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janjaszczak
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data-import-parsers

Stream rows from CSV, XLSX, and JSON files with validation and error logging.

1|Updated Jan 15, 2026
Search Tags:#validation#streaming#etl#data-import#error-logging#coercion
One-click install
npx skills add https://github.com/janjaszczak/cursor --skill data-import-parsers
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-import-parsers
Source: https://github.com/janjaszczak/cursor/tree/main/skills/data-import-parsers
Command: npx skills add https://github.com/janjaszczak/cursor --skill data-import-parsers

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Memory-heavy data imports are risky and error-prone; this skill enables memory-safe streaming of records, validating and coercing types explicitly, and skipping irreparable rows while logging them to an error CSV that includes all original columns plus timestamp, file, line, and error details. It also ensures idempotent DB writes to enable safe re-runs and auditable processing.

Core Features & Use Cases

  • Streaming, row-by-row processing to avoid loading entire datasets into memory.
  • Explicit type validation and coercion per field to enforce data quality.
  • Irreparable rows are skipped and logged with full context in an error CSV (including timestamp, file, line, and error).
  • Idempotent persistence strategies (e.g., upserts, conflict handling) to prevent duplicates on re-runs.

Quick Start

Run the streaming import workflow on a sample dataset to verify memory usage, validation, and idempotent writes.

Frequently Asked Questions about data-import-parsers

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I stream and validate large CSV files without loading them into memory?▼

To stream and validate large CSV files without loading them into memory, this Skill processes input data files sequentially, parsing rows individually to enforce explicit type validation and controlled coercions while avoiding memory overhead.

How do I log skipped rows with full context during an ETL data import?▼

To log skipped rows with full context during an ETL data import, irreparable rows are skipped and appended to an error CSV that includes all original columns alongside timestamp, file, line, and error details.

What is the best way to ensure idempotent database writes when importing JSON data?▼

To ensure idempotent database writes when importing JSON data, the Skill applies idempotent persistence strategies like upserts and conflict handling, preventing duplicate records and enabling safe re-runs of the ETL workflow.

Does this data import parser support XLSX files for row-by-row type coercion?▼

Yes, this data import parser supports XLSX files for row-by-row type coercion, covering CSV, XLSX, and JSON ETL tasks where explicit type validation and controlled coercions are required for data quality.

How are parse errors and metrics tracked when streaming ETL records?▼

Parse errors and metrics are tracked when streaming ETL records by emitting processing metrics like rows_ok, rows_skipped, and parse_errors, while logging detailed row-level error context to a dedicated error CSV file.

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