multi-format-csv-detection-and-deduplication
Detect and handle multiple CSV format versions from the same data source; deduplicate records across format variants
Best use case
multi-format-csv-detection-and-deduplication is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Detect and handle multiple CSV format versions from the same data source; deduplicate records across format variants
Teams using multi-format-csv-detection-and-deduplication should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/multi-format-csv-detection-and-deduplication/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How multi-format-csv-detection-and-deduplication Compares
| Feature / Agent | multi-format-csv-detection-and-deduplication | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Detect and handle multiple CSV format versions from the same data source; deduplicate records across format variants
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Multi-Format CSV Detection and Deduplication When ingesting CSVs from sources that evolve formats over time (e.g., Fidelity exports with old vs. new column orders), detect format from header structure rather than filename. The same year/period can exist in both formats with identical transactions. Use column position and presence of distinguishing headers (e.g., 'Account Number' only in new format) as discriminators. Build a unified parser that normalizes both formats to a canonical schema, then deduplicate by transaction fingerprint (date, ticker, quantity, price) across all input files.
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