A conversational framework for systematic scientific problem selection based on Fischbach & Walsh's "Problem choice and decision trees in science and engineering" (Cell, 2024).
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionscientific-problem-selectionExecute the skills CLI command in your project's root directory to begin installation:
Fetches scientific-problem-selection from anthropics/knowledge-work-plugins and configures it for Cursor.
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Confirm successful installation by checking the skill directory location:
Restart Cursor to activate scientific-problem-selection. Access via /scientific-problem-selection in your agent's command palette.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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:
From there, draw on the specific modules from the problem choice framework most appropriate to the question:
See the complete reference materials in the references/ folder.
Problem Choice >> Execution Quality
Even brilliant execution of a mediocre problem yields incremental impact. Good execution of an important problem yields substantial impact.
Scientists typically spend:
This imbalance limits impact. These skills help invest more time choosing wisely.
For Evaluating Ideas:
Skills help move ideas rightward (more feasible) and upward (more impactful).
| Skill | Purpose | Output | Time |
|---|---|---|---|
| 1. Intuition Pumps | Generate high-quality research ideas | Problem Ideation Document | ~1 week |
| 2. Risk Assessment | Identify and manage project risks | Risk Assessment Matrix | 3-5 days |
| 3. Optimization Function | Define success metrics | Impact Assessment Document | 2-3 days |
| 4. Parameter Strategy | Decide what to fix vs. keep flexible | Parameter Strategy Document | 2-3 days |
| 5. Decision Tree Navigation | Plan decision points and altitude dance | Decision Tree Map | 2 days |
| 6. Adversity Response | Prepare for crises as opportunities | Adversity Playbook | 2 days |
| 7. Problem Inversion | Navigate around obstacles | Problem Inversion Analysis | 1 day |
| 8. Integration & Synthesis | Synthesize into coherent plan | Project Communication Package | 3-5 days |
| 9. Meta-Framework | Orchestrate complete workflow | Complete Project Package | 1-6 weeks |
SKILL 1: Intuition Pumps
| (generates idea)
v
SKILL 2: Risk Assessment
| (evaluates feasibility)
v
SKILL 3: Optimization Function
| (defines success metrics)
v
SKILL 4: Parameter Strategy
| (determines flexibility)
v
SKILL 5: Decision Tree
| (plans execution and evaluation)
v
SKILL 6: Adversity Planning
| (prepares for failure modes)
v
SKILL 7: Problem Inversion
| (provides pivot strategies)
v
SKILL 8: Integration & Communication
| (synthesizes into coherent plan)
v
SKILL 9: Meta-Skill
(orchestrates complete workflow)
Detailed skill documentation is available in the references/ folder:
| File | Content | Search Patterns |
|---|---|---|
01-intuition-pumps.md |
Generate research ideas | Intuition Pump #, Trap #, Phase [0-9] |
02-risk-assessment.md |
Risk identification | Risk.*1-5, go/no-go, assumption |
03-optimization-function.md |
Success metrics | Generality.*Learning, optimization, impact |
04-parameter-strategy.md |
Parameter fixation | fixed.*float, constraint, parameter |
05-decision-tree.md |
Decision tree navigation | altitude, Level [0-9], decision |
06-adversity-planning.md |
Adversity response | adversity, crisis, ensemble |
07-problem-inversion.md |
Problem inversion strategies | Strategy [0-9], inversion, goal |
08-integration-synthesis.md |
Integration and synthesis | narrative, communication, story |
09-meta-framework.md |
Complete workflow | Phase, workflow, orchestrat |
Fischbach, M.A., & Walsh, C.T. (2024). "Problem choice and decision trees in science and engineering." Cell, 187, 1828-1833.
Based on course BIOE 395 taught at Stanford University.
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
Solid pick for teams standardizing on skills: scientific-problem-selection is focused, and the summary matches what you get after install.
scientific-problem-selection is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
We added scientific-problem-selection from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
scientific-problem-selection reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend scientific-problem-selection for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
scientific-problem-selection fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for scientific-problem-selection matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in scientific-problem-selection — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
scientific-problem-selection is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: scientific-problem-selection is the kind of skill you can hand to a new teammate without a long onboarding doc.
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