agentmods.dev

molecular_properties_predictor

molecular_properties_predictor is a skill for Claude Code, Codex from InternScience/ChemClaw. It costs 54 tokens per session (1,699 once invoked), scanned A, original, MIT.

A tool that predicts several physical and chemical properties of a single small molecule from its SMILES structure, such as melting point, boiling point, density, viscosity, and vapor pressure. SMILES is a text notation for describing a molecule.

In plain words
What is it for?
Use it for single-molecule or batch property prediction at a chosen temperature, including melting point, boiling point, refractive index, dielectric constant, surface tension, density, viscosity, and vapor pressure.
Why use it?
It provides multiple property estimates in one request, which can help compare molecules or prepare data for later screening. The documentation warns that its pKa values are not its recommended use.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python scripts/main_script.py --smiles "CC(=O)O" --name "乙酸".

not rated 52repo 5mo ago A scan 54 tokens original MIT

Good fit Use it for single-molecule or batch property prediction at a chosen temperature, including melting point, boiling point, refractive index, dielectric constant, surface tension, density, viscosity, and vapor pressure.

Compare 6 skills from other repositories ↓
View source ↗ InternScience/ChemClaw
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/InternScience/ChemClaw
agentmods
npx agentmods add skills/internscience/chemclaw/molecular-properties-predictor

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for molecular_properties_predictor

README.md
[![agentmods](https://agentmods.dev/badge/skills/internscience/chemclaw/molecular-properties-predictor.svg)](https://agentmods.dev/skills/internscience/chemclaw/molecular-properties-predictor)
Your own site
<a href="https://agentmods.dev/skills/internscience/chemclaw/molecular-properties-predictor"><img src="https://agentmods.dev/badge/skills/internscience/chemclaw/molecular-properties-predictor.svg" alt="Measured on agentmods" height="20"></a>
Per session 54 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,699 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00054 $0.01699
Opus 5 $0.00027 $0.00849
Sonnet 5 $0.00011 $0.00340
Haiku 4.5 $0.00005 $0.00170

Measured 8d ago against content hash de96172fa48c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

molecular_properties_predictor 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 8d ago.

The scan reads SKILL.md. This mod also ships 49 executable files (assets/bamboo_mixer/after_clone.sh, assets/bamboo_mixer/formula_design/__init__.py, assets/bamboo_mixer/formula_design/data/__init__.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/molecular-properties-predictor/SKILL.md · 171 lines

How it starts

The opening of the file, as written. The whole thing — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Molecular Properties Predictor

功能概述

该 skill 用于预测小分子多种物化性质。

当前版本通过 Bamboo-Mixer 单分子模型返回以下 11 个性质：

  • Tm：熔点（K）
  • bp：沸点（K）
  • nD：折射率（无单位）
  • nD_liquid：液体折射率（无单位）
  • dc：介电常数（无单位）
  • ST：表面张力（mN/m）
  • density：密度（g/cm^3）
  • vis：黏度（cP）
  • vapP：蒸气压（Pa）

适用场景

当用户有如下需求时适合调用：

  • 查询一个分子的多种物化性质
  • 一次性获取熔点、沸点、密度、黏度等多个性质
  • 为下游分子筛选提供多指标输入
  • 作为拆分单项物性 skill 之前的总入口

输入形式

单分子输入

  • smiles：必填
  • name：可选
  • temperature：可选，默认 25.0

批量输入

支持 JSON 列表输入，每项至少包含：

  • smiles
  • name（可选）

输出字段

每个结果条目通常包含：

  • name
  • smiles
  • canonical_smiles
  • status
  • temperature_celsius
  • backend_used
  • model_source
  • properties
  • property_units
  • raw_backend_output

⚠️ pKa 预测特别说明

本 skill 虽然输出 pka_a 和 pka_b 字段，但不推荐用于 pKa 预测。

推荐方案： 请使用专门的 pka-predictor skill 进行 pKa 预测。

原因：

对比项 molecular-properties-predictor pka-predictor
pKa 准确度 中等（误差 ~0.4 单位） 高（误差 ~0.17 单位）
后端 Bamboo-Mixer 多任务模型 Uni-pKa 专用模型
微观态处理 无 支持微观态枚举 + 自由能计算
输出详细度 仅返回数值 电荷态、去质子化方向、置信度等

示例：

# ❌ 不推荐：用本 skill 预测 pKa
python scripts/main_script.py --smiles "CC(=O)O" --name "乙酸"

# ✅ 推荐：用 pka-predictor 预测 pKa
cd ../pka-predictor && ./run_with_venv.sh --smiles "CC(=O)O" --name "乙酸" --backend unipka

⚠️ 表面张力预测特别说明

本 skill 可预测表面张力 (ST)，但对于单一表面张力预测需求，推荐使用专门的 surface-tension-predictor skill。

推荐策略：

需求场景 推荐 Skill/后端
仅预测表面张力 surface-tension-predictor
表面张力 + 多种物性 molecular-properties-predictor (Bamboo-Mixer)
小分子 (<10 重原子) surface-tension-predictor (baseline)
大分子 (≥10 重原子) surface-tension-predictor (public_joblib)

原因：

对比项 molecular-properties-predictor surface-tension-predictor
表面张力准确度 好（误差 ~5%） 好（误差 ~5% baseline）
后端 Bamboo-Mixer 多任务模型 baseline / public_joblib
特征数 隐式描述符 8 个 (baseline) / 130 个 (public_joblib)
灵活性 固定 11 个性质 可切换后端，针对表面张力优化
适用场景 多种物性一次性预测 单一表面张力预测

Read the full file on GitHub · 171 lines

Files

What ships with it

60 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.

Changes

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.

  1. 8d ago First seen · 171 lines · 54 tokens per session scan A de96172fa48c

Subscribe to this mod's changes

molecular_properties_predictor is a skill published in the GitHub repository InternScience/ChemClaw (52 stars, last pushed 5mo ago), licensed MIT. It adds 54 tokens to every session and 1,699 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.

Scientific
Related

Other skills, from other repositories

instrument-data-to-allotrope

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…

anthropics/knowledge-work-plugins · 123 tokens

exploratory-data-analysis

Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…

K-Dense-AI/scientific-agent-skills · 83 tokens

matlab

Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.

K-Dense-AI/scientific-agent-skills · 42 tokens

phylogenetics

Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.

K-Dense-AI/scientific-agent-skills · 68 tokens

research-engineer

An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.

davila7/claude-code-templates · 43 tokens

mapping-to-snomed

Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…

maziyarpanahi/openmed · 205 tokens