A comprehensive scientific computing toolkit for solving optimization, statistics, signal processing, and linear algebra problems with production-ready code.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install scipy
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 scipy 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 SciPy?
The SciPy skill is a specialized technical module designed for developers, data scientists, and engineers who need to perform complex mathematical computations within their local Python environment. By leveraging Openclaw Skills, this tool provides instant access to optimized recipes for curve fitting, root finding, and numerical integration, moving beyond theoretical advice to provide fully functional, runnable code.
This skill acts as a stateless interface that builds upon NumPy, ensuring that all computations remain local and secure. Whether you are performing signal analysis or fitting experimental data to a model, the SciPy skill ensures high-performance execution by utilizing vectorized operations and industry-standard algorithms.
SciPy Use Cases
- Finding the minimum or maximum of mathematical functions using constrained or unconstrained optimization.
- Fitting experimental data points to custom mathematical models through curve fitting techniques.
- Performing rigorous statistical analysis, including hypothesis testing, distribution fitting, and confidence interval calculation.
- Processing digital signals using Butterworth filters, smoothing splines, and Fast Fourier Transforms (FFT).
- Solving systems of linear equations and ordinary differential equations (ODEs) for physical modeling.
How SciPy Works
- The agent analyzes the user's scientific problem to determine the appropriate mathematical module within the SciPy library.
- A complete Python script is generated, incorporating necessary imports from sub-packages like scipy.optimize, scipy.stats, or scipy.signal.
- The skill prioritizes vectorized NumPy operations to ensure computational efficiency and performance.
- Validation logic is included in every response, such as checking optimization convergence flags or p-values in statistical tests.
- The code is executed directly in the user's local environment, maintaining total data privacy as no data is transmitted to external servers.
SciPy Setup
To use this skill, ensure you have Python 3 and the necessary libraries installed in your environment. You can install the dependencies and the skill using the following commands:
pip install scipy numpy
clawhub install scipy
SciPy Data Schema & Taxonomy
The SciPy skill is stateless and does not persist data between sessions. It utilizes standard Python and NumPy structures to organize technical data:
| Data Type | Description | Structure |
|---|---|---|
| Input Arrays | Numerical data for analysis | numpy.ndarray |
| Optimization Output | Results of minimization or root finding | OptimizeResult Object |
| Statistical Results | Test statistics and p-values | NamedTuples (e.g., Ttest_indResult) |
| Sparse Matrices | Efficient storage for large, mostly-empty matrices | scipy.sparse (CSR/CSC) |
| Signal Filters | Coefficients for signal processing | b, a coefficients or SOS arrays |
SciPy Advanced Features
- Support for constrained optimization using equality and inequality constraints to model real-world limitations.
- Implementation of zero-phase filtering with filtfilt to prevent phase distortion in time-series data.
- Advanced spatial data handling using KDTree for fast nearest-neighbor lookups.
- Integration with other Openclaw Skills such as the data-analysis and math modules for comprehensive research workflows.
- Multi-dimensional image processing capabilities using the ndimage sub-module for filtering and labeling.
SKILL.md
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