CtrlK
BlogDocsLog inGet started
Tessl Logo

tooluniverse-pharmacogenomics

Pharmacogenomics (PGx) research — drug-gene interactions (CPIC, PharmGKB), CPIC dosing guidelines, variant-drug-response associations, ethnic-allele-frequency considerations, and metabolizer-status scoring. Use for PGx-informed dosing recommendations, CYP/HLA pharmacogenomic allele interpretation, and clinically-actionable PGx report generation.

70

Quality

85%

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

SKILL.md
Quality
Evals
Security

Quality

Content

78%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.

Highly actionable content with concrete tool signatures, gotchas, fallbacks, and example workflows, organized around a clear six-phase pipeline. It is somewhat over-long with API detail inlined and would benefit from splitting reference material into bundle files and adding an explicit validation step before emitting clinical recommendations.

Suggestions

Split the per-tool API reference (signatures, return shapes, parameter notes) into a references/ file and keep SKILL.md as an overview pointing to it, improving progressive disclosure.

Add an explicit validation/safety checkpoint (e.g., confirm evidence level and FDA label before outputting a dosing recommendation) to strengthen workflow_clarity for high-stakes clinical guidance.

Trim redundant tool descriptions that appear in multiple phases (e.g., CPIC_get_drug_info, PharmGKB_get_dosing_guidelines) to a single canonical entry referenced by phase.

DimensionReasoningScore

Conciseness

Largely lean and action-oriented with no padding about what PGx is, but ~270 lines inline detailed tool signatures and parameter notes that could be trimmed or offloaded, leaving minor over-explanation.

4 / 5

Actionability

Provides concrete tool signatures, required parameter names, return shapes, gotchas, fallback strategies, and copy-ready example workflows covering drug-first, gene-first, and variant-first cases.

5 / 5

Workflow Clarity

A clear six-phase sequence diagram plus explicit fallback strategies and reasoning chains; however it lacks an explicit validate→fix→retry feedback loop for the high-stakes clinical output it produces.

4 / 5

Progressive Disclosure

All API reference material is inlined in SKILL.md with no references/ or other bundle files present; the bulk tool reference could be split out, and structure is section-based but references are not used.

3 / 5

Total

16

/

20

Passed

Description

92%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 precise, third-person description that names concrete capabilities and an explicit 'Use for' trigger clause, with strong distinctiveness from named PGx databases. It is slightly specialized rather than lay-friendly in its trigger terms.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'drug-gene interactions (CPIC, PharmGKB)', 'CPIC dosing guidelines', 'variant-drug-response associations', 'metabolizer-status scoring', 'PGx-informed dosing recommendations', 'clinically-actionable PGx report generation' — with comprehensive coverage of the domain.

5 / 5

Completeness

Explicitly answers 'what' (research actions enumerated) and 'when' via the explicit 'Use for PGx-informed dosing recommendations, CYP/HLA ... interpretation, and clinically-actionable PGx report generation' clause.

5 / 5

Trigger Term Quality

Strong natural keywords ('drug-gene interactions', 'dosing recommendations', 'CPIC', 'PharmGKB', 'CYP/HLA'), but terminology is specialized; a few lay synonyms a non-expert might say are missing, so it sits just below fully comprehensive.

4 / 5

Distinctiveness Conflict Risk

Carves a clear PGx niche with named databases (CPIC, PharmGKB) and specific triggers (CYP/HLA allele interpretation, metabolizer scoring), making conflict with unrelated skills minimal.

5 / 5

Total

19

/

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
mims-harvard/ToolUniverse
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.