# csv-excel-merger

A skill that folds several CSV, TSV and Excel files into one: stacks, joins on a key, drops duplicates and checks the row math.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 1200 installs · ★ 382
- Author: OneWave-AI
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/OneWave-AI/claude-skills/tree/HEAD/csv-excel-merger/SKILL.md
- Updated: 2026-10-20
- HTML: https://affpapa.org/en/nevibecode/skills/csv-excel-merger

## What it does

The skill runs on pandas and handles the part where rows usually go missing: it maps mismatched column names onto one schema, normalizes keys (whitespace, email case, date formats), resolves conflicting values and tags every row with its source file. A bundled `scripts/profile_inputs.py` runs first and reports encoding, delimiter, row counts, candidate keys and header overlap without touching anything. Then you pick the operation: append for the same kind of records across periods, join for different facts about the same entities on an ID or email.

Files are read with `dtype=str`, so leading zeros in IDs, ZIP codes and phone numbers survive. For joins the skill passes `validate="one_to_one"`, which makes pandas raise on a duplicate key instead of multiplying rows. Before handing anything back it checks the arithmetic: rows in, rows out, rows removed, blank keys set aside. Blank keys are not treated as duplicates of each other.

You need Python and pandas; Excel reading wants openpyxl, and `engine="calamine"` with `python-calamine` is much faster on large workbooks. One caveat worth knowing up front: values Excel already mangled in the source file, like a long ID shown as `1.23E+15` or a reformatted date, cannot be recovered by pandas — the skill only flags them. A single Excel sheet holds at most 1,048,576 rows, so beyond that you split the output or go with CSV. For files over a few hundred MB, read with `chunksize=` or reach for Polars and DuckDB.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add OneWave-AI/claude-skills --skill csv-excel-merger -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/csv-excel-merger/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/csv-excel-merger/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add OneWave-AI/claude-skills --skill csv-excel-merger -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/csv-excel-merger/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add OneWave-AI/claude-skills --skill csv-excel-merger -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/csv-excel-merger/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install OneWave-AI/claude-skills csv-excel-merger
```

2. **Manually: put the files in place**
- File: `.github/skills/csv-excel-merger/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add OneWave-AI/claude-skills --skill csv-excel-merger -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/OneWave-AI/claude-skills --path csv-excel-merger
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/csv-excel-merger/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add OneWave-AI/claude-skills --skill csv-excel-merger -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/OneWave-AI/claude-skills/tree/HEAD/csv-excel-merger/SKILL.md

## When to use it
- stack January and February exports into one file
- join contacts to their deal values on email
- dedupe records coming from three sources
- map mismatched column names onto one schema
- catch a join that multiplied rows

## Example prompts
- Combine jan.csv and feb.xlsx into one file, E-mail and Email Address are both email
- Do a VLOOKUP-style join of contacts.csv and deals.xlsx on email and show who has no deal
- Dedupe these three lead lists and add a column with the source file name

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 0377c59e86658abf.

BEGIN THIRD-PARTY FILE 0377c59e86658abf
````text
---
name: csv-excel-merger
description: Merges, appends, joins, and deduplicates multiple CSV, TSV, and Excel files (including multi-sheet workbooks) with pandas - mapping mismatched column names to one schema, normalizing keys, resolving conflicting values, tracking which file each row came from, and verifying the row math. Use when the user wants to combine spreadsheets or exports, stack monthly files, consolidate contact or lead lists, VLOOKUP-style join two sheets on an ID or email, or dedupe records across sources, even if they only say "put these together" or "clean up these lists into one".
---

# CSV/Excel Merger

Combine tabular files into one clean output without silently losing, duplicating, or corrupting rows.

## Workflow

Copy this checklist and track progress:

```
- [ ] 1. Profile inputs
- [ ] 2. Choose append vs join
- [ ] 3. Map columns and normalize keys
- [ ] 4. Merge and resolve conflicts
- [ ] 5. Verify row math
- [ ] 6. Write output and report
```

1. **Profile inputs.** Run the bundled profiler first; it reports encoding, delimiter, rows, headers, candidate keys, and header overlap without changing anything:

   ```bash
   python scripts/profile_inputs.py file1.csv file2.xlsx
   ```

   Excel files are profiled per sheet. Confirm with the user which sheets count if a workbook has more than one.

2. **Choose the operation.** This is the decision that most often goes wrong:
   - **Append (stack)** - same kind of records from different sources or periods (Jan + Feb exports, three lead lists). Use `pd.concat`, then dedupe.
   - **Join (enrich)** - different facts about the same entities (contacts + their deal values). Use `pd.merge` on a key.
   - Unsure? If the files share most columns, append. If they share only an ID column, join.

3. **Map columns and normalize keys.** Build an explicit `{original: unified}` rename map per file (see [references/merge_strategies.md](references/merge_strategies.md) for common variants) and show it to the user when any match is fuzzy. Normalize key columns before dedupe or join: strip whitespace, lowercase emails, strip non-digits from phones, unify date formats. Without this, `A@x.com ` and `a@x.com` survive as two people.
````
END THIRD-PARTY FILE 0377c59e86658abf

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