Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/learningmatter-mit/atomisticskills/chem-dft-orca-optimizationnpx skills add learningmatter-mit/AtomisticSkills --skill chem-dft-orca-optimizationgit clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/learningmatter-mit/atomisticskills/chem-dft-orca-optimization)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/chem-dft-orca-optimization"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/chem-dft-orca-optimization.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00037 | $0.02004 |
| Opus 5 | $0.00018 | $0.01002 |
| Sonnet 5 | $0.00007 | $0.00401 |
| Haiku 4.5 | $0.00004 | $0.00200 |
Grade A, and why
chem-dft-orca-optimization scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DFT Geometry Optimization with ORCA
Goal
Optimize the geometry of a molecular structure at the DFT level using the ORCA quantum chemistry program. Supports two modes: minimization (finding the nearest local minimum) and transition state (TS) optimization (single-ended saddle point search). The calculation uses the SCINE/ReaDuct wrapper for robust optimizer management.
[!IMPORTANT] This skill provides single-ended TS optimization only. For reaction pathway methods (NEB, IRC), consider using the MLIP-based NEB skill or IRC skill with MLIP pre-screening, then refine with DFT. For advanced ORCA features, use the advanced ORCA skill.
Background
Geometry optimization iteratively adjusts nuclear positions to minimize (or, for TS search, to find a first-order saddle point of) the potential energy surface $E(\mathbf{R})$. The SCINE/ReaDuct optimizer handles step control, coordinate transformations, and convergence criteria internally.
- Minimization seeks a stationary point where $\nabla E = 0$ and the Hessian has all positive eigenvalues.
- TS optimization seeks a first-order saddle point where $\nabla E = 0$ and the Hessian has exactly one negative eigenvalue.
1. Prerequisites
- Conda environment:
orca-agentwithscine_utilities,scine_readuct, andaseinstalled - ORCA binary: The environment variable
ORCA_BINARY_PATHmust point to the ORCA executableexport ORCA_BINARY_PATH=/path/to/orca - Input structure: A molecular structure file readable by ASE (
.xyz,.cif,.mol, etc.) - For TS optimization: Provide a reasonable TS guess geometry. Poor initial guesses will likely fail to converge to the correct saddle point.
2. Parameters
| Parameter | Default | Description |
|---|---|---|
--structure |
(required) | Path to input structure file |
--opt_type |
min |
min for minimization, ts for transition state search |
--charge |
0 |
Molecular charge |
--spin_multiplicity |
1 |
Spin multiplicity (2S+1) |
--functional |
PBE |
DFT functional (e.g. PBE, B3LYP, wB97X-V) |
--basis_set |
def2-SVP |
Basis set (e.g. def2-SVP, def2-TZVP) |
--dispersion |
None | Dispersion correction (e.g. D3BJ, D4) |
--solvation |
None | Implicit solvation model: CPCM or SMD |
--solvent |
None | Solvent name; required if --solvation is set |
--special_option |
NOSOSCF |
ORCA special option passed to SCINE calculator. Set to empty string to disable. |
--nprocs |
1 |
Number of CPU cores for ORCA |
--convergence_max_iterations |
200 |
Maximum optimization steps |
--calculate_final_hessian |
off | Compute Hessian at optimized geometry (for TS verification) |
--calculator_settings |
None | Extra SCINE calculator settings as a JSON string (see below) |
--optimizer_settings |
None | Extra ReaDuct optimizer kwargs as a JSON string (see below) |
--output_dir |
auto | Output directory |
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 157 lines · 37 tokens per session scan A 5bd0357f1b23
chem-dft-orca-optimization is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed yesterday), licensed MIT. It adds 37 tokens to every session and 2,004 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
datamol
Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters…
deepchem
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first…
binding-affinity
Hybrid ML + physics binding affinity prediction. Empirical scoring, MM/GBSA rescoring, multi-method consensus, and batch virtual screening for protein-ligand complexes.
drug-design
End-to-end drug discovery pipeline orchestration. Deterministic Python script that auto-chains structure prediction, pocket detection, de novo design, docking, scoring, and ADMET filtering into reproducible workflows.
medchem
Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering.
molecular-docking
End-to-end molecular docking pipeline. Target preparation, pocket detection, protein-ligand docking (DiffDock/Vina), scoring, interaction analysis, and pose ranking.