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boltz-small-molecule-adme

Predict Tier-1 ADME/ADMET for small molecules with Boltz from bare SMILES — no target, no docking. Use when the user wants solubility, permeability, or lipophilicity/logD for a molecule or list of molecules. Not for ranking molecules against a protein target (use boltz-small-molecule-screen, which already returns ADME free).

78

Quality

97%

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SecuritybySnyk

Critical

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SKILL.md
Quality
Evals
Security

Quality

Content

100%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.

The body is a tight, fully executable skill: a numbered workflow with cost-confirmation and per-molecule validation checkpoints, a copy-paste-ready command pattern, and well-organized one-level-deep references. It respects the context budget while remaining actionable.

DimensionReasoningScore

Conciseness

Lean and operational throughout: every section and bullet earns its tokens, no padding or explanation of concepts Claude already knows, and dense 'Always Do This' guidance assumes Claude's competence.

5 / 5

Actionability

Provides a fully executable command with concrete flags, absolute-path placeholders, idempotency-key usage, and the exact output path, covering common cases including batching and per-molecule failures.

5 / 5

Workflow Clarity

A 5-step numbered workflow with explicit validation checkpoints (estimate-cost → explicit USD confirmation → run, plus per-molecule status checks) and a feedback loop for batching, satisfying the batch-operation validation requirement.

5 / 5

Progressive Disclosure

SKILL.md is a clear overview pointing one level deep to real, well-signaled references (references/api.md, references/results.md) that hold the bulk schema; navigation is easy and nothing is buried.

5 / 5

Total

20

/

20

Passed

Description

95%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 concise, well-anchored description that clearly states what it does, when to use it, and where the boundary against the sibling screening skill lies. The only minor gap is that the predicted outputs are framed as trigger terms rather than as an enumerated list of concrete actions.

Suggestions

Reframe the output fields (solubility, permeability, lipophilicity) as enumerated capabilities of the action — e.g. 'Predict solubility, permeability, and lipophilicity (LogD) for small molecules from bare SMILES' — to lift specificity from 4 to 5.

DimensionReasoningScore

Specificity

Names the domain and concrete deliverables ('Predict Tier-1 ADME/ADMET for small molecules ... from bare SMILES — no target, no docking') but the action set is somewhat singular; the specific outputs (solubility/permeability/lipophilicity) appear as trigger terms rather than enumerated actions.

4 / 5

Completeness

Explicitly answers both what ('Predict Tier-1 ADME/ADMET ... from bare SMILES') and when ('Use when the user wants solubility, permeability, or lipophilicity/logD ...') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Comprehensive natural-term coverage including synonyms and the canonical shorthand: 'solubility', 'permeability', 'lipophilicity/logD', 'ADME/ADMET', 'SMILES', 'small molecules'.

5 / 5

Distinctiveness Conflict Risk

Clear niche with an explicit boundary clause ('Not for ranking molecules against a protein target (use boltz-small-molecule-screen, which already returns ADME free)') that minimizes overlap with the sibling skill.

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
openai/plugins
Reviewed

Table of Contents

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