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Scientific Problem Selection

OrganizationPopular
anthropics
scientific-problem-selection

This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions. Use this skill when users ask to pitch a new research idea, work through a project problem, evaluate project risks, plan research strategy, navigate decision trees, or get help choosing what scientific problem to work on. Typical requests include "I have an idea for a project", "I'm stuck on my research", "help me evaluate this project", "what should I work on", or "I need strategic advice about my research".

Overview

Publisheranthropics
Repositoryknowledge-work-plugins
Skill namescientific-problem-selection
Stars
24.9K
Forks
3K
Bundled files
9
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 9 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by anthropics on GitHub. Read the source before you install it.

Installation

Install the Scientific Problem Selection AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/anthropics/knowledge-work-plugins.git /tmp/knowledge-work-plugins
mkdir -p .claude/skills
cp -r /tmp/knowledge-work-plugins/bio-research/skills/scientific-problem-selection .claude/skills/scientific-problem-selection
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Scientific Problem Selection in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Scientific Problem Selection on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Scientific Problem Selection is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

  1. What exactly you want to do
  2. How you currently plan to do it
  3. If it works, why will it be a big deal
  4. 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:

  1. The overall goal of your project (if we have not talked about it before)
  2. What exactly went wrong
  3. 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:

  1. The setting (i.e., is this about a current or future project)
  2. 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

SkillPurposeOutputTime
1. Intuition PumpsGenerate high-quality research ideasProblem Ideation Document~1 week
2. Risk AssessmentIdentify and manage project risksRisk Assessment Matrix3-5 days
3. Optimization FunctionDefine success metricsImpact Assessment Document2-3 days
4. Parameter StrategyDecide what to fix vs. keep flexibleParameter Strategy Document2-3 days
5. Decision Tree NavigationPlan decision points and altitude danceDecision Tree Map2 days
6. Adversity ResponsePrepare for crises as opportunitiesAdversity Playbook2 days
7. Problem InversionNavigate around obstaclesProblem Inversion Analysis1 day
8. Integration & SynthesisSynthesize into coherent planProject Communication Package3-5 days
9. Meta-FrameworkOrchestrate complete workflowComplete Project Package1-6 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

  1. Conversational Entry - Meet users where they are with three clear starting points
  2. Thoughtful Interaction - Ask clarifying questions; low confidence prompts additional input
  3. Literature Integration - Use PubMed searches at strategic points for validation
  4. Concrete Outputs - Every skill produces tangible 1-2 page documents
  5. Building Specificity - Progressive detail emerges through targeted questions
  6. Flexibility - Skills work independently, sequentially, or iteratively
  7. 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:

FileContentSearch Patterns
01-intuition-pumps.mdGenerate research ideasIntuition Pump #, Trap #, Phase [0-9]
02-risk-assessment.mdRisk identificationRisk.*1-5, go/no-go, assumption
03-optimization-function.mdSuccess metricsGenerality.*Learning, optimization, impact
04-parameter-strategy.mdParameter fixationfixed.*float, constraint, parameter
05-decision-tree.mdDecision tree navigationaltitude, Level [0-9], decision
06-adversity-planning.mdAdversity responseadversity, crisis, ensemble
07-problem-inversion.mdProblem inversion strategiesStrategy [0-9], inversion, goal
08-integration-synthesis.mdIntegration and synthesisnarrative, communication, story
09-meta-framework.mdComplete workflowPhase, 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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Scientific Problem Selection AI skill do?

This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions. Use this skill when users ask to pitch a new research idea, work through a project problem, evaluate project risks, plan research strategy, navigate decision trees, or get help choosing what scientific problem to work on. Typical requests include "I have an idea for a project", "I'm stuck on my research", "help me evaluate this project", "what should I work on", or "I need strategic advice about my research".

Why use Scientific Problem Selection on TypingMind?

Because you install it once and use it with any model. Scientific Problem Selection is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Scientific Problem Selection in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/anthropics/knowledge-work-plugins/tree/main/bio-research/skills/scientific-problem-selection. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Scientific Problem Selection?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Scientific Problem Selection?

As many as you like. As long as a model supports skills, you can use Scientific Problem Selection with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Scientific Problem Selection AI skill free?

Yes. It is published on GitHub by anthropics under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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