Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework.
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Low
Low-risk findings worth noting
Low
Low-risk findings.
2 low severity findings. Worth noting, but not necessarily harmful.
The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.
The required workflow enables “conduct literature-based hypothesis generation” via WebFetch/WebSearch, which can ingest outsider-authored web page text into the LLM context at runtime (indirect prompt-injection risk).
The skill fetches instructions or code from an external URL at runtime, and the fetched content directly controls the agent’s prompts or executes code. This dynamic dependency allows the external source to modify the agent’s behavior without any changes to the skill itself.
The skill's runtime scripts call the OpenRouter API (e.g., https://openrouter.ai/api/v1) to generate images and to obtain model critiques that are then used to modify prompts (generate_schematic.py / generate_schematic_ai.py), so an external URL is used at runtime to directly control agent prompts and outputs.
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