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Run1 enterprise data parsing

Skill cxcscmu/SkillLearnBench/skills/b3-teacher-feedback-gemini-3-flash-preview/enterprise-information-search/run1_enterprise-data-parsing

Strategies for reading and indexing diverse enterprise data formats located in a flat directory or specific path.From its SKILL.md

Install
npx -y skills add cxcscmu/SkillLearnBench --skill run1_enterprise-data-parsing

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SKILL.md

0.9 KB, 169 tokens by cl100k_base, as published. Nobody here has run it

When retrieving information from enterprise data (like /root/DATA), first identify the file structure. Enterprise data often consists of mixed formats (PDF, CSV, JSON, TXT, or Markdown).

  1. Inventory: List all files in the directory to determine the scope.
    import os
    data_path = "/root/DATA"
    files = os.listdir(data_path)
    
  2. Reading Techniques:
    • Text/Markdown: Use standard open().read().
    • JSON: Use json.load().
    • CSV: Use pandas.read_csv() for structured queries.
  3. Indexing: If the dataset is large, create a simple keyword index or use a search function to map keywords from the questions to specific filenames to narrow the search space.

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