Loading skill
Install any skill in seconds. Free to start, no credit card required.
Get Started Free →mims-harvard/tooluniverse-aging-senescence
Aging biology, cellular senescence, and longevity research. Covers senescence markers (p16/CDKN2A, SASP, SA-beta-gal), aging hallmarks, senolytic drug discovery (dasatinib+quercetin, fisetin, navitoclax), epigenetic clocks, telomere biology, and longevity GWAS.
6 of 22 test cases passed with the skill.
Tokens, turns and time describe resource use, not a dollar price.
Lift withheld: 6 comparable cases; this report requires 8.
One of 4 test cases the skill fixed. Same model, same prompt, with and without the skill.
When searching for all curated aging genes without knowing a specific gene symbol, what tool and query function allows browsing the full catalog of ~2,400 aging genes?
| Checked | Without skill | With skill |
|---|---|---|
| The response recommends using OpenGenes_search_genes to browse the full catalog of aging genes. | Missed | Passed |
| The response explains that OpenGenes aggregates experimental lifespan studies and curator confidence levels. | Missed | Passed |
The instructions your model reads when the skill loads.
Before querying any tool, ask the central question: is this a cause or consequence of aging?
Senescence markers (SA-β-gal, p16/CDKN2A, SASP factors like IL-6 and IL-8) indicate that senescent cells are present. But their presence does not prove that senescence is driving the phenotype. Correlation is easy to establish. Causation requires an intervention. If senolytic drugs (dasatinib+quercetin, fisetin, navitoclax) clear senescent cells and the age-related phenotype improves, that is causal evidence. If clearing senescent cells has no effect, something else is driving the pathology.
Apply this reasoning when interpreting any gene or pathway query: classify it first by hallmark, then ask whether the evidence for its role is correlative (expression data, GWAS association) or causal (functional assay, genetic knockout, senolytic intervention).
Evidence grade the findings: T1 is human genetic evidence (GWAS, centenarian studies). T2 is model organism lifespan data. T3 is cell culture senescence data. T4 is computational prediction. Do not conflate T3 cell culture data with T1 human evidence — they are very different levels of confidence.
A final principle: cellular senescence is one hallmark of aging, not aging itself. Distinguish senescence from organismal aging, from age-related disease, and from progeria (accelerated aging syndromes). These require different tools and different interpretations.
When uncertain about any scientific fact, SEARCH databases first (PubMed, UniProt, ChEMBL, ClinVar, etc.) rather than reasoning from memory. A database-verified answer is always more reliable than a guess.
Not this skill: For rare disease genetics, use tooluniverse-rare-disease-diagnosis. For general disease research, use tooluniverse-disease-research.
Phase 0: Query Parsing — aging gene, senescence marker, age-related disease, or drug query
|
Phase 1: Hallmarks Classification — map to the 12 hallmarks of aging framework
|
Phase 2: Genetic Evidence — GWAS, longevity loci, model organism data
|
test cases passed with the skill
Published skills whose descriptions closely match this one, with their headline benchmark lift.
More skills from the same source and nearby in the registry, with their headline benchmark lift.