explore-data — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited explore-data (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
Aggregate score unchanged between these scans.
The primary manifest — the file an agent reads to learn what this artifact does.
Connect to a dataset, profile its structure and quality, and recommend what analyses to run next.
connect_database with the path or connection string from $ARGUMENTS. Note the assigned database name in the response.describe_database with the database name. Record the list of tables, column names, and column types. Identify which columns are numeric, categorical, datetime, and text.execute_query with SELECT * FROM <table> LIMIT 10. Inspect the returned rows to understand value ranges, formats, and potential join keys.describe_table to get detailed column statistics including cardinality, null counts, and value distributions.get_data_quality_report with the database name. Review completeness, uniqueness, and consistency scores. Flag columns with high null rates (above 20%) or low cardinality that may need attention./localdata-mcp:data-quality for a thorough audit/localdata-mcp:analyze-correlations/localdata-mcp:forecast/localdata-mcp:cluster-analysis or /localdata-mcp:dimensionality-reduction/localdata-mcp:regression/localdata-mcp:ab-test/localdata-mcp:geospatial/localdata-mcp:graph-data-explore/localdata-mcp:find-reference-data/localdata-mcp:process-control/localdata-mcp:anomaly-detection~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.