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-singlepointnpx skills add learningmatter-mit/AtomisticSkills --skill chem-dft-orca-singlepointgit 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-singlepoint)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/chem-dft-orca-singlepoint"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/chem-dft-orca-singlepoint.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 | $0.00039 | $0.01944 |
| Opus 5 | $0.00019 | $0.00972 |
| Sonnet 5 | $0.00008 | $0.00389 |
| Haiku 4.5 | $0.00004 | $0.00194 |
Grade A, and why
chem-dft-orca-singlepoint 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 5d 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 — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DFT Single-Point Calculation with ORCA
Goal
Compute the DFT electronic energy and optionally forces (gradients) and/or the Hessian for a given molecular structure with the ORCA quantum chemistry program. The calculation relies on the SCINE wrapper for automated input generation, output parsing, and error handling, with curated defaults suitable for standard cases.
[!IMPORTANT] This skill is for standard DFT single-point calculations on molecular (non-periodic) systems. For advanced methods, multi-reference calculations, or properties not exposed here, use the advanced ORCA skill. For geometry optimization, use the ORCA optimization skill.
1. Prerequisites
- Conda environment:
orca-agentwithscine_utilitiesandaseinstalled - 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.)
2. Parameters
| Parameter | Default | Description |
|---|---|---|
--structure |
(required) | Path to input structure file |
--charge |
0 |
Molecular charge |
--spin_multiplicity |
1 |
Spin multiplicity (2S+1) |
--functional |
PBE |
DFT functional (e.g. PBE, B3LYP, wB97X-V, PBE0) |
--basis_set |
def2-SVP |
Basis set (e.g. def2-SVP, def2-TZVP, def2-TZVPP) |
--dispersion |
None | Dispersion correction (e.g. D3BJ, D4) |
--solvation |
None | Implicit solvation model: CPCM or SMD |
--solvent |
None | Solvent name (e.g. water, ethanol, dmso); 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 |
--compute_gradients |
off | Flag to also compute forces |
--compute_hessian |
off | Flag to also compute the Hessian matrix |
--calculator_settings |
None | Extra SCINE calculator settings as a JSON string (see below) |
--output_dir |
auto | Output directory |
What ships with it
6 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.
- 5d ago First seen · 169 lines · 39 tokens per session scan A 9bbbb798402c
chem-dft-orca-singlepoint is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (160 stars, last pushed 2d ago), licensed MIT. It adds 39 tokens to every session and 1,944 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…
hypogenic
Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use…
rdkit
Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom…
molfeat
Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
pytdc
Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.