Official agent skill

Scientific Problem Selection

by aws-samples in aws-samples/amazon-bedrock-agents-healthcare-lifesciences

This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions.

OfficialApache-2.0Auto-check passedResearch & Science

Install Scientific Problem Selection

skills CLI
$ npx skills add aws-samples/amazon-bedrock-agents-healthcare-lifesciences --skill scientific-problem-selection -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aws-samples/amazon-bedrock-agents-healthcare-lifesciences scientific-problem-selection --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences.git skills-src && mkdir -p .claude/skills && cp -r skills-src/agents_catalog/36-C4LS-example-agent/C4LS/src/skills/scientific-problem-selection .claude/skills/scientific-problem-selection && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
scientific-problem-selection
GitHub stars
274
Used in
4 other repos
Token cost
~2.8k tokens
SKILL.md length
1,215 words
Files
11 (incl. references)
Skills in repo
12
Repo updated
First seen
Licence
Apache-2.0

At a glance

This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions.

  • Works in 4 steps: What exactly you want to do → How you currently plan to do it → If it works, why will it be a big deal → …
  • Users ask to pitch a new research idea
  • SKILL.md covers Getting Started, Option 1: Pitch an Idea, Option 2: Troubleshoot a Problem and Option 3: Ask a Strategic…, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Scientific Problem Selection is an agent skill from aws-samples/amazon-bedrock-agents-healthcare-lifesciences, published by the product's own GitHub organization. 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…

Its SKILL.md is about 2.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including reference files (for example `references/01-intuition-pumps.md`, `references/02-risk-assessment.md` and `references/03-optimization-function.md`).

It sits in Research & Science, covering Brainstorming and Hypothesis generation. The licence is Apache-2.0.

When your agent uses it

  • Users ask to pitch a new research idea
  • Work through a project problem
  • Evaluate project risks
  • Plan research strategy

Example prompts

  • “I have an idea for a project”
  • “m stuck on my research”
  • “help me evaluate this project”
  • “/scientific-problem-selection”

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  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"

What it can do on your machine

Read from SKILL.md and the folder at commit 9960565. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Scientific Problem Selection loads about 2.8k tokens when it runs, and up to ~26k if it reads all its reference files. Until then it costs about 153 tokens; SKILL.md has 1,215 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~153
When it runs · the whole SKILL.md, loaded when a task matches
~2.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~26k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from aws-samples/amazon-bedrock-agents-healthcare-lifesciences at commit 9960565, republished under its Apache-2.0 licence (© aws-samples). 1,215 words, ~2,792 tokens.

Download SKILL.mdSave it as .claude/skills/scientific-problem-selection/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.
name
scientific-problem-selection
description
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".

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
Show full SKILL.md (484 more words)Show less
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.

© aws-samples, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 10 other files (references) in agents_catalog/36-C4LS-example-agent/C4LS/src/skills/scientific-problem-selection of aws-samples/amazon-bedrock-agents-healthcare-lifesciences.

  • SKILL.md
  • LICENSE.txt
  • references/01-intuition-pumps.md
  • references/02-risk-assessment.md
  • references/03-optimization-function.md
  • references/04-parameter-strategy.md
  • references/05-decision-tree.md
  • references/06-adversity-planning.md
  • references/07-problem-inversion.md
  • references/08-integration-synthesis.md
  • references/09-meta-framework.md

Open the folder on GitHubat commit 9960565

Used in 4 other repositories

We found 4 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 4 other GitHub owners. This page covers the copy in aws-samples/amazon-bedrock-agents-healthcare-lifesciences, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Scientific Problem Selection next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Scientific Problem Selection compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Scientific Problem Selection this skillaws-samples/amazon-bedrock-agents-healthcare-lifesciences2744 repos~2.8kAutomated safety check: PassApache-2.0
Scientific Brainstormingspacering-net/codeg3.8k14 repos~2kAutomated safety check: PassMIT
Scientific BrainstormingOleafly/Oleafly2052 repos~3.5kAutomated safety check: PassMIT
Research IdeationGalaxy-Dawn/claude-scholar5.7k3 repos~2.4kAutomated safety check: PassMIT
News to Research Idea BriefingOpenLAIR/dr-claw1.2k—~1.3kAutomated safety check: NotesCustom licence
Academic GrillExekiel179/psyclaw103—~2kAutomated safety check: PassMIT

Similar skills

  • Scientific Brainstorming

    spacering-net/codeg

    Creative research ideation and exploration. An agent skill from spacering-net/codeg.

    3.8k GitHub starsUsed in 14 repos~2k tokens
    Research & ScienceAuto-check passed
  • Facilitates evidence-aware scientific ideation with independent generation, structured discussion, explicit assumptions, transparent evaluation, adversarial review, and decision logs.

    205 GitHub starsUsed in 2 repos~3.5k tokens
    Research & ScienceAuto-check passed
  • Research Ideation

    Galaxy-Dawn/claude-scholar

    This skill should be used when the user asks to "brainstorm research ideas", "use 5W1H framework", "identify research gaps", "conduct gap analysis", "start research project", "conduct literature…

    5.7k GitHub starsUsed in 3 repos~2.4k tokens
    Research & ScienceAuto-check passed
  • Clusters the latest news-feed results by topic and writes a briefing of research idea seeds with citations, plus a structured seeds file, without crawling new sources.

    1.2k GitHub stars~1.3k tokensUpdated 20 days ago
    Research & ScienceAuto-check: notes
  • Academic Grill

    Exekiel179/psyclaw

    Stress-test an academic research question, proposal, study design, analysis plan, manuscript claim, review protocol, or AI research project through a one-question-at-a-time interview until its…

    103 GitHub stars~2k tokensUpdated 9 days ago
    Research & ScienceAuto-check passed
  • Research Idea Brainstorming

    Orchestra-Research/AI-Research-SKILLs

    Offers ten ideation frameworks for exploring new research directions, stress-testing half-formed ideas and finding gaps when you are stuck or changing fields.

    13k GitHub starsUsed in 3 repos~4.8k tokens
    Research & ScienceAuto-check passed

More from aws-samples/amazon-bedrock-agents-healthcare-lifesciences

All 12 skills in this repo
  • Instrument Data To Allotrope

    aws-samples/amazon-bedrock-agents-healthcare-lifesciences

    Official

    Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV.

    274 GitHub starsUsed in 2 repos~2.7k tokens
    Auto-check passed
  • Biomarker Database Analysis

    aws-samples/amazon-bedrock-agents-healthcare-lifesciences

    Official

    A skill your agent uses when a researcher needs to query biomedical databases for biomarker discovery, build target profiles from UniProt/Open Targets/STRING, rank biomarker candidates by evidence…

    274 GitHub stars~1.1k tokensUpdated 6 days ago
    Auto-check passed
  • Genomics Variant Interpretation

    aws-samples/amazon-bedrock-agents-healthcare-lifesciences

    Official

    A skill your agent uses when interpreting genomic variants from VCF files, performing clinical variant classification using ClinVar/VEP annotations, analyzing allele frequencies against population…

    274 GitHub stars~1.5k tokensUpdated 6 days ago
    Auto-check passed
  • Hcls Build Agent

    aws-samples/amazon-bedrock-agents-healthcare-lifesciences

    Official

    A skill your agent uses when a developer wants to build a new healthcare or life sciences agent, structure tools and system prompts for an HCLS workflow, or create a Strands agent with…

    274 GitHub stars~885 tokensUpdated 6 days ago
    Auto-check passed
  • Hcls Deploy Agent

    aws-samples/amazon-bedrock-agents-healthcare-lifesciences

    Official

    A skill your agent uses when a developer wants to deploy an HCLS agent to Amazon Bedrock AgentCore, configure Gateway tools as MCP endpoints, set up authentication with Cognito, configure memory, or…

    274 GitHub stars~813 tokensUpdated 6 days ago
    Auto-check passed
  • Biomarker Multi Agent Discovery

    aws-samples/amazon-bedrock-agents-healthcare-lifesciences

    Official

    A skill your agent uses when orchestrating a multi-agent biomarker discovery workflow that requires coordinating database queries, pathway analysis, literature review, statistical modeling, and…

    274 GitHub stars~1.4k tokensUpdated 6 days ago
    Auto-check passed

Questions about Scientific Problem Selection

What does Scientific Problem Selection do?

This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions. Scientific Problem Selection is an agent skill from aws-samples/amazon-bedrock-agents-healthcare-lifesciences, published by the product's own GitHub organization. This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions.

When should I use Scientific Problem Selection?

Scientific Problem Selection fits situations like: users ask to pitch a new research idea; work through a project problem; evaluate project risks; plan research strategy.

How do I install Scientific Problem Selection in Claude Code?

Run `npx skills add aws-samples/amazon-bedrock-agents-healthcare-lifesciences --skill scientific-problem-selection -a claude-code`. Or copy the skill folder (agents_catalog/36-C4LS-example-agent/C4LS/src/skills/scientific-problem-selection in aws-samples/amazon-bedrock-agents-healthcare-lifesciences) into .claude/skills/scientific-problem-selection in your project. Claude Code loads it when a task matches its description.

How do I install Scientific Problem Selection in Codex?

Run `npx skills add aws-samples/amazon-bedrock-agents-healthcare-lifesciences --skill scientific-problem-selection -a codex`. Or copy the skill folder (agents_catalog/36-C4LS-example-agent/C4LS/src/skills/scientific-problem-selection in aws-samples/amazon-bedrock-agents-healthcare-lifesciences) into .agents/skills/scientific-problem-selection in your project. Codex loads it when a task matches its description.

Can I use Scientific Problem Selection in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aws-samples/amazon-bedrock-agents-healthcare-lifesciences --skill scientific-problem-selection -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/scientific-problem-selection, .gemini/skills/scientific-problem-selection, .github/skills/scientific-problem-selection and .opencode/skills/scientific-problem-selection in your project.

What does Scientific Problem Selection need to run?

SKILL.md names no scripts, command-line tools or credentials: Scientific Problem Selection is instructions for the agent only.

Does Scientific Problem Selection access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Scientific Problem Selection safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Scientific Problem Selection use?

Scientific Problem Selection is published under the Apache-2.0 licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Scientific Problem Selection use?

About 2.8k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 23k tokens, read only when the agent opens those files.

What are the alternatives to Scientific Problem Selection?

Skills that share tags, products or a category with Scientific Problem Selection: Scientific Brainstorming (spacering-net/codeg, 3.8k stars), Scientific Brainstorming (Oleafly/Oleafly, 205 stars), Research Ideation (Galaxy-Dawn/claude-scholar, 5.7k stars) and News to Research Idea Briefing (OpenLAIR/dr-claw, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Scientific Problem Selection?

aws-samples (a GitHub organization, an official publisher) maintains it in aws-samples/amazon-bedrock-agents-healthcare-lifesciences, which has 274 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on October 1, 2026.

Source: aws-samples/amazon-bedrock-agents-healthcare-lifesciences on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.