Bytesagain Data Analytics

Analyze CSV files with statistical summaries, correlations, and pivot tables. Use when exploring new datasets, checking data quality, finding column correlat...

Security Scan

The skill is coherent with its stated purpose (local CSV analysis using bash + python), contains no obvious exfiltration or unrelated credential requests, but the provided script listing was truncated and contains at least one coding bug — review the full script before using and test on non-sensitive data.

Purpose & Capability

Name/description match what is included: a terminal CSV analysis toolkit implemented as a bash wrapper that runs python3 snippets. The declared requirements (bash, python3) align with the implementation and no unrelated credentials or services are requested.

Instruction Scope

SKILL.md and the script operate only on local CSV files and produce summaries, correlations, pivot tables, and quality reports. There are no network calls or references to external endpoints. Note: the script uses environment variables DA_COL/DA_ROW/DA_VAL to pass column names into Python, which is appropriate, but the embedded Python heredocs are single-quoted so shell variables like $file appear literally in the Python code — this appears to be a coding bug (not a malicious behavior).

Install Mechanism

No install spec (instruction-only with an included script) — lowest install risk. The code uses only standard Python libraries and bash, and there are no downloads or package installs declared.

Credentials

No required environment variables, credentials, or config paths are declared. The script sets transient environment variables when invoking Python to pass column names; nothing requests secrets or unrelated tokens.

Persistence & Privilege

always is false, the skill is user-invocable and does not request elevated persistence or modify other skills or system-wide settings.

Assessment

This skill appears to do what it claims (local CSV analysis) and does not request credentials or perform network I/O. Before installing or running: (1) review the full scripts in the repository — the provided script listing here was truncated, so confirm the complete file contents; (2) be aware of a likely bug where the Python heredocs are single-quoted so shell variable $file may not be expanded — test the tool on non-sensitive sample CSVs first; (3) run it in a sandbox or isolated environment if you have sensitive data, and check file permissions before execution. If you rely on this tool for production, ask the author/maintainer to fix the variable-expansion bug and provide automated tests or a packaged release.

Terminal data analysis toolkit for CSV files. Compute statistical summaries, correlation matrices, top value rankings, trend charts, data quality reports, and pivot tables — no Python data science libraries required.

Usage

bytesagain-data-analytics describe <csv_file>
bytesagain-data-analytics correlate <csv_file>
bytesagain-data-analytics top <csv_file> <column>
bytesagain-data-analytics trend <csv_file> <column>
bytesagain-data-analytics clean <csv_file>
bytesagain-data-analytics pivot <csv_file> <row_col> <value_col>

Commands

• describe — Per-column statistics: count, mean, std, percentiles, top categories
• correlate — Pearson correlation matrix across all numeric columns
• top — Rank top 15 values in any column with percentage and bar chart
• trend — ASCII line chart showing value trend over rows with direction indicator
• clean — Data quality report: null counts, low cardinality, coverage per column
• pivot — Group by a category column and aggregate a numeric column

Examples

bytesagain-data-analytics describe sales.csv
bytesagain-data-analytics correlate metrics.csv
bytesagain-data-analytics top customers.csv country
bytesagain-data-analytics trend revenue.csv amount
bytesagain-data-analytics clean user-data.csv
bytesagain-data-analytics pivot orders.csv category revenue

Requirements

• bash
• python3

When to Use

Use when exploring a new dataset, checking data quality before analysis, finding correlations between metrics, or generating quick visual summaries from CSV exports without opening a spreadsheet.