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boltz-protein-design

Design new protein binders with Boltz. Use when generating protein, peptide, antibody, nanobody, or custom binder candidates for a target. Not for screening existing proteins or small molecules.

75

Quality

92%

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SecuritybySnyk

Critical

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SKILL.md
Quality
Evals
Security

Quality

Content

85%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-structured, highly actionable skill body with a clear numbered workflow, explicit spending/validation gates, and clean one-level-deep reference split. Its only real weakness is redundancy: the spending gate, Codex background-command handling, and step-1 framing are each stated in multiple sections.

Suggestions

Consolidate the spending-gate rule into one place (e.g. step 8) and have Run sizing and Always Do This refer to it rather than restating it, to cut repeated paragraphs.

Move the Codex yield_time_ms / background download handling into a single location (Command Pattern or one Always Do This bullet) and delete the duplicated phrasings in step 10 and the extra bullet.

Trim the Always Do This entries that re-explain the step-1 conversation and the num_proteins range already covered in the Workflow and Run sizing sections, keeping only the guardrails not stated elsewhere.

DimensionReasoningScore

Conciseness

The body assumes Claude's competence (no basic-concept padding) and is actionable throughout, but it is repetitive: the spending gate is restated in step 8, Run sizing, and Always Do This; Codex yield_time_ms/background handling recurs in step 10, the Command-Pattern comments, and two Always Do This bullets; and Always Do This re-covers the step-1 conversation and num_proteins range already in the workflow. It is mostly efficient but could be tightened.

2 / 3

Actionability

The Command Pattern gives a complete, copy-paste-ready boltz-api invocation with real flags and clearly marked path placeholders, the payload field names are named exactly, and Run sizing gives concrete tier counts (20k/50k/100k), matching the fully-executable anchor.

3 / 3

Workflow Clarity

The 11-step workflow is clearly sequenced with explicit validation checkpoints for a batch money-spending operation: estimate-cost + spending gate before every start, client-side 10<=num_proteins<=1,000,000 check, and setup/auth retry loops, matching the clear-sequence-with-explicit-validation anchor.

3 / 3

Progressive Disclosure

SKILL.md is a clear overview that points one level deep to real, well-signaled references (api.md, results.md, target-exploration.md — all present) with content appropriately split, and analysis scripts are bundled separately, matching the clear-overview-with-well-signaled-one-level-deep-references anchor.

3 / 3

Total

11

/

12

Passed

Description

100%

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 tight, third-person description that states the concrete action, enumerates natural binder-type trigger terms, gives an explicit Use-when clause, and adds a negative-scope guardrail against sibling-skill conflicts. It hits every dimension at the top of the scale.

DimensionReasoningScore

Specificity

"Design new protein binders with Boltz" names a concrete action and the phrase "generating protein, peptide, antibody, nanobody, or custom binder candidates" lists multiple specific concrete outputs, matching the anchor for listing multiple specific concrete actions.

3 / 3

Completeness

It explicitly answers both what ("Design new protein binders with Boltz") and when ("Use when generating... candidates for a target"), plus a negative trigger ("Not for screening existing proteins or small molecules"), clearly answering both what and when with explicit triggers.

3 / 3

Trigger Term Quality

"protein, peptide, antibody, nanobody, or custom binder candidates for a target" gives good coverage of natural terms a user would say (e.g. "design an antibody/nanobody for X"), with no jargon-only phrasing.

3 / 3

Distinctiveness Conflict Risk

It carves a clear de-novo-binder niche and the explicit negative scope ("Not for screening existing proteins or small molecules") separates it from sibling screening skills, making wrong-skill triggering unlikely.

3 / 3

Total

12

/

12

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
openai/plugins
Reviewed

Table of Contents

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