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 InternScience/MolClaw --skill molclaw-protein-openmmgit clone --depth 1 https://github.com/InternScience/MolClawWrote 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/internscience/molclaw/molclaw-protein-openmm)<a href="https://agentmods.dev/skills/internscience/molclaw/molclaw-protein-openmm"><img src="https://agentmods.dev/badge/skills/internscience/molclaw/molclaw-protein-openmm/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/internscience/molclaw/molclaw-protein-openmm"><img src="https://agentmods.dev/badge/skills/internscience/molclaw/molclaw-protein-openmm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00064 | $0.01590 |
| Opus 5.5 | $0.00026 | $0.00636 |
| Sonnet 5.5 | $0.00013 | $0.00318 |
| Haiku 4.5 | $0.00006 | $0.00159 |
Grade A, and why
molclaw-protein-openmm 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 1mo 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 — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Protein OpenMM MD and Frame Extraction
Invocation
Use the exact tool name and parameter schema exposed by the current harness. Tool names in SDK examples below are raw MCP names, not model-facing aliases. Local files must be uploaded with molclaw-file-transfer before a server tool can use them. Repair PDB inputs first when the workflow requires it.
Usage
1. Protein OpenMM MD
The description of tool protein_openmm_md.
Runs OpenMM-based protein molecular dynamics preparation and simulation for structure refinement workflows.
Args:
protein_pdb (str): Absolute or relative path to input protein PDB.
solvent_type (str): Solvent mode, 'explicit' or 'implicit', default 'explicit'.
gb_model (str): GB model for implicit solvent mode, default 'GBn2'.
water_model (str): Water model for explicit solvent mode, default 'tip3p'.
force_field (str): OpenMM force field name, default 'amber14'.
md_time (float): Production MD time in picoseconds, default 100000.0.
platform (str): OpenMM compute platform, default 'CUDA'.
full_md (bool): Run full MD procedure if True, default False.
Return:
status (str): 'success' or 'error'.
msg (str): Human-readable execution summary.
command (str): The invoked command ('protein_openmm_md').
run_dir (str | None): Final run directory under tool_result/openmm_md_result.
work_dir (str | None): Same as run_dir for compatibility.
trajectory_path (str | None): Path to md_traj.dcd when available.
energy_log (str | None): Path to md.log when available.
generated_files (List[str]): File paths relative to work_dir.
md_time (float): Echoed requested MD time in ps.
solvent_type (str): Echoed solvent mode.
force_field (str): Echoed force field.
full_md (bool): Echoed full MD mode.
How to use tool protein_openmm_md :
response = await client.session.call_tool(
"protein_openmm_md",
arguments={
"protein_pdb": "/path/to/input.pdb",
"solvent_type": "implicit",
"gb_model": "OBC2",
"water_model": "tip3p",
"force_field": "amber14",
"md_time": 1000.0,
"platform": "CUDA",
"full_md": True
}
)
result = client.parse_result(response)
key_output = result["work_dir"]
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.
- 1mo ago Changed · -1 lines · +40 tokens per session 3c7fae66881f
- 1mo ago First seen · 199 lines · 24 tokens per session scan A 429726c43af8
molclaw-protein-openmm is a skill published in the GitHub repository InternScience/MolClaw (38 stars, last pushed 1mo ago), licensed MIT. It adds 64 tokens to every session and 1,590 once invoked, about $0.0003 per session on Opus 5.5. 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.
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