Helps scientists choose research problems, assess risks, and plan strategy.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "scientific-problem-selection" skill from askskill: 1. Download https://raw.githubusercontent.com/anthropics/knowledge-work-plugins/main/bio-research/skills/scientific-problem-selection/SKILL.md 2. Save it as ~/.claude/skills/scientific-problem-selection/SKILL.md 3. Reload skills and tell me it's ready
I’m deciding between single-cell sequencing and spatial transcriptomics. Compare them based on novelty, feasibility, required resources, publication potential, and my background, then recommend what I should prioritize next.
A structured comparison with decision criteria, key risks, and a clear recommendation.
My research project has been stuck for two months: the experimental results are unstable and the hypothesis is no longer clear. Help diagnose possible causes, list testable hypotheses, and propose a two-week troubleshooting plan.
A diagnostic framework, prioritized troubleshooting paths, and a short-term action plan.
I have a new project idea to study how a drug affects aging-related pathways. Evaluate its scientific value, feasibility, potential pitfalls, minimum viable study design, and whether it is worth investing six months in.
An assessment of project value, risks, a minimum viable plan, and a go/no-go recommendation.
A conversational framework for systematic scientific problem selection based on Fischbach & Walsh's "Problem choice and decision trees in science and engineering" (Cell, 2024).
Present users with three entry points:
1) Pitch an idea for a new project — to work it up together
2) Share a problem in a current project — to troubleshoot together
3) Ask a strategic question — to navigate the decision tree together
This conversational entry meets scientists where they are and establishes a collaborative tone.
Ask: "Tell me the short version of your idea (1-2 sentences)."
After the user shares their idea, return a quick summary (no more than one paragraph) demonstrating understanding. Note the general area of research and rephrase the idea in a way that highlights its kernel—showing alignment and readiness to dive into details.
Then ask for more detail: "Now give me a bit more detail. You might include, however briefly or even say where you are unsure:
From there, guide the user through the early stages of problem selection and evaluation:
See references/01-intuition-pumps.md, references/02-risk-assessment.md, references/03-optimization-function.md, and references/04-parameter-strategy.md for detailed guidance.
Ask: "Tell me a short version of your problem (1-2 sentences or whatever is easy)."
After the user shares their problem, return a quick summary (no more than one paragraph) demonstrating understanding. Note the context of the project where the problem occurred and rephrase the problem—highlighting its core essence—so the user knows the situation is understood. Also raise additional questions that seem important to discuss.
Then ask: "Now give me a bit more detail. You might include, however briefly:
From there, guide the user through troubleshooting and decision tree navigation:
Always include workarounds that might be useful whether or not the problem can be fixed easily.
See references/05-decision-tree.md, references/06-adversity-planning.md, references/07-problem-inversion.md, and references/04-parameter-strategy.md for detailed guidance.
Ask: "Tell me the short version of your question (1-2 sentences)."
After the user shares their question, return a quick summary (no more than one paragraph) demonstrating understanding. Note the broader context and rephrase the question—highlighting its crux—to confirm alignment with their thinking.
Then ask: "Now give me a bit more detail. You might include, however briefly:
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