Three body movement simulator
v1.0.0Simulates the chaotic gravitational motion of three bodies by numerically integrating Newtonian equations using RK4 for accurate trajectory prediction.
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bywow@duanc-chao
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
The name and description claim a numerical three-body simulator and the SKILL.md contains step-by-step physics and RK4 implementation guidance. Example code references typical libraries (numpy) and the content needed to implement the simulator; nothing requested (env vars, binaries, installs) is out of line with this purpose.
Instruction Scope
The instructions are confined to physics, numeric derivatives, and RK4 integration and do not instruct the agent to read unrelated files, access credentials, or send data externally. Note: the provided Python snippet is truncated and contains an obvious variable bug ('stat' instead of 'state'/'new_state') and lacks error handling for singularities (division by zero when bodies collide). These are correctness/robustness issues, not security coherence problems.
Install Mechanism
No install spec and no code files — instruction-only. This means nothing is downloaded or written by the skill itself, which minimizes installation risk.
Credentials
The skill requests no environment variables, credentials, or config paths. That is proportionate for a physics simulation tutorial.
Persistence & Privilege
always is false and the skill does not request persistent system presence or modification of other skills/configs. Autonomous model invocation is allowed (platform default) but not combined with other privileges here.
Assessment
This skill is a tutorial and appears coherent, but exercise normal caution: review and correct the example code before running (it is truncated and has a variable bug), and add numerical safeguards (e.g., softening or collision handling) to avoid division-by-zero. Because the source/homepage is unknown, run any code you paste/run into an interpreter inside an isolated or sandboxed environment, ensure required packages (numpy) are installed from trusted sources, and verify results on small test cases before trusting long simulations.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.