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/internscience/chemclaw/surface-tension-predictornpx skills add InternScience/ChemClaw --skill surface-tension-predictorgit clone --depth 1 https://github.com/InternScience/ChemClawWrote 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/chemclaw/surface-tension-predictor)<a href="https://agentmods.dev/skills/internscience/chemclaw/surface-tension-predictor"><img src="https://agentmods.dev/badge/skills/internscience/chemclaw/surface-tension-predictor.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 5.1 | $0.00052 | $0.01536 |
| Opus 5 | $0.00026 | $0.00768 |
| Sonnet 5 | $0.00010 | $0.00307 |
| Haiku 4.5 | $0.00005 | $0.00154 |
Grade A, and why
surface_tension_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 6d 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 — 238 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Surface Tension Predictor
该 skill 用于根据分子的 SMILES 输出与 surfactant（表面活性剂）任务相关 的表面张力参考预测结果。
核心定位
本 skill 当前的定位是：
- 一个 工程上可运行 的表面张力相关预测 skill
- 一个支持 OpenClaw 调用 的结构-性质预测组件
- 一个包含：
baseline启发式工程基线public_joblib公开 surfactant 模型参考后端 的双后端实现
重要说明：
本 skill 当前不应被解释为：
- 通用小分子纯液体表面张力预测器
- 任意温度下真实表面张力预测模型
- 已完成科学验证的最终正式模型
其中，public_joblib 后端更准确的理解应为：
- 公开 surfactant 任务相关模型的接入与参考推理后端
- 输出值主要用于：
- 公开模型接入验证
- 工程联调
- 与 baseline 对照
- 在相近任务设定下做参考输出
适用场景
当用户有以下需求时，适合调用本 skill：
- 希望基于 SMILES 获得一个表面张力相关参考值
- 希望比较多个分子在当前模型下的相对输出差异
- 希望测试某个分子在 surfactant 相关公开模型上的推理结果
- 希望验证 OpenClaw 中表面张力相关 skill 的联调链路
- 希望对比
baseline与public_joblib的输出差异 - 希望把表面张力相关预测能力接入本地化工作流
不建议的使用方式
以下场景下，不应直接把当前结果解释为真实物性结论：
- 将
public_joblib输出直接视为普通小分子纯液体的真实表面张力 - 将结果用于声称“任意温度下的表面张力预测”
- 将当前输出当作已完成严格实验校准的最终数值
- 对明显不属于 surfactant 任务分布的分子做过强解释
当前支持的后端
本 skill 当前支持两个后端：
1）baseline
用途
- 本地基线预测
- 工程联调
- 轻量、本地、低依赖运行
- 在无公开模型环境下给出一个可返回结果
原理
- RDKit 解析分子
- 计算少量二维分子描述符
- 使用启发式经验公式得到估算值
特点
- 本地稳定可跑
- 适合作为工程基线
- 不依赖公开模型文件
- 更适合联调、快速测试和占位输出
局限
- 不属于正式训练模型
- 预测值是启发式工程估算
- 不应过度解释其物理意义
2）public_joblib
用途
- 接入公开训练模型
- 验证公开模型推理链路
- 作为与 baseline 对照的参考后端
- 提供 surfactant 任务相关的表面张力参考输出
原理
- RDKit 解析分子
- 计算 Mordred 描述符
- 根据
linear_SurfaceTension.joblib_parameters提取并排序特征 - 使用
linear_SurfaceTension.joblib中的预测器对象进行预测
当前已确认
- 预测器对象类型为
PLSRegression - 特征维度为
130 - 已在本地推理成功
- 部分分子会出现一定数量的缺失描述符
- 当前实现已对齐上游
properties_prediction.py的核心推理逻辑
更准确的语义
public_joblib 后端当前应理解为：
- 公开 surfactant 模型的接入版
- 参考后端
- 不是通用纯液体小分子表面张力最终模型
输入形式
本 skill 当前支持以下输入形式：
- 单个
SMILES字符串 - 分子名称 +
SMILES - 可扩展为批量 JSON 输入（若脚本实现中已支持）
推荐输入字段：
smiles- 可选
name
若脚本版本已支持，也可接收：
temperature_c
但需要注意：
- 当前
public_joblib后端即使接收温度参数，也不代表实际使用温度参与预测 - 因此不能把结果解释为温度显式建模结果
输出字段
主要输出字段
surface_tension_prediction
如果当前实现中已加入更保守的语义字段，还可能包括：
What ships with it
15 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.
- README.md 1.9 KB
- references/models/.gitkeep 0 B
- references/models/linear_SurfaceTension.joblib 31 KB
- references/models/linear_SurfaceTension.joblib_parameters 15 KB
- requirements.txt 75 B
- scripts/backends/__init__.py 0 B runs code
- scripts/backends/public_joblib_backend.py 6.7 KB runs code
- scripts/backends/surface_tension_backend.py 3.1 KB runs code
- scripts/check_env.py 6.3 KB runs code
- scripts/main_script.py 2.8 KB runs code
- scripts/utils/__init__.py 0 B runs code
- scripts/utils/descriptors.py 1.2 KB runs code
- scripts/utils/io_utils.py 1.4 KB runs code
- scripts/utils/mordred_utils.py 5.0 KB runs code
- scripts/utils/smiles_utils.py 276 B runs code
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.
- 6d ago First seen · 238 lines · 52 tokens per session scan A 4f45dbb7939b
surface_tension_predictor is a skill published in the GitHub repository InternScience/ChemClaw (52 stars, last pushed 5mo ago), licensed MIT. It adds 52 tokens to every session and 1,536 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.
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…
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.
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…
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.
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.
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…