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tooluniverse-protein-structure-retrieval

Protein structure retrieval from RCSB PDB, PDBe, and AlphaFold with disambiguation, quality assessment (resolution, R-factor, pLDDT), and metadata. Distinguishes high-quality experimental (X-ray under 2 Angstrom) vs predicted vs medium-quality structures. Use for fetching protein structures, structure-quality comparison, and selecting structures for drug design or modeling.

69

Quality

83%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

The canonical home for this skill is tooluniverse-protein-structure-retrieval in mims-harvard/ToolUniverse

SKILL.md
Quality
Evals
Security

Quality

Content

75%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 well-structured, dense skill body with a clearly sequenced four-phase workflow, concrete tool calls, fallback chains, and error-handling guidance. The main gaps are minor: slight redundancy between the Domain Reasoning prose and the quality tables, code fragments with unbound variables rather than runnable examples, no explicit post-retrieval validation checkpoint, and everything inlined with no reference files despite the skill's length.

Suggestions

Merge the 'Domain Reasoning' prose into the Quality Assessment tables — its resolution/pLDDT guidance duplicates the 'Resolution Use Cases' and 'AlphaFold Confidence' sections — or cut it entirely, since the tables already encode the same rules more compactly.

Replace the Phase 1/Phase 2 code fragments that reference unbound variables (name, uniprot_id) with one small self-contained runnable example (e.g. resolving a protein name to a PDB ID and fetching its quality scores), so the guidance is copy-paste ready.

Add an explicit validation checkpoint between Phase 2 and Phase 3 — e.g. 'confirm the retrieved entry matches the requested organism and UniProt accession before reporting; if not, fall back to the next candidate' — and consider moving the Tool Reference and detailed quality tables into a one-level-deep references file to keep SKILL.md as an overview.

DimensionReasoningScore

Conciseness

The body is lean and table-driven — quality tiers, resolution use cases, pLDDT bands, fallback chains, and error handling are all compressed into dense tables with no explanation of concepts Claude already knows. It misses 5 because the 'Domain Reasoning' prose partially duplicates the later Quality Assessment tables ('X-ray <2 A is high-quality for drug design' vs the Resolution Use Cases table), which could be merged.

4 / 5

Actionability

Phase 2 provides concrete, near-executable tool calls with real arguments (e.g. 'PDBeValidation_get_quality_scores(pdb_id=pdb_id)', 'PDBeSIFTS_get_all_structures(pdb_id=pdb_id, cutoff=2.0)') and a worked PDB ID ('4INS'). It falls short of fully copy-paste-ready because the code fragments use unbound variables (name, uniprot_id, uniprot_id in Phase 1) and no full runnable snippet ties the phases together.

4 / 5

Workflow Clarity

The workflow is explicitly sequenced (Phase 0 Clarify → Phase 1 Disambiguate → Phase 2 Retrieve → Phase 3 Report) with clear ask/skip conditions in Phase 0, an identity checklist, fallback chains, and an error-to-response table that functions as recovery guidance. It does not reach 5 because there is no explicit verification checkpoint before reporting (e.g. confirming the retrieved entry matches the requested organism/UniProt).

4 / 5

Progressive Disclosure

No bundle files exist, and the ~135-line body is well organized into clearly signaled sections (Workflow, phases, Quality Assessment, Error Handling, Tool Reference) that are easy to navigate. It scores 4 rather than 5 because the skill exceeds the simple-skill threshold and inlines content — the full Tool Reference and Quality Assessment tables — that would naturally live in one-level-deep reference files.

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 description that names the domain, concrete capabilities with specific quality metrics, and an explicit 'Use for...' trigger clause covering the main use cases. The only weakness is modest synonym coverage in trigger terms (no '3D structure', 'PDB file', or 'download' phrasing).

DimensionReasoningScore

Specificity

The description lists multiple specific concrete actions — 'retrieval from RCSB PDB, PDBe, and AlphaFold with disambiguation, quality assessment (resolution, R-factor, pLDDT), and metadata' — with named databases and named quality metrics, matching the comprehensive-coverage anchor. It is not the 4 anchor because there are no meaningful gaps in the action list for this domain.

5 / 5

Completeness

Both halves are explicit: the 'what' is concrete (retrieval, disambiguation, quality assessment with specific metrics, metadata, distinguishing experimental vs predicted tiers) and the 'when' is an explicit trigger clause ('Use for fetching protein structures, structure-quality comparison, and selecting structures for drug design or modeling'). This matches the anchor that clearly answers both what AND when with concrete trigger phrases.

5 / 5

Trigger Term Quality

Natural phrases like 'fetching protein structures', 'structure-quality comparison', 'selecting structures for drug design or modeling', plus database names (RCSB PDB, PDBe, AlphaFold) give good keyword coverage. It falls short of 5 because common variations users would say — e.g. '3D structure', 'PDB file', 'download structure', 'homolog' — are absent.

4 / 5

Distinctiveness Conflict Risk

The description carves a clear niche — protein structure retrieval from three named structural-biology databases with quality assessment — with trigger phrases ('structure-quality comparison', 'selecting structures for drug design') that would not naturally route to a generic search or document skill. Conflict risk is minimal.

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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