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.
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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.
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
Update check
Throttle to one check per 24 hours per installation; never mutate the skill directory without explicit user consent.
If <this-skill-dir>/.last_update exists and is less than 24 hours old, skip this step entirely.
Compare with this skill's metadata.version from the frontmatter. If the upstream tag is strictly newer (semver), tell the user one line and ask:
"A newer version of this skill is available: vX.Y.Z → vA.B.C. Want me to git pull?"
If they say yes, run git -C <this-skill-dir> pull --ff-only. Refresh .last_update either way so the prompt doesn't repeat for 24 hours.
If upstream is the same or older, refresh .last_update silently and continue.
On any failure (offline, not a git checkout — e.g. ClawHub-installed copy, read-only path, no permission), swallow the error silently and continue with the user's task. Do not mention the failure.
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
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
Proactively check for the most common sources of biological confusion:
Marker vs. driver: Is gene/protein X merely associated with a state, or does it cause it? Enrichment ≠ function.
Correlation vs. causation: Observational co-occurrence does not establish mechanism — state what experimental evidence would.
Association vs. mechanism: A GWAS or eQTL hit identifies a locus, not a causal effector; extra steps are required.
Label vs. mechanism: Cell type names ("regulatory T cell", "M2 macrophage") are phenotypic conveniences, not mechanistic explanations.
State vs. lineage: Is this a stable cell identity or a transient cell state?
In vitro vs. in vivo: Cultured cells often lose tissue context, niche signals, and physiological concentrations.
Model organism vs. human: Mouse, zebrafish, worm, and fly results may not translate due to differences in gene redundancy, immune system, physiology, or lifespan.
Bulk vs. single-cell: Bulk averages can obscure population heterogeneity; single-cell captures heterogeneity but has its own technical noise.
Overexpression vs. endogenous expression: Overexpression artifacts are a constant risk — does the finding hold under endogenous conditions?
Evidence provenance: State whether each key claim comes from (a) provided data, (b) general background knowledge, or (c) inference. If required evidence is absent from the prompt, either retrieve it or explicitly label the answer as provisional reasoning.
Common biological evidence hierarchy (from stronger to weaker, context-dependent):
Genetic perturbation in a relevant in vivo model (KO, KI, conditional, CRISPRi/a)
Biochemical reconstitution or direct structural evidence
Pharmacological inhibition with selective tool compounds
In vivo pharmacology without genetic validation
Organoid or ex vivo primary cell experiments
Immortalized cell lines (note tissue-of-origin and transformation artifacts)
Correlative omics (transcriptomics, proteomics, GWAS) — association only
Position each claim in this hierarchy before concluding.
4. Evaluate the Experimental System
Every biological conclusion is conditional on its experimental system. Ask:
Model fidelity: Does this model recapitulate the biology of interest? (e.g., PDX vs. cell line, humanized mouse vs. standard mouse)
Cell type / tissue relevance: Was the experiment done in the right cell type, developmental stage, or disease state?
Technical confounders: batch effects in omics, doublets in scRNA-seq, off-target effects of CRISPR/shRNA/small molecules, cell line contamination, antibody specificity
Generalizability: Single lab, single cohort, single timepoint — how robust is the finding?
5. Consider Alternative Biological Explanations
Before giving a conclusion:
Is there another plausible mechanistic explanation?
Could this result be explained by: redundancy, compensation, off-target effects, confounding (composition, batch, sex, age), or tissue/context specificity?
Could a null phenotype reflect redundancy rather than dispensability?
Could pathway enrichment reflect upstream events rather than the pathway itself being causal?
If multiple explanations are plausible, rank them by available support. Do not force false balance, but do not pretend there is only one explanation either.
6. Calibrate Claim Strength
Match conclusion language to evidence strength:
Evidence level
Language to use
Multiple orthogonal experiments in vivo + in vitro + human data
"establishes", "demonstrates"
Consistent genetic + pharmacological evidence in one system
"supports strongly", "provides strong evidence"
Single genetic or pharmacological evidence, one system
"supports", "is consistent with"
Correlative omics or in vitro only
"suggests", "raises the possibility"
Computational or indirect
"is compatible with", "cannot exclude"
No relevant evidence
"is insufficient to conclude"
7. Define the Biological Boundary
Every biological conclusion has biological limits. State when relevant:
Species scope (mouse finding vs. human biology)
Cell type scope (cell line finding vs. primary cells vs. in vivo)
Disease stage or context (acute vs. chronic, tumor microenvironment vs. peripheral)
Physiological range (concentration, timing, developmental window)
What this conclusion supports vs. what it does not yet prove
8. Move Toward Resolution
Do not stop at abstract interpretation. Suggest:
The most likely current conclusion given available evidence
The key unresolved biological question
The lowest-cost next experiment that would discriminate between leading explanations (e.g., conditional knockout, orthogonal inhibitor, patient cohort validation)
Output Structure
Unless the user wants a short answer, organize in this order:
Biological level and problem framing
What can be said with confidence (with provenance: data / background / inference)
Assessment of the experimental system
Main possible biological interpretations, ranked by support
Most reasonable current conclusion
Boundary: species, cell type, context, or methodological limits
Next step: lowest-cost discriminating experiment or analysis
If the user wants a concise answer, compress this structure — do not abandon it.
