tabular data processing and analysis

This skill appears to be what it says: a Python-based table preprocessor and EDA helper that uses an OpenAI-compatible LLM for intelligent header merging and summaries. Before installing or running it, consider the following: - Treat OPENAI_API_KEY as sensitive: the skill will (by design) send headers/schema and sometimes sample rows to the configured LLM; do not enable LLM features on tables containing PII, financial data, credentials, or other sensitive content unless you accept remote exposure. Use the --no-abstract flag or disable merge_header when you want to avoid sending data to the remote LLM. - Dynamic code execution: the EDA step allows the agent to generate and run Python code. Run the skill in a sandboxed environment or isolated workspace, and review generated code before execution if possible to prevent accidental directory traversal or data exfiltration. - Use minimal-sending defaults: when you must use LLM features, prefer sending only schema, aggregated stats, or redacted samples. The skill's docs recommend these practices — enforce them in agent policies or by disabling LLM features when privacy is required. - Dependency installation: install requirements in a virtual environment and audit dependencies (openai, pandas, matplotlib). The repo does not perform external downloads beyond pip package installation. If you want a lower-risk setup: run the non-LLM parts (split/clean/transfer) locally and keep OPENAI_API_KEY unset; alternatively configure a private/enterprise LLM endpoint (OPENAI_BASE_URL) with strict monitoring and token scope limits. If you need additional assurance, request a code review focusing on any added/truncated files (the listing shows some files omitted) and enable runtime logging to observe which files are read and what is sent to the LLM.