SciPy
v1.0.0Solve optimization, statistics, signal processing, and linear algebra problems with SciPy recipes and ready-to-run code.
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byIván@ivangdavila
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name and description match the instructions: SKILL.md directs the agent to produce ready-to-run SciPy code (optimize, stats, signal, linalg, etc.). The only required binary is python3, which is appropriate for a Python/SciPy skill. No unrelated binaries, env vars, or config paths are requested.
Instruction Scope
Instructions keep scope to providing runnable Python/SciPy examples and validation. The skill expects code to run in the user's Python environment and instructs the agent to always include complete code. It does not tell the agent to read unrelated system files, exfiltrate data, or call external endpoints. One practical gap: the skill does not provide installation steps for SciPy/NumPy (it assumes they exist in the user's environment), so users may need to install packages themselves before running examples.
Install Mechanism
No install spec and no code files — instruction-only — which is the lowest-risk pattern. Nothing will be downloaded or written by the skill itself.
Credentials
The skill requests no environment variables, credentials, or config paths. That is proportional to its stated purpose of producing SciPy code snippets.
Persistence & Privilege
always:false (default). The skill is stateless by design and offers an optional user-controlled memory template; it does not request persistent or elevated privileges or modify other skills' config.
Assessment
This skill appears coherent and low-risk: it generates SciPy-based Python code and does not ask for secrets or install anything. Before using: (1) confirm you have python3 and SciPy/NumPy installed (or install them in a virtualenv: pip install numpy scipy), (2) review any generated code before running it (check for unintended file or network access and for heavy computation that could strain your machine), and (3) if you prefer, run examples in an isolated environment (virtualenv or container). The skill source is listed as "unknown" in the registry metadata — that's common for third-party skills, but if provenance matters to you, consider verifying the homepage/owner or preferring skills from known publishers.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.
Runtime requirements
🔬 Clawdis
OSLinux · macOS · Windows
Binspython3