Fluid Network Agent
v1.0.0从 TOML 读取稳态不可压缩流体网络,计算各节点压力与各管路流量/压差(可选速度),并按工况输出连通性与负载可靠性(PASS/FAIL)。当用户给出网络 TOML 或询问某工况下系统是否能正常工作时使用。
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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/description match the included code and SKILL.md: the package parses TOML network descriptions, runs numeric solves, and emits connectivity / PASS‑FAIL reports. No unrelated binaries, env vars, or credentials are requested.
Instruction Scope
SKILL.md instructs the agent to validate/complete a TOML and run `python scripts/run_fluid_network.py --input <toml>` to produce a report. Allowing the Agent to '补齐最小可运行字段' (auto-complete missing TOML fields) is functionally reasonable for convenience, but it gives the agent discretion to fabricate parameter values if the user doesn't review/confirm them. Otherwise instructions limit actions to loading the TOML, running the solver, and returning results (no file reads beyond the TOML/output file).
Install Mechanism
No install spec; code is provided in the skill bundle and execution relies on a local Python runtime. No external downloads or non-standard install paths are invoked. The code uses tomllib (py3.11+) or tomli fallback—this is a normal dependency but not declared as an install step.
Credentials
The skill requires no environment variables, credentials, or config paths. All required inputs are user-provided TOML files and optional CLI flags, which are proportionate to the stated purpose.
Persistence & Privilege
The skill does not request 'always: true' or any elevated/always-on presence. It does not attempt to modify other skills or system-wide agent settings in the provided files or instructions.
Assessment
This skill appears internally consistent and implements the described solver/reporting flow. Before installing or running it: 1) Run it in an isolated environment (virtualenv/container). 2) Ensure Python 3.11 or the tomli package is available (the script tries tomllib then tomli). 3) Inspect and, if needed, test with the provided examples/example_network.toml so the agent cannot fabricate parameters silently — SKILL.md allows the agent to auto-complete missing TOML fields, so verify any generated values. 4) Note there are a few code-quality issues to check (the distributed source appears truncated in places and there is an obvious bug near the end of solver.py where a variable name looks incorrect — e.g., use of an undefined variable 'a' for area/velocity calculation); you may want the author to fix/release a corrected version before using on important data. 5) The skill does not access secrets or network resources; nevertheless, treat it as untrusted third-party code: inspect, run tests, and avoid providing any sensitive credentials in TOML or other inputs.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.
