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Performs heavy numerical compute and statistical analysis inside the sandbox.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Basé sur la classification professionnelle SOC
Guides the agent on how to handle git operations and generate .patch files instead of submitting PRs.
Guides the agent on how to read and search GitHub issues to understand the repository's problems.
Records and appends every user-agent customer support interaction to the memory.md log in the workspace.
Scans a website deeply, converting HTML pages to markdown, respecting robots.txt, and updating the snapshots log.
Exposes a 100% local, offline PDF batch extraction utility (extract_to_markdown.py) that isolates invoices under invoices/ and translates PDFs into clean Markdown files for LLM-native parsing.
Reconcile loaded expenses against the pre-parsed invoice database, flagging discrepancies like amount mismatches, missing invoices, and merchant mismatches locally.
| name | python-data |
| description | Performs heavy numerical compute and statistical analysis inside the sandbox. |
Run complex calculations, regressions, and aggregations using pandas and scikit-learn in the sandbox.
pandas, numpy, or sklearn to:
python3 script.py.[
{"segment": "High Risk", "churn_rate": 0.34, "avg_tenure": 2.1},
{"segment": "Low Risk", "churn_rate": 0.05, "avg_tenure": 14.7}
]
select_dtypes(include=["object", "str"]) for categorical columns
(avoids the pandas 4 deprecation warning).pip install -q <package> before importing.