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rowan

Rowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API. Use for pKa and macropKa prediction, conformer and tautomer ensembles, docking and analogue docking, protein-ligand cofolding, MSA generation, molecular dynamics, permeability, descriptor workflows, and related small-molecule or protein modeling tasks. Ideal for programmatic batch screening, multi-step chemistry pipelines, and workflows that would otherwise require maintaining local HPC/GPU infrastructure.

Quality

87%

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SKILL.md
Quality
Evals
Security

Quality

Content

78%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: executable core patterns, explicit validation checkpoints, honest gotchas, and a clean one-level-deep reference bundle. The main improvements are removing duplicated examples and caveats for token efficiency, making the illustrative folder/docking snippets self-contained, and surfacing the batch feedback-loop guidance (currently in a reference) as an explicit validate-fix-retry step in the body.

Suggestions

Consolidate the duplicated aspirin descriptors example (Quick start vs. Core usage pattern) into one canonical example and state the "hosted examples are illustrative, not authenticated" caveat once to save tokens (conciseness).

Make the Folders docking snippet self-contained by defining or clearly constructing `protein`, `pocket`, and `ligand` (e.g., via `retrieve_protein` and `Molecule.from_smiles`) so it is copy-paste ready (actionability).

Add an explicit validate-and-retry step for batch campaigns in the main body — e.g., check each submitted workflow's status on retrieval and resubmit or inspect failures — rather than deferring feedback loops entirely to references/batch_and_webhooks.md (workflow_clarity).

DimensionReasoningScore

Conciseness

The body is dense with non-obvious, API-specific gotchas ("Folder.model_rebuild" workaround, "MW is exact/monoisotopic mass, not average molecular weight", pagination and non-recursive-listing semantics) and assumes Claude's competence throughout. Minor trimming is possible: the aspirin descriptors example appears in both Quick start and Core usage pattern, and the "hosted examples are illustrative" caveat is repeated several times. Fits the efficient-with-minor-trimmable-instances anchor; not 5 due to this duplication, not 3 because there is no padding explaining concepts Claude already knows.

4 / 5

Actionability

Mostly executable, concrete guidance: pinned install command, auth setup with verification via whoami(), submission calls with typed result access, and SMILES validation. Minor gaps remain — the Folders section submits with undefined `protein`, `pocket`, and `ligand` variables, which is labeled illustrative but not copy-paste ready. Matches the mostly-executable anchor; not 5 because a few snippets cannot be run as written.

4 / 5

Workflow Clarity

The submit → wait → retrieve pattern is clearly sequenced, and explicit validation checkpoints exist: SMILES validation with a raised ValueError, a None-check on `best_taut` before chaining workflows, redocking a known ligand to validate docking, and verifying PDB accessions. However, the main body stops short of explicit fix-and-retry feedback loops for batch/destructive operations (those are deferred to references/batch_and_webhooks.md). Fits the clear-sequence-with-most-checkpoints anchor; not 5 because feedback loops are implicit rather than spelled out.

4 / 5

Progressive Disclosure

The SKILL.md body keeps core usage, decision trees, and common pitfalls inline while splitting the workflow catalog, batch/webhooks, pricing, a worked example, and troubleshooting into five real, clearly signaled one-level-deep reference files. Each reference section summarizes exactly what it contains, and the bundle contains no nested (2+ level) references. Matches the clear-overview, well-signaled, appropriately split anchor.

5 / 5

Total

17

/

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 states what the platform does and when to use it with a comprehensive, concrete action list and an explicit "Use for..." trigger clause. The only weakness is slightly incomplete synonym coverage (e.g., "drug discovery", "computational chemistry") for natural user phrasing.

DimensionReasoningScore

Specificity

The description lists comprehensive concrete actions: "pKa and macropKa prediction, conformer and tautomer ensembles, docking and analogue docking, protein-ligand cofolding, MSA generation, molecular dynamics, permeability, descriptor workflows". This matches the anchor for multiple specific concrete actions with comprehensive coverage; not 4 because the gaps are negligible.

5 / 5

Completeness

Explicitly answers both questions: what ("cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API") and when ("Use for... Ideal for programmatic batch screening, multi-step chemistry pipelines..."). Matches the anchor for clearly and explicitly answering both with concrete trigger phrases.

5 / 5

Trigger Term Quality

Good natural keyword coverage ("pKa", "docking", "tautomer", "batch screening", "HPC/GPU infrastructure", "medicinal-chemistry"), but common user phrases like "drug discovery", "computational chemistry", and "ligand screening" are absent. Fits the anchor for good coverage with a few natural terms missing; not 5 because synonym coverage is incomplete.

4 / 5

Distinctiveness Conflict Risk

Names a specific platform (Rowan) in a niche domain (hosted molecular modeling), with distinct triggers unlikely to collide with other skills' descriptions. Clear niche with minimal conflict risk.

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

metadata_field

'metadata' should map string keys to string values

Warning

Total

15

/

16

Passed

Repository

K-Dense-AI/scientific-agent-skills

Path

skills/rowan/SKILL.md

Reviewed

Table of Contents

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