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/kdevos12/alkyl/coarse-grainednpx skills add Kdevos12/ALKYL --skill coarse-grainedgit clone --depth 1 https://github.com/Kdevos12/ALKYLWrote 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/kdevos12/alkyl/coarse-grained)<a href="https://agentmods.dev/skills/kdevos12/alkyl/coarse-grained"><img src="https://agentmods.dev/badge/skills/kdevos12/alkyl/coarse-grained.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 | $0.00066 | $0.00770 |
| Opus 5 | $0.00033 | $0.00385 |
| Sonnet 5 | $0.00013 | $0.00154 |
| Haiku 4.5 | $0.00007 | $0.00077 |
Grade A, and why
coarse-grained 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 3d 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Coarse-Grained Molecular Dynamics
Purpose
Run µs–ms scale MD simulations using coarse-grained force fields. Primary use cases: membrane self-assembly, protein-membrane interactions, lipid nanoparticles, large conformational changes, crowding effects.
When to Use This Skill
- Simulating lipid bilayers, vesicles, or membrane proteins
- Accessing timescales (µs–ms) beyond all-atom MD reach
- Screening protein-membrane binding or insertion
- Studying large-scale conformational changes (IDPs, domain motion)
- Building membrane systems for subsequent AA MD (backmapping)
- Coarse-grained small molecule parameterization (MARTINI)
Reference Files
| File | Content |
|---|---|
references/cg-theory.md |
CG resolution levels, mapping schemes, Boltzmann inversion, force matching, MARTINI 3 philosophy, bead types, scaling factors |
references/martini-proteins.md |
martinize2, elastic network (ElNeDyn), Go-MARTINI, OpenMM/GROMACS protein CG setup, common pitfalls |
references/martini-membranes.md |
Lipid library, insane.py membrane builder, CHARMM-GUI CG, protein-membrane embedding, lipid mixing |
references/cgmd-workflows.md |
GROMACS CG workflow (mdp parameters, timestep, thermostat), OpenMM CG, backmapping (backward.py), equilibration protocol |
references/cg-analysis.md |
MDAnalysis CG trajectories, membrane thickness/APL/order parameters, lateral diffusion, protein CG RMSD/RMSF, density profiles |
Quick Routing
"Set up a lipid bilayer simulation" → martini-membranes.md
"Convert my protein to MARTINI CG" → martini-proteins.md
"Run a CG simulation in GROMACS" → cgmd-workflows.md
"Backmap CG structure to all-atom" → cgmd-workflows.md (backward.py section)
"Analyze membrane properties from CG trajectory" → cg-analysis.md
"What resolution should I use?" → cg-theory.md
Key Numbers (MARTINI 3)
| Property | Value |
|---|---|
| Mapping ratio | ~4 heavy atoms per bead |
| Timestep (default) | 20 fs (safe: 10–30 fs) |
| Time scaling factor | ×4 (CG time ≈ 4× real time) |
| vdW cutoff | 1.1 nm |
| Electrostatics cutoff | 1.1 nm |
| Recommended thermostat | v-rescale (τ=1 ps) |
| Recommended barostat | Parrinello-Rahman (τ=12 ps) |
| Effective timestep | 80 fs (20 fs × 4 scaling) |
| Accessible timescale | µs per day (GPU) |
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.
- 3d ago First seen · 64 lines · 66 tokens per session scan A bdceb13f1417
coarse-grained is a skill published in the GitHub repository Kdevos12/ALKYL (6 stars, last pushed 5mo ago), licensed MIT. It adds 66 tokens to every session and 770 once invoked, about $0.0003 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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