prompt-engineering-patterns
Pass
Audited by Gen Agent Trust Hub on Apr 30, 2026
Risk Level: SAFE
Full Analysis
- [EXTERNAL_DOWNLOADS]: The skill snippets and documentation reference well-known libraries and services including LangChain, Anthropic, OpenAI, Sentence Transformers, and Scikit-learn. These are standard tools for AI development and are used within their intended scope.
- [COMMAND_EXECUTION]: The skill includes a Python script (
scripts/optimize-prompt.py) designed for A/B testing and prompt optimization. Analysis of the script confirms it performs string manipulation, arithmetic for metric calculation, and LLM calls via a client interface, without executing arbitrary shell commands or untrusted external code. - [DATA_EXFILTRATION]: No unauthorized network operations or access to sensitive local files (such as credentials or SSH keys) were found. The code examples demonstrate standard API usage with official SDKs.
- [PROMPT_INJECTION]: While the skill explicitly teaches prompt engineering (which involves crafting instructions for LLMs), it does not contain malicious injection attempts targeting the agent's own safety protocols or system instructions. It includes sections on designing safety guidelines and constraints.
- [INDIRECT_PROMPT_INJECTION]: The skill provides numerous templates and functions that use direct string interpolation (e.g.,
{text}or.format()) to build prompts. This is a common pattern in prompt engineering that defines an attack surface for indirect injection if the resulting prompts are used to process untrusted user data without sufficient boundary markers or sanitization. However, the skill also promotes the use of Pydantic for structured output validation, which serves as a defensive measure.
Audit Metadata