csv-excel-merger

Merge multiple CSV and Excel files with column matching, deduplication, and conflict resolution.

1|Updated Aug 8, 2026
One-click install
npx skills add https://github.com/th-efool/SKILLS --skill csv-excel-merger-th-efool
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: csv-excel-merger
Source: https://github.com/th-efool/SKILLS/tree/main/csv-excel-merger
Command: npx skills add https://github.com/th-efool/SKILLS --skill csv-excel-merger-th-efool

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, and includes references (resource) components.

What problem does it solve? Combining spreadsheets from different sources is tedious and error-prone when files use different column names, formats, and schemas. This Skill automates the merge process while preserving data lineage and surfacing conflicts instead of silently dropping data. ## Core Features & Use Cases - Intelligent Column Matching: Maps variant column names (e.g., "firstname", "First Name", "fname") onto one unified schema using exact, case-insensitive, then fuzzy matching, with an auditable mapping report. - Deduplication & Conflict Resolution: Deduplicates by primary or compound keys with keep-first, keep-last, keep-longest, or merge strategies, and logs every conflict with its resolution. - Verification & Reporting: Asserts row math and key uniqueness after merging, then produces a structured merge report with completeness stats and export options (CSV, Excel, JSON, SQL, Parquet). - Use Case: You receive three contact exports from different teams with inconsistent headers. Use this Skill to consolidate them into one deduplicated file, review the 47 conflicting records it flags, and export the result as Excel. ## Quick Start Merge the attached contacts_jan.csv, contacts_feb.csv, and leads_export.xlsx into one deduplicated file keyed on email and show me the merge report.

Frequently Asked Questions about csv-excel-merger

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

FAQPage Schema
How do I merge multiple CSV files with different column names in Python?▼

Use pandas to read each file, normalize column names with lowercase and strip, then rename variants onto a unified schema before concatenating with pd.concat. Match columns in order of confidence: exact, case-insensitive, then fuzzy matching.

How to deduplicate rows when merging Excel spreadsheets?▼

Identify a primary key such as email, then use pandas drop_duplicates with keep="first" or keep="last" to remove repeats. When no single column is unique, deduplicate on a compound key like email plus company.

Can pandas merge CSV files larger than 100MB?▼

Yes, read large files in chunks using pd.read_csv with the chunksize parameter and process in batches. Estimate memory before loading everything at once and report progress on long-running merges.

What happens when the same record has conflicting values across files?▼

Conflicts are resolved with a chosen rule: keep first, keep last, keep longest, or merge non-conflicting fields. Every conflict is logged with its resolution, and ambiguous cases can be flagged for manual review.

Why do near-duplicate rows survive after deduplication?▼

Near-duplicates slip through when data types and formats differ, such as inconsistent dates, phone formats, or casing. Standardize formats, strip whitespace, and normalize casing before deduplicating.