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-mol-opt-physchemgit 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-mol-opt-physchem)<a href="https://agentmods.dev/skills/internscience/molclaw/molclaw-mol-opt-physchem"><img src="https://agentmods.dev/badge/skills/internscience/molclaw/molclaw-mol-opt-physchem/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-mol-opt-physchem"><img src="https://agentmods.dev/badge/skills/internscience/molclaw/molclaw-mol-opt-physchem.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 | $0.00051 | $0.02241 |
| Opus 5 | $0.00026 | $0.01120 |
| Sonnet 5 | $0.00010 | $0.00448 |
| Haiku 4.5 | $0.00005 | $0.00224 |
Grade A, and why
molclaw-mol-opt-physchem 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 9d 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 — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Molecule Optimization for Physicochemical Properties
Note:
- Local files are not directly accessible by the server. Please upload them to the server using
molclaw-file-transferbefore execution. - For PDB file inputs, it is recommended to preprocess them using
molclaw-pdbfixerbefore execution. - Please refer to skill
molclaw-scp-serverto complete tool invocation.
step 1
Use skill molclaw-admet to calculate multiple physicochemical properties for the source molecule and generate a summary report, with special emphasis on the properties specified in the user query.
step 2
Based on the molecular property analysis report generated in step 1 and the following prompt, leverage the reasoning capabilities of the Large Language Model (LLM) to generate an optimized molecule from the source molecule, while providing a detailed rationale for the optimization.
Role Definition
You are an expert medicinal chemist with 15+ years of experience in lead optimization and drug design. You specialize in optimizing physicochemical properties of small molecule drugs while maintaining structural integrity and synthetic feasibility.
Task Overview
Your task is to optimize the source molecule to improve specific physicochemical properties (LogP, QED, or Solubility) while following drug discovery best practices. You must provide:
- A structured optimization reasoning process
- The final optimized molecule in SMILES format
- Clear justification for each modification
Background Knowledge & Guidelines
1. Lipinski's Rule of Five (RO5) - Fundamental Drug-likeness Criteria
| Property | Optimal Range | Impact |
|---|---|---|
| Molecular Weight (MW) | < 500 Da | Higher MW reduces oral bioavailability |
| LogP (lipophilicity) | -0.4 to 5.0 | Affects membrane permeability & solubility |
| Hydrogen Bond Donors (HBD) | ≤ 5 | Too many reduces cell permeability |
| Hydrogen Bond Acceptors (HBA) | ≤ 10 | Too many reduces absorption |
| Rotatable Bonds | ≤ 10 | Affects molecular flexibility & bioavailability |
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.
- 9d ago First seen · 176 lines · 51 tokens per session scan A 49229b78370a
molclaw-mol-opt-physchem is a skill published in the GitHub repository InternScience/MolClaw (33 stars, last pushed 1mo ago), licensed MIT. It adds 51 tokens to every session and 2,241 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 2026-08-30.
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