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 Ling-MD/md-agent-skills --skill gromacs-mdgit clone --depth 1 https://github.com/Ling-MD/md-agent-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/ling-md/md-agent-skills/gromacs-md)<a href="https://agentmods.dev/skills/ling-md/md-agent-skills/gromacs-md"><img src="https://agentmods.dev/badge/skills/ling-md/md-agent-skills/gromacs-md/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/ling-md/md-agent-skills/gromacs-md"><img src="https://agentmods.dev/badge/skills/ling-md/md-agent-skills/gromacs-md.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 | $0.00086 | $0.01476 |
| Opus 5 | $0.00043 | $0.00738 |
| Sonnet 5 | $0.00017 | $0.00295 |
| Haiku 4.5 | $0.00009 | $0.00148 |
Grade A, and why
gromacs-md 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 11d 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GROMACS Molecular Dynamics
Use GROMACS for classical biomolecular MD workflows: protein or protein-ligand systems, membrane proteins, conformational dynamics, trajectory analysis, and approximate binding free energy post-processing.
This skill is intentionally lightweight. Use it to enforce the scientific workflow and risk checks; use local gmx, official GROMACS documentation, and optional scholaraio toolref for exact command syntax and version-specific .mdp details.
Local Environment
Prefer gmx on PATH. If GROMACS is installed elsewhere, set:
$env:GMX_EXE = "C:\path\to\gromacs\bin\gmx.exe"
Optional: set PYMOL_EXE for PyMOL rendering helpers in the companion skills.
The local scholaraio toolref GROMACS index contains the official current mdp-options source as version manual-current-mdp with 323 records. Full scholaraio toolref fetch gromacs may still require GitHub access; if it fails, the installed MDP index is enough for high-risk .mdp parameter lookup.
Verify before running real work:
gmx --version
For ligand and free-energy workflows, check optional tools before promising execution:
Get-Command acpype,antechamber,gmx_MMPBSA -ErrorAction SilentlyContinue
Protocol
Classify the user's request first:
gmxsubcommand or command-line troubleshooting.mdpparameter design or review- topology, force field, ligand, membrane, or box-building workflow
- production execution and performance
- trajectory analysis, visualization, or MM/PBSA-style post-processing
Before writing .mdp files, verify high-risk parameters: thermostat, barostat, constraints, cutoffs, PME, neighbor search, output frequency, coupling groups, timestep, and production ensemble.
If scholaraio is installed, use toolref-first:
scholaraio toolref search gromacs "temperature coupling"
scholaraio toolref show gromacs mdp integrator
scholaraio toolref show gromacs mdp pcoupl
scholaraio toolref show gromacs mdp tau-t
scholaraio toolref show gromacs mdp ref-t
What ships with it
2 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.
- 11d ago First seen · 113 lines · 86 tokens per session scan A cd6d91f423bc
gromacs-md is a skill published in the GitHub repository Ling-MD/md-agent-skills (4 stars, last pushed 3mo ago), licensed MIT. It adds 86 tokens to every session and 1,476 once invoked, about $0.0004 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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