Advanced Features
- Intelligent kernel management that handles automatic downloading, loading, and purging of cache to optimize Openclaw Skills performance.
- Multi-point trajectory generation for creating detailed path histories over custom intervals.
- Advanced distance analysis tools that identify minimum, maximum, and mean distances between moving bodies over time.
- Support for a wide range of specialized coordinate frames, making it compatible with various astrophysical datasets.
SKILL.md
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