prioritization

Name: prioritization
Availability: InStock
Author: openscientist-io

Rank hypotheses by impact, feasibility, and novelty to select the next research action.

44|13|Updated Nov 15, 2025

Search Tags:#data-analysis #hypotheses #decision-tree #prioritize #literature-search #experimental-design #iteration-management

Ready to run

npx skills add https://github.com/openscientist-io/openscientist --skill prioritization-openscientist-io

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: prioritization
Source: https://github.com/openscientist-io/openscientist/tree/main/skills/workflow/prioritization
Command: npx skills add https://github.com/openscientist-io/openscientist --skill prioritization-openscientist-io

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Prioritization guides scientific work by selecting the most impactful, feasible, and novel hypothesis to test next when many options exist.

Core Features & Use Cases

Ranks hypotheses by Impact, Feasibility, and Novelty and executes the top-scoring test first.
Supports decision-making between testing, exploring data, searching literature, recording findings, and synthesizing results across iterations.
Provides iteration-phase guidance (early, middle, late) to adapt actions as the project evolves.

Quick Start

Ask the agent to determine the next high-priority action when multiple hypotheses compete for limited iteration budget.

Frequently Asked Questions about prioritization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I decide which hypothesis to test next when managing an iterative experimental design?▼

Hypothesis prioritization scores competing research options by impact, feasibility, and novelty to select the most valuable test. It evaluates your current iteration state and budget constraints to execute the top-scoring experiment first.

What is the best way to prioritize data exploration tasks during a scientific workflow?▼

The best way to prioritize data exploration is evaluating whether to test, explore data, search literature, or synthesize results based on project phase. Iteration-phase guidance adapts your chosen actions as the data-rich project evolves.

Can I use automated decision trees to select research actions under a limited iteration budget?▼

Yes, you can automate decision-making to select research actions under budget constraints by scoring hypotheses against impact, feasibility, and novelty. This requires a current iteration state, budget constraint, and access to relevant data.

Does literature search integration help determine what experiments to run next?▼

Literature search integration helps determine what experiments to run next by incorporating existing knowledge into the hypothesis scoring process. It functions as a selectable action alongside data exploration and synthesis during iteration management.

When should I synthesize results instead of testing a new hypothesis in data analysis?▼

You should synthesize results instead of testing a new hypothesis during late iteration phases or when budget constraints limit further experiments. The decision framework shifts guidance toward recording findings and synthesizing data as projects mature.