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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).

Quality

88%

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

82%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-structured, highly actionable domain skill: every phase is backed by concrete executable tool calls and the cause/consequence and evidence-grading frameworks give consistent interpretation guidance. The main improvement levers are trimming a few platitudinous sentences and moving reference-heavy material (gene lists, drug clinical status) into a separate reference file.

Suggestions

Trim universally-known platitudes (e.g., 'A database-verified answer is always more reliable than a guess') and tighten the 'Aging Research Reasoning' prose to raise conciseness toward anchor 5.

Move reference-style tables — the hallmark gene lists in Phase 1 and the per-drug clinical status in Phase 4 — into a references/ file (e.g., references/hallmarks.md, references/senolytics.md) and link them from the body, keeping SKILL.md a leaner overview.

Add explicit validation checkpoints to the workflow (e.g., confirm a tool returned non-empty results before building the report, or re-query with a synonym when a GWAS term misses) to raise workflow_clarity from 4 to 5.

DimensionReasoningScore

Conciseness

The body is dense with non-obvious domain knowledge Claude cannot reliably recall (KEGG pathway IDs, the FOXO3 targeted-genotyping GWAS-Catalog caveat, per-drug clinical trial status), with only minor trimmable platitudes such as 'A database-verified answer is always more reliable than a guess' — efficient with minor over-explanation, matching anchor 4 rather than the fully lean anchor 5.

4 / 5

Actionability

Fully executable, copy-paste-ready tool calls throughout — 'OpenGenes_get_gene(symbol="FOXO3")', 'KEGG_get_pathway_genes(pathway_id="hsa04218")', 'STRING_get_network(identifiers="\r".join(sasp_genes), species=9606)', 'search_clinical_trials(condition="senescence", query_term="senolytic")' — annotated with comments and covering the common query cases, matching the top anchor.

5 / 5

Workflow Clarity

A clear Phase 0–6 sequence is laid out in the workflow diagram and mirrored by matching sections, with the cause-vs-consequence test and T1–T4 evidence grading serving as interpretation checkpoints and an 8-part report structure; however, no explicit verify-output/retry steps exist, matching anchor 4 ('most checkpoints present; minor validation gaps') rather than 5.

4 / 5

Progressive Disclosure

No bundle files exist and the single file is well-organized with clear section headers and a dedicated limitations section; but at ~185 lines, reference-style material (hallmark gene lists, per-drug clinical status) is inlined where a references/ file would keep the overview leaner — good structure with minor organization gaps, anchor 4 rather than 5.

4 / 5

Total

17

/

20

Passed

Description

88%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description that explicitly states what the skill covers and when to use it, with unusually concrete domain specifics (named genes, drugs, and evidence types). Its only weaknesses are compound-artifact trigger phrases that users would less naturally type and minor overlap risk with general disease-genetics skills.

DimensionReasoningScore

Specificity

The description enumerates multiple concrete capabilities with named specifics — 'senescence markers (p16/CDKN2A, SASP, SA-beta-gal), aging hallmarks, senolytic drug discovery (dasatinib+quercetin, fisetin, navitoclax), epigenetic clocks, telomere biology, and longevity GWAS' — giving comprehensive coverage of the domain with no gaps, matching the top anchor.

5 / 5

Completeness

Both questions are explicitly answered: 'Covers senescence markers... epigenetic clocks, telomere biology, and longevity GWAS' (what) and 'Use for senescence-pathway analysis, age-related disease genetics, senolytic-target discovery, and centenarian-genetics queries' (when), with concrete trigger phrases — matches the top anchor rather than the weaker 'when' of anchor 4.

5 / 5

Trigger Term Quality

Good natural-keyword coverage ('cellular senescence', 'longevity', 'senolytic', 'centenarian', 'GWAS', 'telomere'), but the 'when' triggers are compound constructions ('senescence-pathway analysis', 'senolytic-target discovery', 'centenarian-genetics queries') rather than phrases users naturally type, and common variants like 'anti-aging' or 'lifespan' are missing — between anchors 4 and 5, noticeably above the midpoint.

4 / 5

Distinctiveness Conflict Risk

The aging/senescence niche is clear with mostly distinct triggers, but 'age-related disease genetics' creates minor overlap risk with general disease-research skills (which the body itself acknowledges as siblings), placing it at anchor 4 rather than the minimal-conflict anchor 5.

4 / 5

Total

18

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

mims-harvard/ToolUniverse

Path

plugin/skills/tooluniverse-aging-senescence/SKILL.md

Reviewed

Table of Contents

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