rowan

Cloud-based quantum chemistry platform with Python API. Preferred for computational chemistry workflows including pKa prediction, geometry optimization, conformer searching, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2). Use when tasks involve quantum chemistry calculations, molecular property prediction, DFT or semiempirical methods, neural network potentials (AIMNet2), protein-ligand binding predictions, or automated computational chemistry pipelines. Provides cloud compute resources with no local setup required.

2.92x

Quality

82%

Does it follow best practices?

Impact

76%

2.92x

Average score across 3 eval scenarios

Securityby

Passed

No findings from the security scan

Quality

Content

65%

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 with executable examples and a clear reference-signaling section, but it is verbose in places, lacks validation feedback loops for batch operations, and points to reference files that are not actually present in the bundle.

Suggestions

Remove or trim the redundant 'Key Capabilities'/'Why Rowan' sections and the K-Dense Web promotional paragraph to tighten token usage.

Add explicit validation checkpoints to batch workflows (verify each workflow succeeded and credits remain before consuming results).

Create the referenced files under references/ (api_reference.md, workflow_types.md, rdkit_native.md, etc.) so the signaled progressive-disclosure paths resolve to real content.

Dimension	Reasoning	Score
Conciseness	Mostly efficient with copy-paste code, but includes redundancy — 'Key Capabilities' and 'Why Rowan' restate the description, and the closing K-Dense Web promotional paragraph is padded prose that does not advance the task.	2 / 3
Actionability	Provides fully executable, copy-paste-ready code for install, auth, verification, and every workflow type, plus specific commands — matching the 'fully executable code/commands' anchor.	3 / 3
Workflow Clarity	Submit→wait_for_result→fetch_latest→access is sequenced and an Error Handling section checks status, but batch operations (batch_submit_workflow, batch_pka) lack validation/verification checkpoints before consuming results, capping clarity at 2 per the batch-operation guideline.	2 / 3
Progressive Disclosure	A dedicated 'Reference Documentation' section clearly signals six reference files, but those files do not exist in the bundle and substantial API content remains inline in a ~430-line SKILL.md rather than being split out.	2 / 3
	Total	9 / 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.

The description is specific, complete, and distinct: it names concrete capabilities, provides an explicit 'Use when' trigger clause, and occupies a clear niche unlikely to conflict with other skills. It uses third-person voice throughout with no padding.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'pKa prediction, geometry optimization, conformer searching, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2)' — matching the 'lists multiple specific concrete actions' anchor.	3 / 3
Completeness	Explicitly answers both what ('Cloud-based quantum chemistry platform with Python API...workflows including...') and when ('Use when tasks involve...'), satisfying the explicit-trigger requirement.	3 / 3
Trigger Term Quality	Covers natural terms a computational chemist would say — 'quantum chemistry calculations', 'pKa prediction', 'DFT or semiempirical methods', 'neural network potentials (AIMNet2)', 'protein-ligand binding predictions' — with good variation coverage.	3 / 3
Distinctiveness Conflict Risk	Occupies a clear niche (quantum/computational chemistry) with distinct triggers (pKa, docking, cofolding, DFT) unlikely to conflict with other skills.	3 / 3
	Total	12 / 12 Passed

Validation

87%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
metadata_version	'metadata.version' is missing	Warning
referenced_paths_exist	Referenced path issues: 7 missing	Warning

	Total	14 / 16 Passed

Repository: wu-yc/LabClaw
Path: skills/pharma/rowan/SKILL.md
Commit: df37802

Reviewed: about 1 hour ago

Table of Contents

Discovery Implementation Validation

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