temporal-circadian-patterns
Pass
Audited by Gen Agent Trust Hub on Jul 5, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: No security issues were detected. The skill provides legitimate analytical guidelines and boilerplate code for time-series data processing.
- [PROMPT_INJECTION]: No evidence of prompt injection or behavioral overrides found. Instructions are focused on data analysis tasks.
- [DATA_EXFILTRATION]: No network operations or sensitive file access patterns detected. All code snippets operate on data provided within a pandas DataFrame and do not attempt to transmit data externally.
- [EXTERNAL_DOWNLOADS]: The skill references well-known libraries (pandas, numpy, scikit-learn, scipy, statsmodels) which are standard in the data science ecosystem. No untrusted third-party dependencies or remote script executions are present.
- [COMMAND_EXECUTION]: No shell commands, subprocess spawning, or administrative privilege requests detected. The functions provided are limited to mathematical and statistical operations.
- [INDIRECT_PROMPT_INJECTION]: While the skill processes external timestamp data (ingestion point:
dfinvalidate_timestamps), it lacks dangerous capabilities (no file-write, network, or execution sinks). It employs standard data validation viapd.to_datetime(errors='coerce')to handle malformed input.
Audit Metadata