Ingest CSV and TSV files into local feather storage with encoding detection.
npx skills add https://github.com/bbartling/open-fdd --skill driver-csv-ingest
Please help me install this Agent Skill. Skill: driver-csv-ingest Source: https://github.com/bbartling/open-fdd/tree/main/skills/driver-csv-ingest Command: npx skills add https://github.com/bbartling/open-fdd --skill driver-csv-ingest
This Skill simplifies the process of importing CSV and TSV files into local feather storage systems, enabling efficient data analysis and management.
Instruct the system to process a CSV file by uploading it to the specified route and verifying the ingestion success through the provided metrics.
High-intent search queries and answers about installing and using this skill.
To ingest CSV and TSV files into local storage, this Skill accepts multipart uploads or path-based files, automatically detects encoding formats like UTF-16 LE BOM, normalizes timestamps, and loads data into feather storage with row count and preview validation.
Automating batch CSV ingestion with encoding detection is handled by this Skill through automatic identification of formats such as UTF-16 LE BOM and tab separation, normalizing timestamps for consistent batch data loading into local feather storage.
Yes, this Skill uses pandas to convert CSV and TSV files into feather format, applying automatic encoding detection and timestamp normalization to ensure data consistency during local storage ingestion.
You can validate data integrity after importing a TSV file into feather storage because this Skill provides row count summaries and sample data previews immediately following the ingestion process.
Handling UTF-16 LE BOM encoded CSV files during data ingestion is automatic with this Skill, which detects the encoding format alongside tab separation and normalizes the data before loading it into local feather storage.
CSV ingestion can fail without automatic encoding detection when formats like UTF-16 LE BOM are misinterpreted, which this Skill prevents by automatically identifying encoding and normalizing timestamps before loading data into feather storage.
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