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tooluniverse-biomedical-fact-lookup

Answer biomedical FACTUAL / recall / multiple-choice questions by querying ToolUniverse database tools instead of answering from memory. Triggers on any 'which gene/drug/variant/disease/pathway/miRNA/TF...' lookup, any question phrased 'according to <database>' (DisGeNet, OMIM, MSigDB, miRDB, GTRD, MGI, Ensembl, ClinVar, ChEMBL, OpenTargets, Reactome, GtoPdb, UniProt...), and multiple-choice biology/medicine knowledge questions where one option must be verified against an authoritative source. NOT for analyzing user-supplied data files (CSV/VCF/h5ad → use the data-analysis router) and NOT for open-ended literature synthesis. Use whenever a single correct answer exists in a public biomedical database and could be looked up rather than guessed.

75

Quality

92%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

The canonical home for this skill is tooluniverse-biomedical-fact-lookup in mims-harvard/ToolUniverse

SKILL.md
Quality
Evals
Security

Quality

Content

85%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 high-quality, highly actionable body with executable code, a clear sequenced MCQ workflow, and explicit verification discipline. The main weakness is structure: with no references/scripts/assets, everything lives inline in one long file rather than being split into navigable one-level-deep references.

Suggestions

Move the computational-procedure code blocks (ORF counting, restriction digest, gamete ratios) into a scripts/ file and reference it from SKILL.md, keeping only a short usage summary inline to reduce body length and support progressive disclosure.

Extract the full routing table into a references/ document (e.g. ROUTING.md) and keep a condensed 'most common patterns' table inline, linking out for the long tail of databases.

Tighten rhetorical asides like 'Mental arithmetic on these is the #1 avoidable error' and 'A surprising number of misses are cases where...' to imperative guidance, trimming a few tokens without losing the rule.

DimensionReasoningScore

Conciseness

The body is largely efficient and assumes Claude's biology/bioinformatics knowledge, but a few passages could be tightened (e.g. 'Mental arithmetic on these is the #1 avoidable error: the model reliably mis-counts or mis-multiplies') — minor over-explanation rather than padding.

4 / 5

Actionability

Fully executable guidance: copy-paste-ready Python (ORF counting, restriction digest, gamete ratios), exact MSigDB set-name conventions, named tools with concrete params (e.g. 'MSigDB_check_gene_in_set', 'gene'), and a UMLS-CUI resolution recipe covering common cases.

5 / 5

Workflow Clarity

The MCQ procedure (Parse → Resolve → Query → Check → Answer) and the gene–disease 'in X but not Y' recipe are clearly sequenced with explicit 'Insufficient information' criteria, elimination logic, and final-answer discipline checkpoints ('match the computed value back to the options explicitly') providing strong verification feedback.

5 / 5

Progressive Disclosure

The body is well-sectioned with clear headers, but no bundle files exist and all content — including the dense routing table and multiple code procedures that could plausibly live in separate reference files — is inlined in a single ~158-line SKILL.md, so progressive disclosure across files is not exercised.

3 / 5

Total

17

/

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.

The description is excellent: it states concrete capabilities, comprehensive natural trigger terms, explicit 'what' and 'when' guidance, and clear exclusions that distinguish it from sibling skills. Voice is third person throughout, with no padding.

DimensionReasoningScore

Specificity

Names concrete actions ('querying ToolUniverse database tools', 'verified against an authoritative source') plus a comprehensive list of specific databases (DisGeNet, OMIM, MSigDB, miRDB, GTRD, MGI, Ensembl, ClinVar, ChEMBL, OpenTargets, Reactome, GtoPdb, UniProt), giving comprehensive coverage of capabilities.

5 / 5

Completeness

Explicitly answers both 'what' ('Answer biomedical FACTUAL / recall / multiple-choice questions by querying ToolUniverse database tools') and 'when' ('Triggers on any...', 'Use whenever a single correct answer exists in a public biomedical database') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Includes natural user phrasings ('which gene/drug/variant/disease/pathway/miRNA/TF...', 'according to <database>', 'multiple-choice biology/medicine knowledge questions') with extensive synonyms and named databases, covering the terms users would actually say.

5 / 5

Distinctiveness Conflict Risk

Occupies a clear niche (biomedical database lookups) with explicit negative boundaries ('NOT for analyzing user-supplied data files (CSV/VCF/h5ad → use the data-analysis router) and NOT for open-ended literature synthesis'), minimizing conflict with adjacent skills.

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
mims-harvard/ToolUniverse
Reviewed

Table of Contents

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