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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%

Does it follow best practices?

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SecuritybySnyk

Passed

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Fix and improve this skill with Tessl

tessl review fix ./plugins/tooluniverse/skills/tooluniverse-gpcr-structural-pharmacology/SKILL.md

The canonical home for this skill is tooluniverse-gpcr-structural-pharmacology in mims-harvard/ToolUniverse

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 thorough, highly actionable reference skill with concrete code for every tool and useful domain interpretation. Its main weaknesses are redundancy (workflows described three times) and a monolithic structure with no progressive disclosure via reference files.

Suggestions

Remove the redundant third retelling of the flows: either drop Common Research Patterns or fold its concrete input/output examples into the Phase sections so each workflow appears once.

Trim basic pharmacology definitions Claude already knows (e.g. "Agonist: activates receptor") and keep only the domain-specific interpretation guidance (BSA thresholds, Ballesteros-Weinstein numbering, CDR-H3 note).

Move the Tool Parameter Reference table and/or Common Research Patterns into a separate references/ file (e.g. TOOL_REFERENCE.md) referenced one level deep from SKILL.md to improve progressive disclosure.

DimensionReasoningScore

Conciseness

The body is dense and mostly actionable, but explains concepts Claude already knows (e.g. "Agonist: activates receptor", "Antagonist/Inverse agonist: blocks or suppresses") and describes the same workflows three times — Workflow Overview, Phase 1-5 detail, and Common Research Patterns — so it is mostly efficient with clear tightening opportunities rather than lean.

3 / 5

Actionability

Every tool has concrete, copy-paste-ready Python examples with real entry names, parameter values, and return-value comments, plus specific interpretive thresholds (BSA > 1500 Ų) and fallback chains — fully executable guidance covering the common cases.

5 / 5

Workflow Clarity

Five phases are clearly sequenced with a workflow diagram and a completeness checklist acting as a verification gate, plus fallback chains for error recovery; however the individual phase steps lack inline validation checkpoints, keeping it just below the explicit-feedback-loop anchor.

4 / 5

Progressive Disclosure

The skill is well-sectioned but is a ~300-line monolithic SKILL.md with no bundle files (references/, scripts/, assets/ absent); the detailed tool-parameter reference and research patterns that could live in one-level-deep reference files are all inlined, matching the "some structure, content that should be separate is inline" anchor.

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 well-crafted description that concretely states what the skill does and gives explicit, natural trigger phrases for when to use it. Minor gains are possible by adding a few more synonyms (antibody engineering, structural pharmacology) and tightening the antibody-overlap boundary.

Suggestions

Add a couple of natural synonyms users might say, such as "antibody engineering" or "structural pharmacology", to broaden trigger-term coverage from 4 to 5.

Clarify the antibody boundary (e.g. "antibody-target interface retrieval" rather than broad antibody work) to reduce overlap with a dedicated antibody-engineering skill.

DimensionReasoningScore

Specificity

Lists multiple concrete capabilities — "agonist/antagonist/inverse-agonist/biased-agonist classification, GPCRdb structural data, receptor-ligand binding analysis, antibody-target interface (SAbDab)" — matching the comprehensive-coverage anchor rather than the 1-2-action anchor at 3.

5 / 5

Completeness

Explicitly answers both what (the four capabilities before the period) and when (the "Use for..." clause with concrete trigger phrases), matching the anchor that requires both with explicit triggers.

5 / 5

Trigger Term Quality

"Use for GPCR drug discovery, biased-agonism analysis, receptor subtype selectivity questions, and orthosteric vs allosteric pocket characterization" are natural user phrasings, but a few common variants (e.g. "antibody engineering", "structural pharmacology", "drug-target") are absent, fitting good-but-not-comprehensive coverage.

4 / 5

Distinctiveness Conflict Risk

The GPCR structural-pharmacology niche is clearly distinct, but the "antibody-target interface (SAbDab)" coverage creates minor overlap risk with a general antibody-engineering skill, so it is mostly distinct rather than minimal-conflict.

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

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