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 skills add SFETNI/Deep-Matter-Chem-Skills --skill computational-chemistry-workflowgit clone --depth 1 https://github.com/SFETNI/Deep-Matter-Chem-SkillsWrote 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/sfetni/deep-matter-chem-skills/computational-chemistry-workflow)<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/computational-chemistry-workflow"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/computational-chemistry-workflow/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/computational-chemistry-workflow"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/computational-chemistry-workflow.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00007 | $0.08749 |
| Opus 5 | $0.00003 | $0.04374 |
| Sonnet 5 | $0.00001 | $0.01750 |
| Haiku 4.5 | $0.00001 | $0.00875 |
Grade A, and why
computational-chemistry-workflow 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 12d 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 — 510 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Computational Chemistry Workflow
Description
This skill covers end-to-end quantum chemistry workflows for molecules, reactions, and molecular materials: structure preparation and standardization; geometry optimization; frequency analysis and thermochemistry; transition-state search and reaction-profile construction; method and basis-set selection and benchmarking; solvation modeling; open-shell and multireference diagnostics; excited-state calculations; and coupling to molecular featurization, property prediction, and surrogate-model pipelines. Primary backends are ORCA for DFT and post-HF calculations and xTB for fast semiempirical pre-screening and conformer ranking. Invoke this skill when an agent needs to compute molecular geometries, energies, reaction barriers, thermochemical corrections, spectra, or molecular properties from first principles.
Domain Context
Quantum chemistry describes molecular electronic structure by solving approximations to the electronic Schrödinger equation. The accuracy of any prediction depends on three choices that must be made consistently: the electronic structure method (how electron correlation is treated), the basis set (how atomic orbitals are represented), and the molecular model (charge, multiplicity, conformer, solvent environment). Getting any one wrong can produce qualitatively incorrect results regardless of compute time spent.
The method–accuracy–cost ladder (approximate, molecule-dependent):
| Tier | Representative methods | Formal cost scaling | Typical use |
|---|---|---|---|
| Force-field / classical | MMFF94, UFF, GAFF | O(N) | Conformer search, crude geometry seed |
| Semiempirical tight-binding | GFN2-xTB, GFN1-xTB, PM7 | O(N²–N³) | Large-scale conformer ranking, pre-screening |
| Pure DFT / HF | PBE, BLYP, HF | O(N³–N⁴) | Fast DFT for large systems; limited accuracy |
| Hybrid DFT | B3LYP, PBE0, M06-2X, ωB97X-D | O(N³–N⁴) | Mainstream molecular energetics and geometries |
| Double-hybrid DFT | B2PLYP, DLPNO-B2PLYP, ωB97M(2) | O(N⁵) | High-accuracy energetics; challenging cases |
| MP2 / SCS-MP2 | MP2, RI-MP2, SCS-MP2 | O(N⁵) | Post-HF baseline; poor for MR or open-shell |
| Coupled cluster | CCSD, CCSD(T), DLPNO-CCSD(T) | O(N⁶–N⁷) | Benchmark quality; DLPNO makes it feasible |
| Composite methods | G4, W1, CBS-QB3, ONIOM | Problem-dependent | Thermochemical accuracy (ΔH < 1 kcal/mol) |
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.
- 12d ago First seen · 510 lines · 7 tokens per session scan A ae1827cfb830
computational-chemistry-workflow is a skill published in the GitHub repository SFETNI/Deep-Matter-Chem-Skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 7 tokens to every session and 8,749 once invoked, about $0.0000 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 2026-08-31.
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