CtrlK
BlogDocsLog inGet started
Tessl Logo
SkillPublic

tooluniverse-pathway-disease-genetics

Connect GWAS variants to biological pathways and druggable targets. Maps GWAS hits to causal genes (via fine-mapping/eQTL), then to pathways (Reactome, KEGG, WikiPathways), then to existing drugs hitting those pathways. Use for pathway-level disease mechanisms, druggable-pathway prioritization from GWAS, SNP-to-pathway-to-target tracing, and tissue-specific eQTL evidence for drug target hypotheses.

Quality

90%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide
SecuritybySnyk

Passed

No findings from the security scan

The canonical home for this skill is tooluniverse-pathway-disease-genetics in mims-harvard/ToolUniverse

SKILL.md
Quality
Evals
Security

Quality

Content

75%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 strong, well-structured instruction skill: the three-phase workflow is clearly sequenced, the tool calls include exact parameter names and a dedicated gotcha list covering the exact failure modes (wrong param names, string-vs-array formats, broken tools) that Claude could not know. The main gaps are the absence of a worked end-to-end example and the absence of any validation step after retrieval phases.

Suggestions

Add one worked end-to-end mini-example (real disease, real gene) with concrete tool invocations and a small Python analysis snippet, so the 'Compute, don't describe' mandate has an executable template rather than placeholder calls like <disease>.

Add an explicit validation checkpoint between phases — e.g., after Step 1, verify the prioritized gene list is non-empty and each gene has an EFO/Ensembl ID before running enrichment; after Step 3, verify drug-gene interactions returned results.

Define the ranking procedure concretely: 'Rank pathways by enrichment FDR x number of GWAS genes x biological plausibility' and 'Genetic Evidence x Druggability x Pathway Centrality' currently have no scoring scale or tie-breaking rule, so convert them to an explicit numeric scoring formula.

DimensionReasoningScore

Conciseness

The body is dense with non-obvious operational knowledge (parameter gotchas, broken-tool warnings, response-format quirks) and doesn't re-explain generic concepts. However, sections like the TCF7L2/Wnt worked example and the "Undruggable... does not mean permanently undruggable" paragraph spend tokens on biology exposition Claude could largely supply itself. This matches 'efficient; minor instances of over-explanation that could be trimmed' — not a 5, but well above the 'mostly efficient with some unnecessary explanation' midpoint.

4 / 5

Actionability

Guidance is highly concrete: exact tool names with parameter signatures ("ReactomeAnalysis_pathway_enrichment(identifiers=\"P04637 P38398 ...\")\" — space-separated UniProt STRING, not an array"), exact param gotchas, and some real values (ENSG00000148737.11, species=9606, gtex_v8). What's missing for a 5 is a copy-paste-ready worked example — the calls use placeholders like <disease> and <gene>, and there is no complete end-to-end invocation or Python snippet showing the 'compute, don't describe' mandate in practice. This fits 'mostly executable guidance; concrete code or commands with minor gaps'.

4 / 5

Workflow Clarity

The Three-Step Workflow (GWAS to causal gene → enrichment → drug target) is clearly sequenced with numbered steps, evidence tiers (High/Medium/Low), and cross-validation checkpoints (cross-check with OpenTargets, converge across Reactome/KEGG/STRING). This matches 'clear sequence with most checkpoints present; minor validation gaps' — the rankings are conceptual formulas ("enrichment FDR x number of GWAS genes x biological plausibility") rather than defined procedures, and there is no explicit verify-output step. Not a 3 because decision checkpoints (evidence tiers, convergence criteria, classification of candidates) are present and explicit.

4 / 5

Progressive Disclosure

No bundle files exist (no references/, scripts/, or assets/), so the skill is a single self-contained document — no nested or dead references. Sections are well-organized with clear headers (When to Use, Tool Selection Guide by phase, Three-Step Workflow, Gotchas, Limitations, Related Skills). This fits 'good structure; most content is appropriately placed; minor organization gaps' — not a 5 because at ~157 lines the Tool Selection Guide and Key Parameter Gotchas sections are candidates for a separate reference file if the skill grows, keeping the core even leaner.

4 / 5

Total

16

/

20

Passed

Description

100%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.

An excellent description: it states a concrete three-stage pipeline (variants → causal genes → pathways → drugs), uses third person, and closes with an explicit 'Use for' clause enumerating four natural trigger scenarios. It is specific, complete, and clearly distinguishable from adjacent genetics/drug skills.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions in a clear pipeline: "Maps GWAS hits to causal genes (via fine-mapping/eQTL), then to pathways (Reactome, KEGG, WikiPathways), then to existing drugs hitting those pathways." This matches the anchor 'lists multiple specific concrete actions; comprehensive coverage' — it names each pipeline stage, the data sources, and the analytical method. Not a 4 because there are no meaningful coverage gaps; every phase of the workflow (gene mapping, pathway mapping, drug lookup) is explicitly named.

5 / 5

Completeness

It explicitly answers 'what' ("Connect GWAS variants to biological pathways and druggable targets. Maps GWAS hits to causal genes... then to pathways... then to existing drugs") and 'when' ("Use for pathway-level disease mechanisms, druggable-pathway prioritization from GWAS, SNP-to-pathway-to-target tracing, and tissue-specific eQTL evidence for drug target hypotheses"). This matches the anchor 'clearly and explicitly answers both what AND when with concrete trigger phrases'. Not a 4 because the 'when' clause is not merely present but enumerates four concrete trigger scenarios.

5 / 5

Trigger Term Quality

Trigger terms are comprehensive and natural for this domain: "pathway-level disease mechanisms", "druggable-pathway prioritization from GWAS", "SNP-to-pathway-to-target tracing", "tissue-specific eQTL evidence" — plus synonyms like GWAS variants, SNP/variant, drug targets, causal genes. A user asking any of these questions would naturally use these phrases. Not a 4 because both the technical terms and their common variations (GWAS/SNP/variant; drug target/druggable) are covered.

5 / 5

Distinctiveness Conflict Risk

The description carves out a clear niche — pathway-level GWAS-to-drug-target tracing — with distinct triggers (pathway mechanisms, druggable-pathway prioritization, SNP-to-pathway tracing) unlikely to fire for generic genetics or drug-lookup skills. It uses third person throughout ('Connect', 'Maps'). Not a 4 because even against closely related sibling skills (GWAS-to-gene, enrichment-only), the pathway-to-drug-target framing is uniquely specific to this skill.

5 / 5

Total

20

/

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

plugins/tooluniverse/skills/tooluniverse-pathway-disease-genetics/SKILL.md

Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.