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tooluniverse-structural-proteomics

Structural biology plus proteomics integration for drug target validation. Combines PDB experimental structures, AlphaFold predictions, GPCRdb, SAbDab antibody structures, ProteinsPlus binding-site prediction, and BindingDB ligand-affinity data. Use for druggability assessment, binding-site characterization, ligand-pocket analysis, structural-confidence scoring (resolution, pLDDT), and antibody-target interface analysis.

69

Quality

84%

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-structural-proteomics in mims-harvard/ToolUniverse

SKILL.md
Quality
Evals
Security

Quality

Content

76%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 concise, information-dense skill body with strong actionable tool guidance and well-structured sections. Its main weaknesses are the absence of explicit validation/feedback loops in the batch workflows and a lack of any runnable code example.

Suggestions

Add explicit validation checkpoints to each workflow (e.g., after retrieving structures, verify resolution/coverage thresholds are met before proceeding; add a validate-fix-retry loop) to lift workflow_clarity above the batch-operation cap of 3.

Include at least one small executable Python example showing the intended ToolUniverse-retrieve-then-analyze pattern (e.g., fetching PDB entries with pandas/scipy) to make the 'COMPUTE, DON'T DESCRIBE' directive concrete.

Consider extracting the large Tool Inventory into a references/ file referenced from the body, to improve progressive_disclosure and keep SKILL.md as a tighter overview.

DimensionReasoningScore

Conciseness

Lean and dense throughout: tight tool inventory with inline parameters, compact phase-based workflows, and no padding or explanation of concepts Claude already knows; every section earns its place.

5 / 5

Actionability

Highly actionable with concrete tool names, parameters, a gotchas table, specific artifact-filter lists (GOL, EDO, SO4...) and thresholds (DoGSiteScorer >0.6), but provides no actual runnable Python/code example despite instructing Claude to 'write and run Python code'.

4 / 5

Workflow Clarity

Three clearly sequenced multi-phase workflows with decision rules exist, but there are no explicit validation/verification checkpoints or validate-fix-retry feedback loops for batch/data operations, which caps this dimension.

3 / 5

Progressive Disclosure

Well-organized single file with clear section headers (tool inventory, workflows, gotchas, evidence grading) and no external bundle files; structure is good though all content is inlined monolithically rather than split across references.

4 / 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 strong, specific description that names concrete capabilities, data sources, and explicit trigger use cases with a clear 'Use for' clause. It is slightly jargon-heavy but well-scoped and clearly distinguishable.

DimensionReasoningScore

Specificity

Lists multiple concrete actions ('druggability assessment, binding-site characterization, ligand-pocket analysis, structural-confidence scoring (resolution, pLDDT), and antibody-target interface analysis') plus specific data sources, giving comprehensive coverage.

5 / 5

Completeness

Clearly answers 'what' ('Combines PDB experimental structures, AlphaFold predictions...') and explicitly answers 'when' with a 'Use for...' clause listing concrete trigger use cases.

5 / 5

Trigger Term Quality

Includes natural phrases users would say ('drug target validation', 'druggability assessment', 'binding-site characterization') but is heavily domain-jargon-laden and omits some common synonyms; good but not fully comprehensive.

4 / 5

Distinctiveness Conflict Risk

Occupies a clear niche (structural proteomics for drug target validation) with distinct, specialized triggers; minimal risk of firing for unrelated skills.

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

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