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Clean and transform tabular data (CSV/JSON). Performs deduplication, null-row removal, string trimming, and column normalization. Useful for ETL tasks that don't need pandas — uses only Python standard library.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
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Change impact scan for Python projects. When modifying function signatures, interfaces, data models, config structs, or shared modules, automatically grep all call sites and dependent files to prevent incomplete changes. Outputs a structured markdown impact report.
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| name | data-processing-pipeline |
| description | Clean and transform tabular data (CSV/JSON). Performs deduplication, null-row removal, string trimming, and column normalization. Useful for ETL tasks that don't need pandas — uses only Python standard library. |
Use this skill when you need to clean a CSV or JSON-array file without spinning up a full pandas workflow. It handles the common 80%: dedupe, drop nulls, normalize.
input_path (required, str) — path to a .csv or .json file.output_path (required, str) — where to write the cleaned data.operations (optional, list[str]) — any of dedupe, drop_empty_rows, trim_strings, normalize_columns. Default: all four.output_format (optional, str) — csv or json. Default: same as input.{
"output_path": "/path/to/cleaned.csv",
"rows_in": 1000,
"rows_out": 875,
"operations_applied": ["dedupe", "drop_empty_rows", "trim_strings", "normalize_columns"],
"duplicates_removed": 100,
"empty_rows_removed": 25
}
| Name | Behavior |
|---|---|
dedupe | Remove exact-duplicate rows (all columns equal). |
drop_empty_rows | Drop rows where every value is empty/whitespace-only. |
trim_strings | .strip() on every string value. |
normalize_columns | Column names → lowercase, spaces/dashes → underscores. |
JSON input must be a top-level array of objects; non-conforming JSON raises ValueError.