Data Cleaner Skill
v1.1.0AI-powered tool for cleaning Excel/CSV data by removing duplicates, handling missing values, standardizing formats, detecting outliers, and batch processing...
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MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
The name/description match the included code: a Python/pandas-based data cleaner. The README and SKILL.md mention batch processing and a batch_clean.py example, but no batch_clean.py is included in the package — a minor inconsistency. Overall, required tools (pandas/openpyxl) are appropriate for the stated purpose.
Instruction Scope
Runtime instructions and examples only run local Python scripts on local files and request installing pandas/openpyxl. They do not instruct reading unrelated system files or exporting data to external endpoints. One mismatch: the SKILL.md and README call this an 'AI-driven' tool, but the provided script is heuristic/deterministic (no external model/API calls).
Install Mechanism
No install spec; dependencies are installed via standard pip (pandas, openpyxl) as documented. No downloads from untrusted URLs or archive extraction are present.
Credentials
The skill requests no environment variables, no credentials, and no special config paths — proportional to a local data-cleaning tool.
Persistence & Privilege
Skill is not marked always:true and is user-invocable (normal). It does not request persistent system-wide privileges or modify other skills/config.
Assessment
This package appears to be a straightforward local data-cleaner and does not request credentials or make network calls, so risk is low. Before installing/running: 1) note that README/SKILL.md mention batch_clean.py but that file is missing — batch-processing example is incomplete; 2) the script is deterministic (pandas/regex rules), not actually calling any external AI service despite the 'AI-driven' wording; 3) there are small code/documentation mismatches (e.g., --fix-date handling in the CLI and the function signature) but these are bugs, not malicious behavior; 4) sanitize or review outputs if your data may contain untrusted spreadsheet formulas: the tool writes CSV/Excel but does not sanitize leading characters (e.g., =,+,-,@) which can be a CSV/Excel formula-injection risk when opening in spreadsheet apps; 5) run it first on non-sensitive test data or in a sandbox, and inspect the generated _cleaned files to confirm behavior. If you need batch processing, request the missing batch script from the author or implement your own wrapper.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.