Data Quality Check
v2.1.0Assess construction data quality using completeness, accuracy, consistency, timeliness, and validity metrics. Automated validation with regex patterns, thres...
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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 (data quality checks for construction data) aligns with the content: SKILL.md contains Python code and examples that read Excel/CSV/JSON and run completeness/accuracy/consistency checks. Requiring python3 is appropriate for the provided Python snippets.
Instruction Scope
Instructions and code operate on user-provided data files (CSV/Excel/JSON) and include only local processing (pandas, numpy, regex). The instructions explicitly constrain the skill to 'Only use data provided by the user or referenced in the skill.' No network endpoints, external exfiltration, or requests for unrelated system data are present in the provided SKILL.md/instructions.md excerpts.
Install Mechanism
This is an instruction-only skill with no install spec and no downloaded code. That minimizes disk-write/install risk. The only runtime requirement is python3 (no packages are auto-installed by the skill manifest itself).
Credentials
The skill declares no required environment variables, no credentials, and no config paths. That is appropriate: the code only needs access to local data files and standard Python libraries (pandas/numpy/etc. are referenced but not enforced by the manifest).
Persistence & Privilege
claw.json lists 'filesystem' in permissions, which is consistent with reading user data files but does grant the skill the ability to access the agent's filesystem. The skill is not always-enabled and does not request other elevated privileges. Users should be aware that filesystem permission allows reading local files the agent can access.
Assessment
This skill appears coherent for running local data-quality checks in Python. Before installing or invoking it: 1) Confirm you are comfortable granting filesystem access (the skill needs to read user-supplied files). 2) Run it only on non-sensitive/test data first to validate behavior. 3) Ensure a Python environment with required packages (pandas, numpy) is available on your Windows host. 4) Note minor metadata oddities: the package manifest (claw.json) claims filesystem permission and a slightly different version than the registry entry; the publisher/source are not fully documented — consider checking the homepage (https://datadrivenconstruction.io) and verifying the publisher before trusting sensitive data. If you need networked or automated processing of sensitive files, require additional review; for offline, user-supplied file checks this skill looks appropriate.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
OSWindows
Binspython3