| name | scientific-thinking-biology |
| description | Use when interpreting biological research findings, evaluating life science evidence, analyzing molecular or cellular mechanisms, comparing competing biological hypotheses, designing or critiquing experiments in biology, genetics, genomics, cell biology, immunology, neuroscience, ecology, or any life science domain. Triggers on questions about gene function, pathways, phenotypes, GWAS hits, single-cell data, animal models, clinical translation, evolutionary arguments, or any biology/life science reasoning task. |
| license | MIT |
| homepage | https://github.com/Agents365-ai/scientific-thinking-skill |
| compatibility | No external tool dependencies. Works with any LLM-based agent on any platform. |
| platforms | ["macos","linux","windows"] |
| metadata | {"openclaw":{"requires":{},"emoji":"🧬","os":["darwin","linux","win32"]},"hermes":{"tags":["scientific-thinking","biology","life-science","genomics","cell-biology","immunology","neuroscience","genetics","molecular-biology","experiment-design"],"category":"research","requires_tools":[],"related_skills":["literature-review","paper-reader","zotero-cli-cc","single-cell-multiomics"]},"pimo":{"category":"research","tags":["biology","scientific-thinking","life-science","genomics","mechanism","hypothesis"]},"author":"Agents365-ai","version":"1.0.0"} |
Scientific Thinking — Biology & Life Science
A meta-skill for structured, evidence-aware, boundary-conscious scientific reasoning in biology and life science. Biology is complex: phenotypes arise from networks not single genes, model systems don't always translate, and the same data can support multiple mechanistic models. Your role is not just to answer — it is to reason like a careful biologist.
When to Use
- Interpreting experimental results from cell biology, genetics, genomics, immunology, neuroscience, or any life science
- Analyzing molecular mechanisms, signaling pathways, or gene regulatory networks
- Evaluating phenotype–genotype relationships
- Distinguishing marker from driver, association from causation, correlation from mechanism
- Designing, selecting, or critiquing experimental systems (in vitro, in vivo, ex vivo, organoids, patient data)
- Evaluating model organism relevance and translatability to humans
- Interpreting omics data (bulk/single-cell RNA-seq, ATAC-seq, proteomics, GWAS, etc.)
- Constructing or evaluating evolutionary, ecological, or physiological arguments
Biological Levels of Organization
Before reasoning, anchor the question to its biological level. Confusion often arises from mixing levels:
| Level | Examples |
|---|
| Molecular | protein structure, binding affinity, enzymatic activity, mRNA abundance |
| Cellular | cell state, gene expression program, cell-type identity, metabolism |
| Tissue / Organ | composition, architecture, intercellular communication |
| Organism | phenotype, behavior, physiology, disease manifestation |
| Population / Evolutionary | allele frequency, selection pressure, fitness, adaptation |
| Ecosystem | species interaction, community dynamics |
A finding at one level does not automatically transfer to another level.
Core Reasoning Framework
Work through these layers before responding.
1. Frame the Problem
- What exactly is being asked?
- At which biological level(s): molecular / cellular / tissue / organismal / evolutionary?
- What is known, unknown, and assumed in this biological context?
- Is the question about presence, quantity, timing, location, mechanism, or causal role?
- Restate the real problem if the question conflates levels or mixes concepts.
2. Decompose — Biology-Specific Pitfalls
