Agent skill · data analytics · onewave-ai
csv-excel-merger
Merge multiple CSV/Excel files with intelligent column matching, data deduplication, and conflict resolution. Handles different schemas, formats, and combines data sources. Use when users need to merge spreadsheets, combine data exports, or consolidate multiple files into one.
Why this skill is useful
Provides domain-specific knowledge on merging CSV and Excel files with intelligent column matching and deduplication strategies that the AI wouldn't reliably generate on its own.
What it needs
Requires pandas installed locally. About 2k tokens when loaded. Last updated 2026-07-15. 242 stars on the source repository.
What this skill does
CSV/Excel Merger Merge multiple CSV or Excel files with automatic column matching, deduplication, and conflict resolution. Contents Workflow — the step-by-step merge process Verification — confirm the merge before handing it back Special cases — encoding, compound keys, large files Guidelines — quality and transparency standards Example triggers references/mergestrategies.md — column matching, conflict resolution, and dedup options references/outputtemplate.md — the merge-report format Workflow 1. Inspect the inputs. Determine file count, format (CSV / Excel / TSV), and whether the files are attached or read from disk. Read each header; identify column names, data types, and encoding (UTF-8, Latin-1). Note the candidate primary key. 2. Plan the merge. Match columns across files to one unified schema, choose a conflict-resolution rule, and pick a deduplication strategy. See references/mergestrategies.md for the matching heuristics and the full set of options. 3. Execute the merge with pandas: 4. Verify the result before reporting — see Verification. 5. Report using the layout in references/outputtemplate.md, then offer export options: CSV (UTF-8), Excel (.xlsx), JSON, SQL INSERT statements, or Parquet for large datasets. Verification Never hand back a merge without checking it. After merging, assert the row math holds and the key is actually unique: Report rows in vs. out, duplicates removed, and per-column completeness so the user can sanity-check the numbers against their own expectations. Special cases Compound keys — when no single column is unique, key on a tuple: subset=["email", "company"]. Mixed data types — standardize dates, phone numbers, and country codes; strip whitespace and normalize casing before deduping, or near-duplicates slip through. Missing columns — fill absent columns with empty values and flag them in the report; never silently drop data. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills onewave-ai/csv-excel-merger