prompt-engineering-patterns
Pass
Audited by Gen Agent Trust Hub on Mar 22, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: The skill provides a robust framework for prompt optimization and few-shot learning. All external dependencies (e.g., Anthropic, OpenAI, LangChain) are well-known, industry-standard services. The code patterns demonstrated reflect established engineering paradigms for structured LLM interactions.
- [PROMPT_INJECTION]: The skill involves dynamic prompt construction, which is a common surface for indirect prompt injection. This is an inherent characteristic of prompt engineering tools.
- Ingestion points:
scripts/optimize-prompt.py(interpolates test case inputs into templates) andreferences/prompt-templates.md(implements a custom template rendering system). - Boundary markers: Most provided templates utilize clear delimiters (e.g., 'Article:', 'Context:', 'Problem:') to differentiate instructions from user data.
- Capability inventory: The skill is designed to interact with LLM APIs to process and evaluate text.
- Sanitization: The examples focus on functional implementation; production systems should include content validation to ensure interpolated data does not contain control-sequence attempts.
Audit Metadata