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0xelitesystem0xelitesystem/claude-skills-templates/skills/csv-cleaner/SKILL.md

csv-cleaner

Skill2 starsChanged 7 months agoMITView on GitHub

Diagnose and clean messy CSV, TSV, or spreadsheet files. Use whenever the user uploads a CSV or TSV that has data quality issues, asks to "clean", "normalize", "fix", or "deduplicate" tabular data, or hands over a file with encoding problems, broken headers, type inconsistencies, duplicate rows, or missing values. Triggers on "clean this CSV", "fix this data", "this spreadsheet is messy", "deduplicate these rows", or any data-quality request on a tabular file. Always run scripts/diagnose.py before making changes so the cleanup is informed by what is actually broken.

1 other project uses a version of this.

Adopted by

Most recently taken up by

  • Other versions1 project uses a version of this.

    Word for word · 0

    None found.

    Edited · 0

    None found.

    Wrote their own version · 1

    1. 0xelitesystem/claude-skills-collection★ 0same name

    How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.

  • In this repository4 other files it gives its agents.

The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 4 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Run scripts/diagnose.py on the input file:

    line 12
  2. Distinctive:

    Read the output carefully before doing anything else.

    line 30
  3. Distinctive:

    Do not silently change data.

    line 46
  4. Distinctive:

    If the file appears to be a different format (Excel binary, JSON, fixed-width), do not try to clean it as CSV.

    line 68
  5. Distinctive:

    If the file has fewer than 5 rows after deduplication, ask the user whether the input is correct before continuing.

    line 70

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

CSV Cleaner

Two-phase workflow: diagnose first, clean second. Never start fixing before knowing what is broken.

Phase 1: Diagnose

Run scripts/diagnose.py on the input file:

python scripts/diagnose.py <path-to-file>

The script reports:

  • Encoding (utf-8, latin-1, utf-16, etc.)
  • Delimiter (comma, tab, semicolon)
  • Quote character and escape behavior
  • Header row detection (present, absent, or duplicate)
  • Row count
  • Column count consistency across rows
  • Type inference per column (integer, float, date, string, mixed)
  • Null distribution per column
  • Duplicate row count

Read the output carefully before doing anything else. The diagnosis tells you which fixes apply.

Phase 2: Clean

Apply fixes in this order:

  1. Fix encoding: re-read with detected encoding, write as utf-8.
  2. Normalize line endings to \n.
  3. Strip BOM and zero-width characters.
  4. Fix headers: lowercase, snake_case, replace spaces with underscores, ensure uniqueness.
  5. Trim whitespace from every cell.
  6. Coerce types where the column is type-consistent.
  7. Standardize null representations: empty string, "NA", "null", "n/a" all become empty.
  8. Deduplicate exact rows.
  9. Validate column count consistency. Flag rows that do not match the header.

Do not silently change data. If a fix alters values, report the count.

Output

Save the cleaned file as <original-name>-cleaned.csv in the same directory. Always utf-8, always comma-delimited, always with a header row.

Then emit a summary:

## Diagnosis
<key findings from phase 1>

## Fixes applied
- <fix>: <count of rows or cells affected>
- <fix>: <count>

## Manual review needed
- <issue that the script could not fix safely>

When to refuse

If the file appears to be a different format (Excel binary, JSON, fixed-width), do not try to clean it as CSV. Tell the user what the file actually is and suggest the right tool.

If the file has fewer than 5 rows after deduplication, ask the user whether the input is correct before continuing.

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