evaluating-llms-harness
Pass
Audited by Gen Agent Trust Hub on May 16, 2026
Risk Level: SAFE
Full Analysis
- [EXTERNAL_DOWNLOADS]: The documentation recommends installing standard, well-known machine learning and benchmarking libraries such as lm-eval, transformers, and vllm from official package registries.
- [COMMAND_EXECUTION]: Workflow examples demonstrate the use of shell commands and Python's os.system to automate periodic model evaluations and performance comparisons.
- [REMOTE_CODE_EXECUTION]: The skill documents the intended use of the allow_code_execution flag required for functional correctness benchmarks like HumanEval, which involves executing model-generated code in a controlled manner.
- [DYNAMIC_EXECUTION]: Detailed guides are provided for creating custom evaluation tasks that utilize Python-based utility functions for specialized data processing and metric aggregation.
- [SAFE]: No malicious obfuscation, unauthorized data access, or persistence mechanisms were detected. The skill follows security best practices by recommending environment variables for API key management.
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