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medchem

Medicinal chemistry filters for compound triage. Apply drug-likeness rules (Lipinski, Veber, CNS), structural alert catalogs (PAINS, NIBR, ChEMBL), complexity metrics, and the medchem query language for library filtering.

68

Quality

81%

Does it follow best practices?

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Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

80%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 highly actionable and well-structured with real, clearly signaled reference files, but its batch workflows lack explicit validation/verification checkpoints, which caps workflow clarity. Tightening a few verbose sections would further improve conciseness.

Suggestions

Add explicit validation steps to the workflow patterns (e.g., verify non-empty filtered output, check for failed SMILES parses, confirm expected DataFrame columns before writing).

Trim the Citing block or relocate it to a reference file to reduce non-instructional tokens in the main body.

Replace the placeholder lambda body in the Scaffold Constraints example with a concrete callback or a clear note that the function is user-supplied.

DimensionReasoningScore

Conciseness

The body is dense and mostly earns its tokens — lean per-capability code blocks, a brief overview, and a useful version note — but the overall volume (8 capabilities, 3 workflows, best practices, citation block) leaves a few sections that could be trimmed without loss.

4 / 5

Actionability

Nearly every section provides copy-paste-ready, executable Python with imports and expected outputs (e.g. RuleFilters, CommonAlertsFilters, QueryFilter), and the batch script is invoked with a concrete command line — fully covering the common cases.

5 / 5

Workflow Clarity

The three Workflow Patterns give clear sequences, but they are batch operations on compound libraries (read CSV → filter → write CSV) with no explicit validation or verification checkpoints, which the rubric caps at 3 for batch operations.

3 / 5

Progressive Disclosure

SKILL.md is a well-organized overview that signals one-level-deep references to real bundle files — references/api_guide.md, references/rules_catalog.md, and scripts/filter_molecules.py — each described with a usage example, keeping detail appropriately split and easy to navigate.

5 / 5

Total

17

/

20

Passed

Description

82%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.

The description is specific, trigger-rich, and highly distinctive, but it omits any explicit "Use when..." trigger guidance, which caps its completeness. Adding a use-when clause would lift the weakest dimension.

Suggestions

Append an explicit trigger clause such as "Use when triaging compound libraries, applying drug-likeness or structural-alert filters, or when the user mentions Lipinski/Veber/PAINS/NIBR filtering."

Optionally include the file formats or inputs users would name (.sdf, .smi, SMILES) to broaden natural trigger terms.

DimensionReasoningScore

Specificity

Lists multiple concrete actions with named specifics — "Apply drug-likeness rules (Lipinski, Veber, CNS)", "structural alert catalogs (PAINS, NIBR, ChEMBL)", "complexity metrics", and "the medchem query language for library filtering" — giving comprehensive coverage of the skill's capabilities.

5 / 5

Completeness

The "what" is clearly and concretely stated, but there is no "Use when..." clause or equivalent explicit trigger guidance; per the rubric, a missing use-when clause caps completeness at 3.

3 / 5

Trigger Term Quality

Comprehensive coverage of natural domain terms a medicinal chemist would actually say, including synonyms across rule families (Lipinski, Veber, CNS) and alert catalogs (PAINS, NIBR, ChEMBL) plus "drug-likeness", "compound triage", and "library filtering".

5 / 5

Distinctiveness Conflict Risk

Occupies a clear, specialized niche (medicinal chemistry compound triage) with distinct, domain-specific triggers that are unlikely to fire for unrelated skills, giving minimal conflict risk.

5 / 5

Total

18

/

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
K-Dense-AI/scientific-agent-skills
Reviewed

Table of Contents

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