القائمة
General-purpose data profiling and exploration. Use when first encountering any dataset to understand its structure, quality, and analysis potential.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | data-explorer |
| description | General-purpose data profiling and exploration. Use when first encountering any dataset to understand its structure, quality, and analysis potential. |
Profile any tabular dataset (CSV, JSON, Parquet) and produce a structured summary the other skills can consume.
{
"files": [
{
"filename": "customers.csv",
"rows": 91,
"columns": 7,
"schema": [
{"name": "CustomerID", "dtype": "object", "nulls": 0, "unique": 91},
{"name": "CompanyName", "dtype": "object", "nulls": 0, "unique": 91}
],
"quality": {
"missing_pct": {"Region": 0.60},
"duplicates": 0
},
"recommendations": [
"CustomerID is a unique string identifier",
"Region column has a high missing percentage (60%)",
"Can be joined with orders.csv on CustomerID to analyze customer behavior"
]
}
]
}
pandas for profiling. It is pre-installed in the sandbox.select_dtypes(include=["object", "str"]) for categorical columns.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.