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Physics Simulation

v1.0.0

Simulates and analyzes physical systems by solving ODEs/PDEs, applying finite element methods, and performing numerical computations for scientific research.

⭐ 0· 45·0 current·0 all-time
by@chunxiaoxx
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
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Benign
medium confidence
✓
Purpose & Capability
Name and description match the instructions: the SKILL.md describes solving ODE/PDE, FEM, and numerical workflows and lists reasonable Python scientific libraries as dependencies. There are no unrelated requirements (no unexpected cloud credentials or unrelated binaries).
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Instruction Scope
Instructions reference tasks being delivered via https://www.nautilus.social/api/academic-tasks and describe inputs/outputs; they do not instruct reading local files, environment secrets, or other system paths. Note: the doc does not detail authentication for the Nautilus API — if the platform requires credentials, that is not declared here.
✓
Install Mechanism
No install spec and no code files are present (instruction-only skill). This minimizes on-disk risk; nothing will be automatically downloaded or written by an installer.
✓
Credentials
The skill declares no required environment variables or credentials and the SKILL.md does not reference any secrets. This is proportionate to an instruction-only simulation helper. Caveat: real integration with Nautilus may require auth not documented here.
✓
Persistence & Privilege
always is false and there is no indication the skill requests persistent/privileged presence or modifies other skills or system-wide settings.
Assessment
This skill appears internally consistent: it is an instruction-only physics simulation helper that names appropriate libraries and points to a Nautilus API endpoint for tasks. Before installing, verify the Nautilus platform (https://www.nautilus.social) to learn whether API access requires authentication and whether submitting results involves sharing data or keys. Because the skill has no code files, it won't execute local binaries or install packages by itself — however your agent may still make network calls to the referenced endpoint, so confirm you trust that external service and its data-handling/payout mechanisms. If you plan to run heavy numerical work locally, ensure required libraries (NumPy/SciPy/FEniCS/deal.II) are installed and sandbox computational workloads appropriately.

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.
Termshttps://spdx.org/licenses/MIT-0.html

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