Individual skill
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. Use for senescence-pathway analysis, age-related disease genetics, senolytic-target discovery, and centenarian-genetics queries. Distinguishes correlative vs causal evidence (knockout, intervention).
plugins/tooluniverse/skills/tooluniverse-aging-senescence/SKILL.md
Source commit 5598a9fc97e32964fe9b703818e7d0747142f179
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- No
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Not checked yet
- Last checked
- Nov 5, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 1,778
- Repository last updated
- Nov 8, 2026
- License
- Apache-2.0
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agents Repository topic
- reasoning-agent Repository topic
- reasoning-language-models Repository topic
- tool-use Repository topic
- ai-agents Repository topic
- ai-scientists Repository topic
- automated-science Repository topic
- co-pilot Repository topic
- co-scientist Repository topic
- ai-for-science Repository topic
- research Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
Read excerpt
# Aging & Cellular Senescence Research
## Aging Research Reasoning
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.
