scientific-problem-selection▌
anthropics/knowledge-work-plugins · updated Apr 8, 2026
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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).
Scientific Problem Selection Skills
A conversational framework for systematic scientific problem selection based on Fischbach & Walsh's "Problem choice and decision trees in science and engineering" (Cell, 2024).
Getting Started
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.
Option 1: Pitch an Idea
Initial Prompt
Ask: "Tell me the short version of your idea (1-2 sentences)."
Response Approach
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.
Follow-up Prompt
Then ask for more detail: "Now give me a bit more detail. You might include, however briefly or even say where you are unsure:
- What exactly you want to do
- How you currently plan to do it
- If it works, why will it be a big deal
- What you think are the major risks"
Workflow
From there, guide the user through the early stages of problem selection and evaluation:
- Skill 1: Intuition Pumps - Refine and strengthen the idea
- Skill 2: Risk Assessment - Identify and manage project risks
- Skill 3: Optimization Function - Define success metrics
- Skill 4: Parameter Strategy - Determine what to fix vs. keep flexible
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.
Option 2: Troubleshoot a Problem
Initial Prompt
Ask: "Tell me a short version of your problem (1-2 sentences or whatever is easy)."
Response Approach
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.
Follow-up Prompt
Then ask: "Now give me a bit more detail. You might include, however briefly:
- The overall goal of your project (if we have not talked about it before)
- What exactly went wrong
- Your current ideas for fixing it"
Workflow
From there, guide the user through troubleshooting and decision tree navigation:
- Skill 5: Decision Tree Navigation - Plan decision points and navigate between execution and strategic thinking
- Skill 4: Parameter Strategy - Fix one parameter at a time, let others float
- Skill 6: Adversity Response - Frame problems as opportunities for growth
- Skill 7: Problem Inversion - Strategies for navigating around obstacles
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.
Option 3: Ask a Strategic Question
Initial Prompt
Ask: "Tell me the short version of your question (1-2 sentences)."
Response Approach
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.
Follow-up Prompt
Then ask: "Now give me a bit more detail. You might include, however briefly:
- The setting (i.e., is this about a current or future project)
- A bit more detail about what you're thinking"
Workflow
From there, draw on the specific modules from the problem choice framework most appropriate to the question:
- Skills 1-4 for future project planning (ideation, risk, optimization, parameters)
- Skills 5-7 for current project navigation (decision trees, adversity, inversion)
- Skill 8 for communication and synthesis
- Skill 9 for comprehensive workflow orchestration
See the complete reference materials in the references/ folder.
Core Framework Concepts
The Central Insight
Problem Choice >> Execution Quality
Even brilliant execution of a mediocre problem yields incremental impact. Good execution of an important problem yields substantial impact.
The Time Paradox
Scientists typically spend:
- Days choosing a problem
- Years solving it
This imbalance limits impact. These skills help invest more time choosing wisely.
Evaluation Axes
For Evaluating Ideas:
- X-axis: Likelihood of success
- Y-axis: Impact if successful
Skills help move ideas rightward (more feasible) and upward (more impactful).
The Risk Paradox
- Don't avoid risk—befriend it
- No risk = incremental work
- But: Multiple miracles = avoid or refine
- Balance: Understood, quantified, manageable risk
The Parameter Paradox
- Too many fixed = brittleness
- Too few fixed = paralysis
- Sweet spot: Fix ONE meaningful constraint
The Adversity Principle
- Crises are inevitable (don't be surprised)
- Crises are opportune (don't waste them)
- Strategy: Fix problem AND upgrade project simultaneously
The 9 Skills Overview
| 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 Workflow
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)
Key Design Principles
- Conversational Entry - Meet users where they are with three clear starting points
- Thoughtful Interaction - Ask clarifying questions; low confidence prompts additional input
- Literature Integration - Use PubMed searches at strategic points for validation
- Concrete Outputs - Every skill produces tangible 1-2 page documents
- Building Specificity - Progressive detail emerges through targeted questions
- Flexibility - Skills work independently, sequentially, or iteratively
- Scientific Rigor - Claims about generality and feasibility should be evidence-based
Who Should Use These Skills
Graduate Students (Primary Audience)
- When: Choosing thesis projects, qualifying exams, committee meetings
- Focus: Skills 1-3 (ideation, risk, impact) + Skill 9 (complete workflow)
- Timeline: 2-4 weeks for comprehensive planning
Postdocs
- When: Starting new position, planning independent projects, fellowship applications
- Focus: All skills, emphasizing independence and risk management
- Timeline: 1-2 weeks intensive planning
Principal Investigators
- When: New lab, new direction, mentoring trainees, grant cycles
- Focus: Skills 1, 3, 4, 6 (ideation, impact, parameters, adversity)
- Timeline: Ongoing, integrate into lab culture
Startup Founders
- When: Company inception, pivot decisions, investor pitches
- Focus: Skills 1-4 (ideation through parameters) + Skill 8 (communication)
- Timeline: 1-2 weeks for initial planning, revisit quarterly
Reference Materials
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 |
Expected Outcomes
Immediate (After Completing Workflow)
- Clear project vision
- Honest risk assessment
- Contingency plans
- Communication materials ready
- Confidence in problem choice
6-Month
- Faster decisions (have framework)
- Productive adversity handling
- No existential crises (risks mitigated)
2-Year
- Published results or strong progress
- Avoided dead-end projects
- Career aligned with goals
- Time well-spent (ultimate measure)
Foundational Reference
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.
How to use scientific-problem-selection on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add scientific-problem-selection
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches scientific-problem-selection from GitHub repository anthropics/knowledge-work-plugins and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate scientific-problem-selection. Access the skill through slash commands (e.g., /scientific-problem-selection) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
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
Competitive Analysis
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
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ 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.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★26 reviews- ★★★★★Rahul Santra· Dec 16, 2024
Solid pick for teams standardizing on skills: scientific-problem-selection is focused, and the summary matches what you get after install.
- ★★★★★Xiao Malhotra· Dec 16, 2024
scientific-problem-selection is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Pratham Ware· Nov 7, 2024
We added scientific-problem-selection from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Ira Wang· Nov 7, 2024
scientific-problem-selection reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Dhruvi Jain· Nov 3, 2024
I recommend scientific-problem-selection for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Sakshi Patil· Oct 26, 2024
scientific-problem-selection fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Daniel Singh· Oct 26, 2024
Registry listing for scientific-problem-selection matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Oshnikdeep· Oct 22, 2024
Useful defaults in scientific-problem-selection — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Kiara Srinivasan· Sep 25, 2024
scientific-problem-selection is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Kiara Iyer· Aug 16, 2024
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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