data-processing
Skill0 starsChanged 7 months agoMITView on GitHub
CSV/JSON/Excel data cleaning, transformation, and validation pipeline
1 other project uses a version of this.
Adopted by
Most recently taken up by
- ykotik/cli-power-skillstheir own version, first seen Apr 14, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- ykotik/cli-power-skills★ 1same 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 repository7 other files it gives its agents.
- api-documenterSkill
- code-reviewSkill
- config-managerSkill
- doc-generatorSkill
- log-analyzerSkill
- performance-analyzerSkill
- test-generatorSkill
The file
We split this skill into the rules it gives an agent
5 rules. About 0 are common, 5 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈5
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 18
Identify the data: Use Glob to find the data files.
- Distinctive: line 20
Inspect the data: Read the first few rows to understand the structure:
- Distinctive: line 25
Process the data: Run the full cleaning pipeline:
- Distinctive: line 31
Validate the output: If a schema is available, validate the cleaned data:
- Distinctive: line 43
Offer further transformations: Ask if the user needs aggregation, pivoting, merging, or export to a different format.
≈ “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.
Files
Data Processing Skill
You are a data engineering specialist. When the user asks you to process data, follow this workflow:
Workflow
- Identify the data: Use Glob to find the data files. Ask the user about the expected schema if unclear.
- Inspect the data: Read the first few rows to understand the structure:
python3 {skill_path}/scripts/processor.py <input_file> --inspect - Process the data: Run the full cleaning pipeline:
Operations include: remove duplicates, handle missing values, fix data types, normalize strings, detect outliers.python3 {skill_path}/scripts/processor.py <input_file> --output <output_file> [--dropna] [--dedup] [--fix-types] - Validate the output: If a schema is available, validate the cleaned data:
python3 {skill_path}/scripts/validator.py <data_file> --schema <schema_file> - Present results: Show the user:
- Row count before/after cleaning
- Columns with type changes
- Number of nulls filled/rows dropped
- Validation results (pass/fail per field)
- Preview of cleaned data (first 10 rows)
- Offer further transformations: Ask if the user needs aggregation, pivoting, merging, or export to a different format.
Supported Formats
- CSV (with auto-detection of delimiter and encoding)
- JSON (records, nested)
- Excel (.xlsx, .xls)
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