R Analyst

v1.0.1

R-style statistical analysis powered by Python 3. Use when computing descriptive statistics, generating ASCII histograms, calculating correlation matrices, d...

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MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
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Benign
medium confidence
Purpose & Capability
Name/description (R-style CSV analysis) align with the provided script: the bash wrapper launches embedded Python to compute summaries, histograms, correlations, cleaning hints and dataset descriptions. No unrelated permissions, env vars, or binaries are requested.
Instruction Scope
Runtime instructions call scripts/script.sh which reads the provided CSV and runs local Python code to analyze it. The script does not reference network endpoints, external credentials, or other system configuration in the visible portion. Note: the provided transcript of script.sh in the prompt is truncated near the end of the 'describe' command, so I could not inspect the final few lines — based on the visible code the behavior is local file analysis, but the truncation prevents a 100% review.
Install Mechanism
No install spec is provided (instruction-only plus included script). Nothing is downloaded or installed by the skill itself; it expects python3 and bash on PATH, which is reasonable for the stated purpose.
Credentials
The skill declares no required environment variables, no credentials, and no config paths. The script as shown does not read environment secrets or unrelated files.
Persistence & Privilege
Flags are normal (not always:true). The skill does not request persistent/system-wide changes in the visible code and is user-invocable only.
Assessment
This appears to be a straightforward local CSV analysis tool that runs Python3 on files you give it and doesn't request credentials or network access. Before installing or running: (1) Inspect the full script (the preview was truncated) to confirm there are no hidden network calls or unexpected exec/system modifications in the omitted lines. (2) Only run it on datasets you are comfortable exposing to a local process — it reads files you pass to it, so avoid giving it sensitive datasets without review. (3) Ensure python3 is up-to-date and run on a machine with resource limits for very large CSVs (the scripts do in-memory reads and could use significant RAM). (4) If you need stronger guarantees (sandboxing, no disk writes or network), run it inside a container or isolated environment. Overall, the skill looks coherent with its stated purpose but verify the truncated tail of the script before trusting it with sensitive 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.

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