Data Analysis
rowan
85100Total Score
Core Capability
83 / 100
Functional Suitability
11 / 12
Reliability
9 / 12
Performance & Context
6 / 8
Agent Usability
14 / 16
Human Usability
7 / 8
Security
11 / 12
Maintainability
9 / 12
Agent-Specific
16 / 20
Medical Task
20 / 20 Passed
91Cloud-based quantum chemistry platform providing a Python API. Preferred for computational chemistry workflows including pKa prediction, geometry optimization, conformational search, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2). Suitable for tasks involving quantum chemistry calculations, molecular property prediction, DFT or semi-empirical methods, neural network potentials (AIMNet2), protein-ligand binding prediction, or automated computational chemistry pipelines. Provides cloud computing resources without local installation
4/4
87Cloud-based quantum chemistry platform providing a Python API. Preferred for computational chemistry workflows including pKa prediction, geometry optimization, conformational search, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2). Suitable for tasks involving quantum chemistry calculations, molecular property prediction, DFT or semi-empirical methods, neural network potentials (AIMNet2), protein-ligand binding prediction, or automated computational chemistry pipelines. Provides cloud computing resources without local installation
4/4
85Cloud-based quantum chemistry platform providing a Python API. Preferred for computational chemistry workflows including pKa prediction, geometry optimization, conformational search, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2). Suitable for tasks involving quantum chemistry calculations, molecular property prediction, DFT or semi-empirical methods, neural network potentials (AIMNet2), protein-ligand binding prediction, or automated computational chemistry pipelines. Provides cloud computing resources without local installation
4/4
85Documentation-first workflow with no packaged script requirement
4/4
85Listing and Querying Workflows
4/4
Veto GatesRequired pass for any deployment consideration
Skill Veto✓ All 4 gates passed
✓
Operational Stability
System remains stable across varied inputs and edge cases
PASS✓
Structural Consistency
Output structure conforms to expected skill contract format
PASS✓
Result Determinism
Equivalent inputs produce semantically equivalent outputs
PASS✓
System Security
No prompt injection, data leakage, or unsafe tool use detected
PASSResearch Veto✅ PASS — Applicable
| Dimension | Result | Detail |
|---|---|---|
| Scientific Integrity | PASS | The archived review kept this workflow anchored to supplied data fields and observable execution behavior, not fabricated results. |
| Practice Boundaries | PASS | Practice boundaries held because the package remained focused on Cloud-based quantum chemistry platform providing a Python API. Preferred for computational... rather than overclaiming what the records supported. |
| Methodological Ground | PASS | Methodological grounding was preserved through the documented inputs, transformations, and expected artifacts. |
| Code Usability | PASS | Code usability passed because the package still exposed a reviewable execution surface for its documented workflow. |
Core Capability83 / 100 — 8 Categories
Functional Suitability
Functional suitability was softened by the legacy issue 'Improve stress-case output rigor'. Stress and boundary scenarios show weaker consistency
11 / 12
92%
Reliability
Reliability was softened by the legacy issue 'Improve stress-case output rigor'. Stress and boundary scenarios show weaker consistency
9 / 12
75%
Performance & Context
The archived review left minor headroom in how this analysis workflow scales across heavier contexts.
6 / 8
75%
Agent Usability
The packaged analysis path is understandable, though the archived score suggests slightly clearer routing would help.
14 / 16
88%
Human Usability
The package is readable overall, though the archived review still left a small human-usability gap.
7 / 8
88%
Security
The packaged workflow stayed safe overall, with only a small remaining deduction around boundary signaling.
11 / 12
92%
Maintainability
The analysis package is maintainable overall, though the archived score suggests modest cleanup headroom.
9 / 12
75%
Agent-Specific
Related legacy finding for rowan: Improve stress-case output rigor. Stress and boundary scenarios show weaker consistency
16 / 20
80%
Core Capability Total83 / 100
Medical TaskExecution Average: 86.6 / 100 — Assertions: 20/20 Passed
91
Canonical
Cloud-based quantum chemistry platform providing a Python API. Preferred for computational chemistry workflows including pKa prediction, geometry optimization, conformational search, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2). Suitable for tasks involving quantum chemistry calculations, molecular property prediction, DFT or semi-empirical methods, neural network potentials (AIMNet2), protein-ligand binding prediction, or automated computational chemistry pipelines. Provides cloud computing resources without local installation
4/4 ✓
87
Variant A
Cloud-based quantum chemistry platform providing a Python API. Preferred for computational chemistry workflows including pKa prediction, geometry optimization, conformational search, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2). Suitable for tasks involving quantum chemistry calculations, molecular property prediction, DFT or semi-empirical methods, neural network potentials (AIMNet2), protein-ligand binding prediction, or automated computational chemistry pipelines. Provides cloud computing resources without local installation
4/4 ✓
85
Edge
Cloud-based quantum chemistry platform providing a Python API. Preferred for computational chemistry workflows including pKa prediction, geometry optimization, conformational search, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2). Suitable for tasks involving quantum chemistry calculations, molecular property prediction, DFT or semi-empirical methods, neural network potentials (AIMNet2), protein-ligand binding prediction, or automated computational chemistry pipelines. Provides cloud computing resources without local installation
4/4 ✓
85
Variant B
Documentation-first workflow with no packaged script requirement
4/4 ✓
85
Stress
Listing and Querying Workflows
4/4 ✓
91
Canonical✅ Pass
Cloud-based quantum chemistry platform providing a Python API. Preferred for computational chemistry workflows including pKa prediction, geometry optimization, conformational search, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2). Suitable for tasks involving quantum chemistry calculations, molecular property prediction, DFT or semi-empirical methods, neural network potentials (AIMNet2), protein-ligand binding prediction, or automated computational chemistry pipelines. Provides cloud computing resources without local installation
The archived run treated Cloud-based quantum chemistry platform providing a Python API.... as a bounded analysis workflow rather than a purely narrative instruction path.
Basic 36/40|Specialized 55/60|Total 91/100
✅A1The rowan output structure matches the documented deliverable
✅A2The instruction path remains actionable for the documented case
✅A3The output stays fully within the documented skill boundary
✅A4The response quality is acceptable for the documented path
Pass rate: 4 / 4
87
Variant A✅ Pass
Cloud-based quantum chemistry platform providing a Python API. Preferred for computational chemistry workflows including pKa prediction, geometry optimization, conformational search, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2). Suitable for tasks involving quantum chemistry calculations, molecular property prediction, DFT or semi-empirical methods, neural network potentials (AIMNet2), protein-ligand binding prediction, or automated computational chemistry pipelines. Provides cloud computing resources without local installation
The archived run treated Cloud-based quantum chemistry platform providing a Python API.... as a bounded analysis workflow rather than a purely narrative instruction path.
Basic 34/40|Specialized 53/60|Total 87/100
✅A1The rowan output structure matches the documented deliverable
✅A2The instruction path remains actionable for the documented case
✅A3The output stays fully within the documented skill boundary
✅A4The response quality is acceptable for the documented path
Pass rate: 4 / 4
85
Edge✅ Pass
Cloud-based quantum chemistry platform providing a Python API. Preferred for computational chemistry workflows including pKa prediction, geometry optimization, conformational search, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2). Suitable for tasks involving quantum chemistry calculations, molecular property prediction, DFT or semi-empirical methods, neural network potentials (AIMNet2), protein-ligand binding prediction, or automated computational chemistry pipelines. Provides cloud computing resources without local installation
The archived run treated Cloud-based quantum chemistry platform providing a Python API.... as a bounded analysis workflow rather than a purely narrative instruction path.
Basic 33/40|Specialized 52/60|Total 85/100
✅A1The rowan output structure matches the documented deliverable
✅A2The instruction path remains actionable for the documented case
✅A3The output stays fully within the documented skill boundary
✅A4The response quality is acceptable for the documented path
Pass rate: 4 / 4
85
Variant B✅ Pass
Documentation-first workflow with no packaged script requirement
Documentation-first workflow with no packaged script requirement remained tied to the documented analysis contract even when the preserved evidence centered on instructions instead of a full rerun.
Basic 32/40|Specialized 53/60|Total 85/100
✅A1The rowan output structure matches the documented deliverable
✅A2The instruction path remains actionable for the documented case
✅A3The output stays fully within the documented skill boundary
✅A4The response quality is acceptable for the documented path
Pass rate: 4 / 4
85
Stress✅ Pass
Listing and Querying Workflows
This stress case stayed within the packaged analysis boundary and kept a reviewable task contract.
Basic 29/40|Specialized 56/60|Total 85/100
✅A1The rowan output structure matches the documented deliverable
✅A2The instruction path remains actionable for the documented case
✅A3The output stays fully within the documented skill boundary
✅A4The response quality is acceptable for the documented path
Pass rate: 4 / 4
Medical Task Total86.6 / 100
Key Strengths
- Primary routing is Data Analysis with execution mode A
- Static quality score is 83/100 and dynamic average is 78.6/100
- Assertions and command execution outcomes are recorded per input for human review
- Execution verification summary: No script verification was applicable