Scientific Problem Selection
AnthropicOfficialPublished by Anthropic
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- Science and research
- Apache-2.0
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
Scientific Problem Selection provides a structured, conversational approach to navigating the research lifecycle. Based on the principles of problem choice over execution quality, this skill helps researchers move from initial ideation to strategic planning. It guides users through nine specific modules—including intuition pumps, risk assessment, and decision tree navigation—to ensure that time is invested in high-impact, feasible problems. By treating research as a series of testable assumptions and strategic decision points, the framework helps scientists avoid common pitfalls, manage adversity, and optimize their research strategy.
What it does
- Facilitates project ideation using structured intuition pumps.
- Performs rigorous risk assessment and assumption analysis.
- Defines success metrics through optimization functions.
- Navigates complex decision trees for project troubleshooting.
- Provides strategies for adversity response and problem inversion.
- Synthesizes research plans into coherent communication packages.
When to use it
- When choosing a new thesis, postdoc, or lab research direction.
- When a project is stuck and requires a strategic pivot.
- When evaluating the feasibility and impact of a new research idea.
- When planning long-term research strategy or navigating career transitions.
- When preparing for committee meetings or grant applications.
Advantages
- Increases research impact by prioritizing problem selection over incremental execution.
- Reduces time wasted on dead-end projects through early risk identification.
- Provides a repeatable, evidence-based framework for scientific decision-making.
- Helps transform crises into opportunities for project growth.
- Encourages objective evaluation of biological and technical assumptions.
Usage examples
- “I have an idea for a new project involving CRISPR, but I am not sure if it is worth pursuing.”
- The agent initiates the ideation workflow, asking for a summary and guiding the user through intuition pumps to refine the idea and assess its potential impact.
- “My current experiment is failing and I am stuck. Can you help me troubleshoot?”
- The agent shifts to the troubleshooting workflow, helping the user identify the core problem, analyze assumptions, and navigate the decision tree to find a path forward.
- “I need to evaluate the risks of my proposed research strategy.”
- The agent leads the user through a systematic risk assessment, helping to list assumptions, score them by risk level and time-to-test, and identify potential red flags.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/anthropics/scientific-problem-selection/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip scientific-problem-selection.zip -d .agents/skills
Documentation by Anthropic
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.
Files
- ATTRIBUTION.mdAdded by AutoGineer1004 B
- LICENSE.txt10.0 KiB
- SKILL.md10.9 KiB
- references/01-intuition-pumps.md12.2 KiB
- references/02-risk-assessment.md12.9 KiB
- references/03-optimization-function.md17.7 KiB
- references/04-parameter-strategy.md14.1 KiB
- references/05-decision-tree.md3.2 KiB
- references/06-adversity-planning.md4.6 KiB
- references/07-problem-inversion.md5.5 KiB
- references/08-integration-synthesis.md5.3 KiB
- references/09-meta-framework.md16.2 KiB
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Requirements and compatibility
Written for: Claude Code, Claude apps
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
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Attribution and licence
- Published by
- Anthropic
- Publisher
- Anthropic (Official)
- Source
- anthropics/knowledge-work-plugins · bio-research/skills/scientific-problem-selection · commit e050c2e
- Licence
- Apache License 2.0 (redistribution allowed)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
- Also distributed in
- anthropics/life-sciences
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