dspy-gepa-optimizer
Pass
Audited by Gen Agent Trust Hub on Apr 22, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: The skill documentation and accompanying Python scripts are legitimate technical resources for the DSPy framework. They demonstrate standard optimization workflows without any suspicious behavior or malicious code.
- [PROMPT_INJECTION]: The optimizer ingests training and validation datasets to perform reflective optimization of LLM prompts. While this is the intended purpose of the tool, it constitutes an interface where external data influences the generated instructions. 1. Ingestion points: trainset and valset variables in example_gepa.py and example_bettertogether.py. 2. Boundary markers: None; the data is treated as ground truth for the optimization process. 3. Capability inventory: Performs automated prompt mutation and evaluation using dspy.GEPA and dspy.BetterTogether classes. 4. Sanitization: None; the tool assumes input data is trusted for the purpose of training.
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