Cloud quantum chemistry platform with Python SDK. Run geometry optimization, conformer generation, torsional scans, and energy minimization (DFT/semiempirical), and retrieve properties (dipole, partial charges, frontier orbitals) — no local QC software or HPC needed.
SkillScore95Maintained
Automatic
◆Research pack
Last verified 8/24/2026
What it changes
Passed 21 of 22 test cases with the skill, and 11 of 22 without it.
Without skill50%
With skill95%
Checks it passed that the model alone missed
Calls .to_xyz() on result.geometry.
Sets solvent="water" in client.compute.
Requests 10 conformers using n_conformers=10.
and 13 more in the full report
10 fixed11 passed either way1 still failing0 broke
Pass-rate lift
+45 pp
Tokens
+178%
Agent turns
0%
Response time
-44%
Model gemini-3.6-flashMeasured 8/24/2026v121 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 10
Prompt
After running a Rowan geometry optimization job, I want to save the resulting optimized atomic positions to an XYZ file on disk. What attribute and method on the result object produces the formatted XYZ string?
What was checked, without and with the skill
Checked
Without skill
With skill
Calls .to_xyz() on result.geometry.
Missed
Passed
case-10 · gemini-3.6-flash · 8/24/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md412 linessingle file
rowan
Overview
Rowan is a cloud quantum chemistry platform that exposes DFT and semiempirical calculations through a Python SDK (rowan). Submit calculations (geometry optimization, conformer generation, torsional scans, single-point energies) from Python scripts or Jupyter notebooks, and retrieve results — energies, geometries, partial charges, frontier orbital energies — without managing Gaussian, ORCA, or Psi4 installations. Rowan handles job queuing, execution, and storage. A free tier is available for academic and exploratory use.
When to Use
Geometry optimization of small molecules: Getting accurate equilibrium geometries for drug candidates, fragments, or building blocks using DFT.
Conformer generation with energy ranking: Generating and optimizing multiple conformers to identify the lowest-energy conformation for docking or property prediction.
Torsional potential scans: Mapping the energy profile along a rotatable bond to understand conformational preferences.