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 gromacs-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/gromacs-workflow)<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/gromacs-workflow"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/gromacs-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/gromacs-workflow"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/gromacs-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 | $0.00005 | $0.17025 |
| Opus 5 | $0.00003 | $0.08512 |
| Sonnet 5 | $0.00001 | $0.03405 |
| Haiku 4.5 | $0.00001 | $0.01702 |
Grade A, and why
gromacs-workflow scanned grade A with 1 finding 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
result = subprocess.run(cmd, capture_output=True, text=True) How it starts
The opening of the file, as written. The whole thing — 1,093 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GROMACS Workflow
Description
This skill covers end-to-end molecular dynamics simulations using GROMACS: topology construction and the .top/.itp hierarchy, .mdp parameter anatomy for energy minimization, NVT, NPT, and production runs, the grompp → mdrun → analysis pipeline, cutoff and PME setup, thermostat and barostat selection, constraint algorithms (LINCS, SETTLE), force-field compatibility (AMBER, CHARMM, OPLS-AA, GROMOS, Martini), HPC execution with MPI/OpenMP/GPU offload, and restart continuation. GROMACS is the standard tool for biomolecular and soft-matter simulations and is the reference code for AMBER, CHARMM36, and OPLS-AA force fields. Invoke this skill when setting up, running, diagnosing, or extending any GROMACS simulation.
Domain Context
GROMACS integrates Newton's equations of motion using a leap-frog Verlet integrator (the md integrator) or stochastic dynamics (sd). Its distinctive architecture separates the run-input file (.tpr, compiled from topology + mdp + coordinates by grompp) from the raw inputs (.top, .mdp, .gro), which means all simulation parameters are locked into the binary .tpr before mdrun starts. This compile-then-run model makes GROMACS simulations highly reproducible but requires re-running grompp whenever any parameter changes.
Several domain assumptions govern GROMACS applicability:
- Fixed topology: The bond list, angle list, dihedral list, and improper list are defined at the start of the simulation in the
.topfile and cannot change during the run. GROMACS is not a reactive MD code. Bond-breaking, proton transfer, and chemical reactions require reactive force fields (ReaxFF via PLUMED or an external code) or QM/MM. - Classical nuclei: GROMACS integrates classical equations of motion. Zero-point energy and nuclear tunneling are ignored. For proton dynamics at low temperature, path-integral extensions (GROMACS + i-PI) are required.
- Long-range electrostatics are always present in biomolecular systems. Biomolecular and drug-like systems have partial charges on every atom. PME (Particle Mesh Ewald) is mandatory; a plain cutoff produces artifacts in electrostatic energy and forces that invalidate free energy calculations, solvation energies, and structural properties of charged systems.
- Force-field and water-model pairing is non-negotiable. AMBER ff14SB was parameterized with TIP3P; CHARMM36m with mTIP3P (a modified TIP3P with different LJ parameters). Using TIP4P with ff14SB or TIP3P with CHARMM36m introduces systematic density and structural errors that cannot be corrected by re-equilibration.
- GROMACS units: nm, kJ/mol, ps, K, bar. All
.mdpdistance parameters (rcoulomb, rvdw, rlist, box vectors) are in nanometres, not Ångströms. This is the single most frequent source of silently wrong input from users familiar with LAMMPS or AMBER.
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 · 1,093 lines · 5 tokens per session scan A 3ad8440261a2
gromacs-workflow is a skill published in the GitHub repository SFETNI/Deep-Matter-Chem-Skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 5 tokens to every session and 17,025 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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…
smiles-validation
Strict SMILES validation, structural comparison, and modification verification. Catches invalid LLM-generated molecules.
patsnap-biological-modality
Biological sequence and modality intelligence via Patsnap MCP.
patsnap-scientific-translational-evidence
Patsnap Scientific & Translational Evidence MCP for AI agents. Retrieval platform focusing on scientific literature and translational outcomes, covering academic publication queries and translational medicine record tracking.
patsnap-target-disease
Patsnap Target & Disease MCP for AI agents. Target and disease profiling tool, covering target characterization, disease profiling, and epidemiology evidence retrieval.
patsnap-solution-engine
Patsnap TRIZ Concept Solution Engine MCP for AI agents. Generates innovation or product cost-reduction concepts through asynchronous TRIZ and TRIZ/DFMA workflows. Use for engineering problem solving, concept alternatives, cost-reduction analysis, task-progress retrieval, and selected-solution details.