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tooluniverse-protein-therapeutic-design

AI-guided de novo protein design — RFdiffusion backbone generation, ProteinMPNN sequence design, structure validation (pLDDT, pTM, MPNN scores). Use for designing therapeutic protein binders, novel scaffolds, enzyme variants, and miniprotein/protein-interface design before experimental validation.

Quality

84%

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SecuritybySnyk

Passed

No findings from the security scan

The canonical home for this skill is tooluniverse-protein-therapeutic-design 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 well-structured, dense skill body with concrete tool/parameter guidance, a clear phased workflow, and validation criteria. Its main weaknesses are dangling references to files absent from the bundle, no explicit error-recovery loop, and repeated API-key notes.

Suggestions

Ship the five referenced files (DESIGN_PROCEDURES.md, TOOLS_REFERENCE.md, EXAMPLES.md, CHECKLIST.md, design_templates.md) in the bundle — e.g., under references/ — or remove the Reference Files section, since none of them currently exist alongside SKILL.md.

Add an explicit feedback loop to the workflow for failed validation, e.g., 'if pLDDT < 70 or pTM < 0.65, regenerate the backbone with different contigs or resample sequences at a lower sampling temperature' instead of delegating fallback chains entirely to the missing DESIGN_PROCEDURES.md.

State the NVIDIA_API_KEY requirement once in the NVIDIA NIM Requirements section and drop the parenthetical from each table row, and fold the Common Parameter Mistakes table into the main tools table to remove duplicated parameter guidance.

DimensionReasoningScore

Conciseness

Mostly efficient tables and terse directives, but redundancy exists: the note "(requires NVIDIA_API_KEY env var; free key at build.nvidia.com)" is repeated five times in the tools table and again in the NVIDIA NIM Requirements section, and the Common Parameter Mistakes table partially repeats the key parameters already given. Not score 5 because not every token earns its place; well above score 3's noticeable padding.

4 / 5

Actionability

Concrete guidance throughout — exact tool names, required parameters ("contigs + input_pdb (both required), diffusion_steps (NOT num_steps...)"), sampling thresholds, and tier cutoffs ("pLDDT >85, pTM >0.8") — but no executable example invocations in the body; the code examples are delegated to DESIGN_PROCEDURES.md, which is not in the bundle. Fits 'mostly executable with minor gaps' rather than fully copy-paste ready.

4 / 5

Workflow Clarity

The 6-phase workflow is clearly sequenced with validation checkpoints (filter backbones by geometry, pLDDT/pTM thresholds, '>= 3 passing') and a completeness checklist, but the error-recovery feedback loop (what to do when validation fails, e.g., regenerate backbone or resample at different temperature) is only implied and delegated to a reference file. Score 4 rather than 5 because feedback loops are not explicit in the body.

4 / 5

Progressive Disclosure

The Reference Files section is well-signaled with one-line descriptions and would be one level deep, but none of the five referenced files (DESIGN_PROCEDURES.md, TOOLS_REFERENCE.md, EXAMPLES.md, CHECKLIST.md, design_templates.md) exist in the bundle — no references/, scripts/, or assets/ directories are present — so the disclosure paths are dangling and the detail content they promise is unavailable. Structure is better than score 2, but broken references keep it below score 4.

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: concrete, tool-named capabilities plus an explicit 'Use for' clause with specific design scenarios. Only minor gap is a few missing natural synonyms (e.g., protein engineering, antibody/peptide design) that users might say.

DimensionReasoningScore

Specificity

Lists multiple concrete pipeline actions with named tools and metrics — "RFdiffusion backbone generation, ProteinMPNN sequence design, structure validation (pLDDT, pTM, MPNN scores)" — covering the full design workflow comprehensively, matching the anchor for multiple specific concrete actions rather than the score-4 anchor with minor gaps.

5 / 5

Completeness

Explicitly answers both: what ("RFdiffusion backbone generation, ProteinMPNN sequence design, structure validation") and when ("Use for designing therapeutic protein binders, novel scaffolds, enzyme variants, and miniprotein/protein-interface design before experimental validation"), with concrete trigger phrases exactly as the score-5 anchor requires.

5 / 5

Trigger Term Quality

Good natural-term coverage ("protein design", "therapeutic protein binders", "novel scaffolds", "enzyme variants", "protein-interface design") but misses common synonyms a user might say such as "protein engineering", "antibody/nanobody", or "peptide design". Not score 5 since synonyms are not comprehensively covered; clearly above score 3's partial coverage.

4 / 5

Distinctiveness Conflict Risk

Clear niche (de novo therapeutic protein design) with named tools (RFdiffusion, ProteinMPNN) and distinct design-oriented triggers; minimal overlap with general structure-prediction or campaign-management skills since the 'Use for' clause scopes it to design tasks specifically.

5 / 5

Total

19

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

relative_links

Relative link issues: 1 suspicious

Warning

Total

15

/

16

Passed

Repository

mims-harvard/ToolUniverse

Path

plugins/tooluniverse/skills/tooluniverse-protein-therapeutic-design/SKILL.md

Reviewed

Table of Contents

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