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deepchem

Builds molecular property prediction and MoleculeNet workflows with DeepChem, including SMILES featurization, scaffold or grouped holdouts, masked labels, graph models and explicit pretrained encoder transfer. Used for ADMET, toxicity, solubility and chemistry ML when DeepChem data/model contracts and scientific validation are needed.

Quality

91%

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SKILL.md
Quality
Evals
Security

Quality

Content

86%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, highly actionable skill body: executable commands, a clear validated workflow, and exemplary progressive disclosure into real reference files. The main improvements are consolidating repeated negative-capability caveats and adding an explicit recovery step when the CSV guard rejects rows.

Suggestions

Consolidate the repeated fixed-epochs/no-early-stopping/no-hyperparameter-search caveat (stated in both 'Bundled scripts' and the reference files) into a single location.

Trim negative-capability sentences that do not change agent behavior (e.g., listing everything the script does not export) into the review reference file.

Add an explicit feedback step for CSV-guard rejections, e.g., 'if the guard rejects rows, save them to a rejection audit, correct or drop them, then re-run loading'.

DimensionReasoningScore

Conciseness

The body is dense with non-obvious contract details ("MoleculeNet's 'ECFP' alias is 1024 bits, while the fingerprint class defaults to 2048") and assumes competence rather than explaining basics. It falls short of fully lean because the fixed-epochs/no-early-stopping caveat and several negative-capability statements ("does not export split IDs", "simply creating a fresh model is not transfer learning") recur across sections and could be consolidated.

4 / 5

Actionability

Guidance is fully executable: a copy-paste pinned install block, concrete script invocations with arguments, and a model-to-featurizer contract table that maps each model path to its required input. Common cases are covered with specific commands.

5 / 5

Workflow Clarity

The 6-step Workflow is clearly sequenced with embedded checkpoints ("reject empty splits", "AUC requires both observed classes", "evaluate the final holdout once") and the CSV guard validates inputs. It lacks an explicit fix-and-retry feedback loop after validation failures, keeping it just below the top anchor.

4 / 5

Progressive Disclosure

The body is a genuine overview that pushes detail into five well-signaled, one-level-deep reference files (api_reference.md, typical_workflows.md, workflows.md, core_capabilities.md, review.md), all of which exist and are appropriately split by concern. Navigation via the 'References and validation' list is easy.

5 / 5

Total

18

/

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, specific description in third-person voice that clearly separates what the skill does from when to use it, with domain-natural trigger terms. The only gap is a few missing synonyms (e.g., QSAR, drug discovery) that some users might use instead.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions — "SMILES featurization, scaffold or grouped holdouts, masked labels, graph models and explicit pretrained encoder transfer" — covering the skill's capability range comprehensively in third-person voice. It exceeds the 'minor gaps in coverage' level below.

5 / 5

Completeness

The first sentence explicitly states what the skill builds, and "Used for ADMET, toxicity, solubility and chemistry ML when DeepChem data/model contracts and scientific validation are needed" is an explicit when-clause with concrete trigger phrases. Both what and when are clearly and explicitly answered.

5 / 5

Trigger Term Quality

Strong natural terms ("ADMET", "toxicity", "solubility", "SMILES", "MoleculeNet", "DeepChem") that users in this domain would actually say. A few natural synonyms such as "QSAR" or "drug discovery" are missing, so it falls just short of comprehensive coverage.

4 / 5

Distinctiveness Conflict Risk

The description is anchored to a specific library and benchmark ("DeepChem", "MoleculeNet") with niche-specific triggers, giving a clear lane with minimal risk of firing for unrelated skills.

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

K-Dense-AI/scientific-agent-skills

Path

skills/deepchem/SKILL.md

Reviewed

Table of Contents

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