scientific-problem-selection

What it does

This skill enables AI agents to autonomously identify, formulate, and prioritize research questions based on provided datasets or domain constraints. It transforms raw information into structured, actionable scientific inquiries ready for investigation.

When to use it

• You have a large dataset but lack the expertise to derive meaningful hypotheses from it.
• You need an AI agent to scan literature or data sources and generate a ranked list of potential research topics.
• You want to automate the initial phase of a research project by defining clear problem statements before execution.

Key capabilities

• Analyzes input data or context to detect gaps, anomalies, or trends suitable for inquiry.
• Generates specific, well-defined scientific questions rather than vague topics.
• Prioritizes identified problems based on feasibility, significance, or relevance to user goals.

Example prompts

• "Review the attached climate data and select three critical research problems that address recent temperature anomalies."
• "Analyze this list of 50 open-source biology papers and formulate one high-priority problem statement for further study."
• "Given my constraints on computational resources, identify the most tractable scientific question in quantum computing from this summary."

Tips & gotchas

Ensure your input data is sufficiently detailed; vague inputs will result in generic or low-quality problem formulations. This skill focuses on selection and formulation, so it does not execute experiments or solve the problems itself—those steps require separate skills.