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tooluniverse-gpcr-structural-pharmacology

GPCR receptor pharmacology — agonist/antagonist/inverse-agonist/biased-agonist classification, GPCRdb structural data, receptor-ligand binding analysis, antibody-target interface (SAbDab). Use for GPCR drug discovery, biased-agonism analysis, receptor subtype selectivity questions, and orthosteric vs allosteric pocket characterization.

67

Quality

80%

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SecuritybySnyk

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tessl review fix ./plugin/skills/tooluniverse-gpcr-structural-pharmacology/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

71%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 highly actionable, well-sequenced research skill with concrete code and clear workflow patterns. It is held back by some redundant domain background and a monolithic structure with no progressive disclosure into reference files.

Suggestions

Trim the introductory pharmacology background (biased-agonism clinical rationale, opioid example, CDR-H3 dominance) to assume Claude's domain knowledge and save tokens.

Move the Tool Parameter Reference table and Common Research Patterns into separate reference files (e.g. references/tool-params.md, references/patterns.md) and link to them from SKILL.md to improve progressive disclosure.

Add an explicit verification step at the end of each phase (e.g. 'confirm entry name resolves before proceeding to ligand queries') to strengthen the workflow's feedback loops.

DimensionReasoningScore

Conciseness

Code and tables are efficient, but the opening paragraph explains GPCR concepts Claude already knows (biased-agonism clinical implications, the opioid respiratory-depression example, 'CDR-H3 is most variable') that could be trimmed.

3 / 5

Actionability

Provides copy-paste-ready python calls with real tool names and parameters, a full parameter-reference table, concrete entry-name formats, and BSA thresholds covering the common cases.

5 / 5

Workflow Clarity

Five sequenced phases, a workflow diagram, four input/flow/output patterns, fallback chains, and a completeness checklist give a clear sequence with a verification checkpoint; no explicit validate-retry loops, but the skill is non-destructive lookups so the validation cap does not apply.

4 / 5

Progressive Disclosure

Section headers are well-organized, but no bundle files exist and all content — the full tool-parameter reference table and common-research-patterns — is inlined rather than split into one-level-deep reference files.

3 / 5

Total

15

/

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, third-person description that concretely states capabilities and gives explicit 'Use for' triggers tuned to a GPCR pharmacology audience. Minor overlap risk and a few missing trigger synonyms keep it just short of perfect.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'agonist/antagonist/inverse-agonist/biased-agonist classification', 'GPCRdb structural data', 'receptor-ligand binding analysis', 'antibody-target interface (SAbDab)' — with comprehensive coverage and no vague filler.

5 / 5

Completeness

Explicitly answers both 'what' (classification, structural data, binding, interface analysis) and 'when' via a concrete 'Use for...' clause with multiple trigger scenarios.

5 / 5

Trigger Term Quality

Includes natural domain phrases a pharmacologist would say ('GPCR drug discovery', 'biased-agonism analysis', 'receptor subtype selectivity questions', 'orthosteric vs allosteric pocket') but is missing a few common synonyms and ID-style triggers.

4 / 5

Distinctiveness Conflict Risk

A clear GPCR niche with distinct triggers, but bundling 'antibody-target interface (SAbDab)' creates minor overlap with a dedicated antibody-engineering skill.

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
mims-harvard/ToolUniverse
Reviewed

Table of Contents

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