Outlier Detection & Handling
v1.0.1Use outlier detection handler for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.
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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
Name, description, SKILL.md, requirements.txt, and scripts/main.py all describe and implement statistical outlier detection and handling. Declared dependencies (numpy, scipy) and the packaged script are appropriate and proportionate for the stated purpose.
Instruction Scope
SKILL.md instructs validating inputs, running the packaged script, and producing bounded outputs. The instructions reference only workspace files and the packaged script; they do not request unrelated system files, credentials, or external endpoints.
Install Mechanism
There is no install spec; dependencies are standard Python packages listed in requirements.txt and installed via pip as documented. No arbitrary remote downloads, URL shorteners, or archive extraction are used.
Credentials
The skill requires no environment variables, no credentials, and no special config paths. The code reads a user-specified data file or uses built-in demo data — this matches the declared parameters and purpose.
Persistence & Privilege
The skill does not request persistent/always-on presence and does not modify other skills or system-wide settings. It performs local execution only and is user-invocable by default.
Scan Findings in Context
[pre-scan-none] expected: No static regex findings were detected. This is consistent with a small, straightforward Python utility that performs local numeric processing.
Assessment
This skill appears coherent and limited to local outlier analysis. Before installing/running: (1) run it in a sandboxed environment or isolated workspace if you will process sensitive data; (2) verify Python 3.10+ and install requirements.txt in a virtualenv to avoid dependency conflicts; (3) validate and restrict input file paths (avoid symlinks or unexpected directories) to prevent accidental data exposure; (4) review the small scripts/main.py if you need absolute assurance (the code is short and readable); (5) for large datasets, test performance and memory use first. No credentials or network access are required by this skill.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.