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tooluniverse-protein-structure-prediction

Protein 3D structure prediction from sequence — ESMFold de novo prediction, AlphaFold database retrieval, experimental structures from RCSB, ProtVar variant impact assessment, ProtParam sequence properties. Use for structure prediction when no experimental structure exists, fold-confidence scoring, and structure-guided variant interpretation.

Quality

85%

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-protein-structure-prediction in mims-harvard/ToolUniverse

SKILL.md
Quality
Evals
Security

Quality

Content

70%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-sequenced, actionable workflow with strong error recovery and interpretation guidance, but it is a long single-file skill with systematic caveat repetition and no progressive disclosure into reference files. Tightening duplicated caveats and splitting optional tool detail (DTU, Boltz) into references would materially improve it.

Suggestions

State each caveat once — collapse the repeated ~800-residue ESMFold limit and single-chain limitation into the Limitations section (or their primary phase) and reference it elsewhere.

Move optional tool detail — the DTU model table and Boltz paid-workflow section — into reference files (e.g., references/dtu-tools.md, references/boltz.md) linked from short overview paragraphs.

Add one concrete example invocation and a short parsing snippet (e.g., computing mean pLDDT and low-confidence ranges from the returned array) to make the Phase 2 workflow copy-paste executable.

DimensionReasoningScore

Conciseness

The bulk is genuinely useful tool-specific detail (parameters, thresholds, report templates), but key caveats are systematically repeated: the ~800-residue ESMFold limit appears six times (KEY PRINCIPLES, Input Parameters, Phase 0, Fallback Strategies, Databases, Limitations) and the single-chain limitation four times, and KEY PRINCIPLES duplicates the phase content.

3 / 5

Actionability

Concrete guidance throughout: exact tool names, parameter formats with examples ("P04637 R175H"), quantitative pLDDT/pTM interpretation tables, a report template, and per-failure fallbacks. As an instruction-only skill, absent code is acceptable, but there are no example invocations or parsing snippets (e.g., computing mean pLDDT from the returned array is described rather than shown), leaving minor gaps.

4 / 5

Workflow Clarity

The six-phase pipeline is explicitly sequenced with an overview diagram, per-phase step lists, decision logic, a Fallback Strategies table covering each failure mode (error-recovery loops), and a Quality Minimums checklist — matching the top anchor. Operations are read-only/computational, so the destructive-batch cap does not apply.

5 / 5

Progressive Disclosure

No bundle files exist and all ~370 lines are inline in SKILL.md. Internal structure is well organized with clear headers, but detail that plausibly belongs in reference files (DTU model table, Boltz paid-workflow specifics, tool parameter reference) is fully inlined with no pointer files, so the skill is a monolithic single-file overview.

3 / 5

Total

15

/

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: specific, comprehensive, and with an explicit use-when clause covering concrete triggers. Its only weakness is modest synonym coverage for natural user phrasings of structure prediction requests.

Suggestions

Add common user phrasings as trigger terms, e.g. "predict the fold of a sequence", "model a protein", or "how reliable is the predicted structure (pLDDT)".

Include retrieval-comparison phrasings ("compare AlphaFold to the PDB structure") since that comparison workflow is a core part of the skill.

DimensionReasoningScore

Specificity

The description lists five distinct concrete capabilities ("ESMFold de novo prediction, AlphaFold database retrieval, experimental structures from RCSB, ProtVar variant impact assessment, ProtParam sequence properties"), comprehensively covering the workflow with no material gaps.

5 / 5

Completeness

Both questions are answered explicitly: the "what" enumerates five named capabilities, and the "when" is a concrete trigger clause ("Use for structure prediction when no experimental structure exists, fold-confidence scoring, and structure-guided variant interpretation").

5 / 5

Trigger Term Quality

Natural trigger terms like "structure prediction", "fold-confidence scoring", and "structure-guided variant interpretation" are present, but common user phrasings and synonyms ("predict the fold", "model a protein", "pLDDT", "experimental structure comparison") are missing.

4 / 5

Distinctiveness Conflict Risk

It carves out a clear niche (sequence-to-structure prediction with named databases) with distinct triggers such as fold-confidence scoring and variant interpretation, minimizing overlap with generic retrieval 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

Path

plugins/tooluniverse/skills/tooluniverse-protein-structure-prediction/SKILL.md

Reviewed

Table of Contents

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