csv-processor
Process CSV Files With Data Cleaning Workflows
CSV files often contain inconsistent formats, missing values, duplicates, and large datasets. This skill provides practical workflows for parsing, cleaning, transforming, merging, and analyzing CSV data.
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Review the Skillstore skill "csv-processor" from https://skillstore.io/skills/curiouslearner-csv-processor.md and its manifest at https://skillstore.io/api/skills/curiouslearner-csv-processor/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "csv-processor". A customer CSV has duplicate emails, mixed casing, blank age values, and inconsistent country names.
Expected outcome:
The result would describe a cleaning plan, validation checks, normalized fields, duplicate handling, and a clean export strategy.
Using "csv-processor". A sales CSV needs monthly revenue totals, top products, missing value checks, and outlier detection.
Expected outcome:
The result would provide grouped metrics, data quality notes, suspicious values, and recommendations for a report-ready dataset.
Using "csv-processor". Several regional CSV files need to be combined and joined with a customer lookup table.
Expected outcome:
The result would outline merge keys, join type choices, schema alignment checks, and validation steps for the combined file.
Security Audit
SafeAll static findings were false positives caused by Markdown code fences, documented CSV command examples, and expected local CSV file handling. No evidence found of prompt injection, data exfiltration, hidden command execution, or host reconnaissance intent in SKILL.md.
Risk Factors
⚙️ External commands (68)
📁 Filesystem access (3)
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https://skillstore.io/skills/curiouslearner-csv-processor/audits/8?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportMarkdown badge
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<iframe src="https://skillstore.io/embed/skills/curiouslearner-csv-processor.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>Academic citations (APA · BibTeX · CFF)
APA citation
CuriousLearner. (2026). csv-processor security audit report (audit version 8) [Author version unspecified]. Skillstore. https://skillstore.io/skills/curiouslearner-csv-processor/audits/8BibTeX citation
@techreport{curiouslearner-curiouslearner-csv-processor-2026,
author = {CuriousLearner},
title = {csv-processor security audit report (audit version 8)},
institution = {Skillstore},
year = {2026},
number = {8},
url = {https://skillstore.io/skills/curiouslearner-csv-processor/audits/8},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "csv-processor security audit report (audit version 8)"
version: "unspecified"
type: report
authors:
- name: "CuriousLearner"
date-released: "2026-07-06"
url: "https://skillstore.io/skills/curiouslearner-csv-processor/audits/8"
identifiers:
- type: other
value: "skillstore:curiouslearner-csv-processor:audit:8"
description: "Skillstore immutable audit report identifier"
Skillstore Score
Why this score Evidence Confidence: HighWhat You Can Build
Clean customer exports
Remove duplicates, normalize emails, validate required fields, and prepare a clean CSV for import.
Profile data quality
Summarize missing values, data types, duplicates, unique counts, and outliers before analysis.
Merge reporting files
Combine monthly CSV exports, join related tables, and build grouped metrics for reports.
Try These Prompts
Use the csv-processor skill to inspect my CSV file. Identify columns, likely data types, missing values, duplicates, and immediate quality issues.
Use the csv-processor skill to clean this CSV. Trim whitespace, remove duplicate rows, standardize email casing, handle missing values, and list each transformation.
Use the csv-processor skill to analyze this CSV. Group results by the relevant category, calculate totals and averages, find outliers, and summarize key findings.
Use the csv-processor skill to design a repeatable CSV workflow. Include parsing options, validation rules, cleaning steps, merge logic, large-file handling, and output checks.
Best Practices
- Profile the CSV before transforming it, including data types, missing values, duplicates, and encoding.
- Test transformations on a small sample before processing the full dataset.
- Keep an unchanged copy of source files and document every transformation step.
Avoid
- Do not assume delimiters, encodings, headers, or date formats without inspection.
- Do not overwrite source CSV files before validating the transformed output.
- Do not use row filters or joins without checking record counts before and after processing.
Frequently Asked Questions
Can this skill handle large CSV files?
Does it support Python and JavaScript workflows?
Can it detect delimiters and encodings?
Can it generate data quality reports?
Does it include command-line CSV tools?
Does it automatically modify my files?
Developer Details
Author
CuriousLearnerLicense
MIT
Skillstore revision
r1
Version notice
The author did not declare a version.
Ref
d11af420e07cd866fe39cfa937efef3223896c06
Maintenance freshness
7/18/2026
Usage
7 downloads · 282 views
File structure
📄 SKILL.md