Statistical Analysis Advisor
v1.0.0Recommends appropriate statistical methods (T-test vs ANOVA, etc.) based.
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byAIpoch@aipoch-ai
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 (statistical test recommendations) match the provided materials: a decision tree, assumption checks, and power-analysis helpers in scripts/main.py and supporting references. There are no unrelated environment vars, binaries, or cloud credentials requested.
Instruction Scope
SKILL.md instructs the agent to validate inputs, run the packaged script (scripts/main.py), and return structured results. The instructions do not ask the agent to read arbitrary system files, harvest environment variables, or transmit results to external endpoints. The doc explicitly notes it does not access raw data directly and focuses on recommendations rather than automatic data exfiltration or side effects.
Install Mechanism
There is no install spec; this is an instruction + packaged script skill. That lowers risk because nothing is downloaded or installed automatically. Requirements.txt lists small, reasonable Python-only entries (dataclasses, enum).
Credentials
The skill declares no required environment variables, credentials, or config paths. The code and docs operate on inputs supplied by the user, so no secret access is requested or implied.
Persistence & Privilege
The skill is not always-enabled and does not request elevated or persistent privileges. There is no indication it modifies other skills or global agent configuration.
Assessment
This skill appears coherent and self-contained: it recommends statistical tests, documents assumptions, and includes a local Python script and reference guides. Before installing or executing: 1) Inspect scripts/main.py yourself (or run python -m py_compile scripts/main.py) to confirm there are no unexpected network calls or subprocess usage; the provided excerpts show none but you should verify the whole file. 2) Run the script locally with non-sensitive, synthetic data to confirm behavior. 3) Do not feed secrets or production datasets unless you review how inputs/outputs are handled. 4) Treat the recommendations as advisory — the SKILL.md itself warns that human verification is required for research/publication use.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.