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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

86%

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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, information-dense skill body with concrete tool parameters, explicit validation gates, and clearly signaled one-level-deep references. It loses points for redundant inline API-key notes, the absence of any inline executable example, and only implied error-recovery loops.

Suggestions

State the NVIDIA_API_KEY requirement once in the 'NVIDIA NIM Requirements' section and remove the repeated parenthetical from each table row to reclaim tokens.

Include one short inline example call (e.g., an NvidiaNIM_rfdiffusion invocation with contigs, input_pdb, and diffusion_steps) so the quick-start is executable without opening a reference file.

Add a brief inline error-recovery loop (e.g., 'if pLDDT < 70 or few sequences pass, regenerate backbones or raise MPNN sampling temperature') and format the Reference Files entries as markdown links.

DimensionReasoningScore

Conciseness

The body is dense and operational (parameter tables, a wrong-vs-correct mistakes table, thresholds, checklists) with no explanation of basic concepts, but the note '(requires NVIDIA_API_KEY env var; free key at build.nvidia.com)' is repeated inline in six table rows despite a dedicated 'NVIDIA NIM Requirements' section, which could be trimmed.

4 / 5

Actionability

Highly concrete guidance — exact parameter names ('contigs + input_pdb (both required)', 'diffusion_steps (NOT num_steps; default 15)'), the mistakes table, rate limits, and quantified targets ('>= 5 backbones', '>= 8 sequences per backbone') — but no executable code or command examples appear inline; they are all deferred to reference files, so it stops short of copy-paste ready.

4 / 5

Workflow Clarity

Six phases are clearly sequenced with explicit validation checkpoints (Phase 4 pLDDT/pTM comparison, tier thresholds, a completeness checklist gating '>= 3 passing'), but error-recovery feedback loops are only implied — 'fallback chains' is deferred to DESIGN_PROCEDURES.md and no inline guidance says what to do when validation fails.

4 / 5

Progressive Disclosure

Good structure: an overview-level body plus a 'Reference Files' section listing five files with one-line descriptions, one level deep. Minor gaps: the file entries are bolded names rather than links, and no bundle files were present to verify the referenced paths resolve.

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 pairs concrete named-tool capabilities with an explicit, concrete 'Use for...' trigger clause in third-person voice. Its only weakness is modest synonym coverage in trigger terms.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions with named tools and metrics — 'RFdiffusion backbone generation, ProteinMPNN sequence design, structure validation (pLDDT, pTM, MPNN scores)' — giving comprehensive coverage of the skill's capabilities.

5 / 5

Completeness

Explicitly answers both 'what' (backbone generation, sequence design, structure validation with named tools) and 'when' ('Use for designing therapeutic protein binders, novel scaffolds, enzyme variants, and miniprotein/protein-interface design before experimental validation') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Good natural keyword coverage ('therapeutic protein binders', 'novel scaffolds', 'enzyme variants', 'miniprotein/protein-interface design') that users would plausibly say, but common synonyms like 'protein engineering', 'antibody design', or 'peptide design' are missing, so it falls short of the comprehensive-synonyms anchor.

4 / 5

Distinctiveness Conflict Risk

Clear niche — de novo therapeutic protein design tied to specific named tools (RFdiffusion, ProteinMPNN, pLDDT/pTM validation) — with distinct triggers ('binder', 'scaffold', 'enzyme variant', 'miniprotein') that minimize conflict with generic bioinformatics or structure-analysis skills.

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

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

Reviewed

Table of Contents

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