data-quality-checker
v1.0.0Validate CSV, JSON, and JSONL data files for quality issues. Detects missing values, duplicates, type inconsistencies, statistical outliers, format violation...
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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
medium confidencePurpose & Capability
Name/description match the included script: the code implements CSV/JSON/JSONL loading and the listed quality checks (missing, duplicates, types, outliers, formats, whitespace, empty, drift). No unrelated binaries, env vars, or services are requested.
Instruction Scope
SKILL.md instructs running the included Python script against local data files and generating reports; the instructions do not ask the agent to read unrelated system files, credentials, or transmit data externally.
Install Mechanism
No install spec (instruction-only + bundled script). This is low risk: nothing is downloaded or installed automatically by the skill.
Credentials
The skill declares no required environment variables or credentials and the visible code does not access environment secrets or configuration. No excessive permissions are requested.
Persistence & Privilege
The skill is not marked always:true and does not attempt to modify system or other skills' configurations in the visible code. Autonomous invocation is allowed (platform default) but not combined with other red flags.
Assessment
This skill appears to do what it says: run the included Python script on local CSV/JSON files to produce a data‑quality report. Before installing or running on sensitive data: 1) review the entire scripts/check_data_quality.py file — the listing you provided is truncated, and I couldn't inspect the file tail where networking or other behavior could appear; 2) run it first on non-sensitive sample data in an isolated environment; 3) check memory/CPU behavior on large files (the tool appears in-memory and may not stream very large datasets); 4) prefer installing skills from a known/published source (owner and homepage are unknown and STATUS.md notes a price), and 5) if you need to use it in CI on sensitive datasets, consider adding monitoring or sandboxing and/or reimplementing core checks within your vetted tooling.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.