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Csv Cleaner

skill-0xelitesystem-claude-skills-templates-csv-cleaner · by 0xelitesystem

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", "dedupl…

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Install

$ agentstack add skill-0xelitesystem-claude-skills-templates-csv-cleaner

✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

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 

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 -cleaned.csv in the same directory. Always utf-8, always comma-delimited, always with a header row.

Then emit a summary:

````

Diagnosis

Fixes applied

  • :
  • :

Manual review needed

````

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.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.