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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.

72

Quality

87%

Does it follow best practices?

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SecuritybySnyk

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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

82%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 dense, well-organized instruction skill that earns its context budget with tool knowledge Claude cannot know (signatures, gotchas, thresholds). The main gaps are the absence of worked example invocations and explicit error-recovery checkpoints in the workflows.

Suggestions

Add one worked example invocation per workflow (e.g., PDBeSIFTS_get_best_structures(uniprot_id="P04637") followed by expected output fields) to make the guidance copy-paste ready.

Add explicit validation/recovery checkpoints to the workflows, such as what to do when BindingDB times out (60s+ noted in Limitations) or when no co-crystal structure exists for a target.

Move the Tool Inventory and Interpretation tables into a one-level-deep reference file (e.g., references/tools.md) to slim the always-loaded SKILL.md overview.

DimensionReasoningScore

Conciseness

Lean and dense throughout: "LOOK UP DON'T GUESS", "COMPUTE, DON'T DESCRIBE", terse tool signatures, a parameter-gotchas table, and domain-specific thresholds (resolution/R-free/pLDDT cutoffs, DoGSiteScorer >0.6) that Claude cannot be assumed to know. Not 4: there is essentially no over-explanation to trim — every section adds non-obvious domain or tool knowledge.

5 / 5

Actionability

Concrete tool names with their parameters, a correct-vs-mistake gotchas table, artifact ligand filter lists, and numeric decision thresholds give mostly executable guidance. Not 5: workflows are phase outlines with no worked example invocation (e.g., an actual tool call with values) or sample output; not 3: the guidance is specific and directly executable, not pseudocode.

4 / 5

Workflow Clarity

Three workflows with clearly sequenced phases and decision rules ("Resolution <2.5A for drug design", "Phase 4: PDBeValidation quality → binding site well-resolved?") plus evidence-grading tiers. Not 5: no explicit validate-and-retry feedback loops or error-recovery guidance (e.g., what to do when BindingDB times out or no co-crystal exists); not 3: checkpoints are present in each workflow, just implicit rather than looped.

4 / 5

Progressive Disclosure

No bundle files exist, so all content is appropriately in the single SKILL.md, and it is well-organized into scannable sections (tool inventory, workflows, gotchas, evidence grading, interpretation, limitations). Not 5: the ~45-line tool inventory and interpretation tables are content that could be split into one-level-deep reference files to slim the always-loaded overview; not 3: nothing is buried and navigation is easy.

4 / 5

Total

17

/

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 description that states concrete capabilities, names its specific data sources, and gives an explicit "Use for" clause with concrete trigger activities. The only weakness is modest synonym coverage for natural user phrasings.

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 named data sources (PDB, AlphaFold, GPCRdb, SAbDab, ProteinsPlus, BindingDB), giving comprehensive coverage of the domain. Not 4: there are no meaningful gaps in the action list; everything is concrete rather than generic.

5 / 5

Completeness

Explicitly answers both: "what" ("Combines PDB experimental structures, AlphaFold predictions, GPCRdb, SAbDab antibody structures...") and "when" ("Use for druggability assessment, binding-site characterization, ligand-pocket analysis...") with concrete trigger phrases. Not 4: the "when" clause is explicit and specific rather than needing more detail.

5 / 5

Trigger Term Quality

Good natural keyword coverage — "drug target validation", "druggability", "binding site", "ligand", "antibody", "AlphaFold", "pLDDT" are phrases users in this domain would actually say. Not 5: common variations and synonyms such as "protein structure", "cryo-EM", "co-crystal", or "affinity data" are absent or only implicit.

4 / 5

Distinctiveness Conflict Risk

Clear niche — structural proteomics for drug target validation with named specialized databases (GPCRdb, SAbDab, BindingDB, ProteinsPlus) that other skills are unlikely to claim. Not 4: trigger terms are domain-distinct with minimal overlap risk even against closely related bioinformatics 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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
mims-harvard/ToolUniverse
Reviewed

Table of Contents

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