Dataset Intake Auditor
v1.0.0在新数据集接入前检查字段、单位、缺失率、异常值与可用性。；use for data, dataset, audit workflows；do not use for 伪造统计结果, 替代正式数据治理平台.
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byvx：17605205782@52yuanchangxing
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description (dataset intake audit) match the included files and scripts. The only required binary is python3 and the code uses only the standard library. There are no environment variables, external credentials, or unexpected binaries requested.
Instruction Scope
SKILL.md instructs the agent to run the included scripts/run.py or to produce output from local templates if execution is not available. The script is designed primarily for CSV/TSV auditing (spec.mode is 'csv_audit'), but it also implements directory and pattern-audit helpers that can read many text file types (md, py, sh, json, csv, etc.). This is expected for an audit tool, but it means the tool will read any files the user points it at — so avoid pointing it at system/root directories or folders containing secrets or unrelated code unless you intend that.
Install Mechanism
No install spec is provided (instruction-only with an included local script). No downloads, package installs, or archive extraction are performed by the skill. This is low-risk from an install standpoint.
Credentials
The skill declares no required environment variables or credentials. The code does not reference external API keys or secret config. This is proportionate to its stated purpose.
Persistence & Privilege
always is false and the skill does not request persistent privileges or modify other skills or global config. The bundle is local and runs only when invoked.
Assessment
This skill appears to do what it says: local, read-only dataset auditing via a bundled Python script. Before running: (1) inspect scripts/run.py yourself (it's included) and run smoke tests; (2) invoke it only on intended dataset files or a dedicated workspace — do not point it at system or credential-containing directories; (3) if outputs will be shared with external systems or pasted into chats, scrub any sensitive values (the tool can read many file types and may surface snippets); (4) you can run with --dry-run or on small sample files first. If you need networked ingestion, pipeline integrations, or automated writes, plan authorization and gating outside this skill.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.
Runtime requirements
🧺 Clawdis
OSmacOS · Linux · Windows
Binspython3