data-quality-frameworks-sickn33
Pass
Audited by Gen Agent Trust Hub on Jun 28, 2026
Risk Level: SAFEPROMPT_INJECTIONEXTERNAL_DOWNLOADSDATA_EXFILTRATION
Full Analysis
- [PROMPT_INJECTION]: Indirect prompt injection surface identified in resources/implementation-playbook.md.\n * Ingestion points: Data validation results containing values from external datasets are interpolated into a Markdown report via the generate_report method in implementation-playbook.md.\n * Boundary markers: Absent. No delimiters or warnings are used in the report generation logic to isolate untrusted data.\n * Capability inventory: The skill uses Python and the Great Expectations library, with the ability to perform file operations and send network notifications via Slack.\n * Sanitization: Absent. The code does not sanitize observed_value before interpolation into the markdown report.\n- [EXTERNAL_DOWNLOADS]: The skill documentation references installing the great_expectations package from PyPI, which is a standard and well-known library for data quality validation.\n- [DATA_EXFILTRATION]: Provides a pattern for Slack notifications using the ${SLACK_WEBHOOK} environment variable in resources/implementation-playbook.md. This is a standard operational practice for alerting and does not involve hardcoded credentials.
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