prompt-engineering-patterns
Pass
Audited by Gen Agent Trust Hub on Mar 28, 2026
Risk Level: SAFEPROMPT_INJECTION
Full Analysis
- [SAFE]: The skill is primarily educational, providing well-documented patterns for few-shot learning, chain-of-thought prompting, and prompt optimization.\n- [SAFE]: Code snippets in the references and the optimization script use standard Python libraries such as numpy, scikit-learn, and sentence-transformers for utility purposes like semantic similarity and metric calculation.\n- [SAFE]: No evidence of hardcoded credentials, malicious network operations, or obfuscation was found across the analyzed files.\n- [PROMPT_INJECTION]: The skill implements prompt template systems and optimization scripts that interpolate variables into prompts, which constitutes an indirect prompt injection surface.\n
- Ingestion points: Ingestion of user-provided variables occurs in
PromptOptimizer.evaluate_prompt(scripts/optimize-prompt.py) andPromptTemplate.render(references/prompt-templates.md).\n - Boundary markers: No explicit boundary markers or instructions to ignore embedded commands are present in the rendering logic.\n
- Capability inventory: Interpolated prompts are executed via an LLM client (e.g.,
client.completeoropenai.ChatCompletion.create) across multiple reference scripts.\n - Sanitization: There is no evidence of input sanitization, escaping, or validation of interpolated variables before they are sent to the model.
Audit Metadata