Biostatistics: Actuarial-Level Statistical Analysis
v1.0.0Provides advanced actuarial-level biostatistical analyses including Bayesian inference, Monte Carlo simulation, machine learning, survival models, and health...
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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
The name/description (actuarial-level biostatistics) matches the SKILL.md capabilities (Bayesian inference, Monte Carlo, ML, survival models, actuarial metrics). No unrelated privileges, binaries, or credentials are requested.
Instruction Scope
Instructions are narrowly scoped to clarifying analysis questions, selecting statistical frameworks, writing and executing Python code, reporting results, and stating limitations. This is coherent for the stated purpose. Note: the skill assumes a Python runtime with many scientific libraries 'available via python3 on this host' — the SKILL.md gives no mechanism to verify or install these, and it implicitly requires the agent be allowed to run code (normal for execution-capable agents). There are no instructions to read unrelated system files, access credentials, or transmit data to third-party endpoints.
Install Mechanism
This is an instruction-only skill with no install spec and no code files. That minimizes disk-write/install risk.
Credentials
No environment variables, credentials, or config paths are requested. The only environmental assumption is availability of Python and listed libraries; these are proportional to the declared functionality.
Persistence & Privilege
Skill is not force-included (always: false) and uses the platform default for autonomous invocation. It does not request persistent system-wide configuration or modify other skills' settings.
Assessment
This skill appears coherent with its stated purpose, but review these practical points before installing: 1) The SKILL.md assumes python3 plus many scientific packages are present — verify the runtime has the required libraries or be prepared to install them in a controlled way. 2) The skill's runtime pattern involves writing and executing Python code — ensure your agent's execution environment is sandboxed and you trust code execution for your use case. 3) If you plan to analyze sensitive health data, ensure data are de‑identified and that use complies with applicable privacy/regulatory rules (HIPAA, GDPR, etc.). 4) Ask the publisher for reproducible example notebooks or tests so you can validate outputs before using results in decisions. 5) If you require PyMC or other extra packages, prefer installing from official package indexes (conda/pypi) and review dependency versions. Overall the skill is internally consistent, but exercise standard precautions for executing analytic code and handling patient-level data.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.