agentmods.dev

pka_predictor

pka_predictor is a skill for Claude Code, Codex from InternScience/ChemClaw. It costs 39 tokens per session (1,921 once invoked), scanned A, original, MIT.

A small-molecule pKa prediction tool. pKa describes how readily a molecule gains or loses a proton, which affects its behavior in solution.

In plain words
What is it for?
Use it to predict small-molecule pKa values with the custom or Uni-pKa backend.
Why use it?
It provides either a local rule-based estimate or a model-based prediction without requiring a laboratory measurement.

Skill for Claude CodeCodex

not rated 52 5mo ago A scan 39 tokens original MIT
View the source file InternScience/ChemClaw
Install

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.

agentmods
npx agentmods add skills/internscience/chemclaw/pka-predictor
Any agent
npx skills add InternScience/ChemClaw --skill pka-predictor
Clone the repo
git clone --depth 1 https://github.com/InternScience/ChemClaw

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 pka_predictor

README.md
[![agentmods](https://agentmods.dev/badge/skills/internscience/chemclaw/pka-predictor.svg)](https://agentmods.dev/skills/internscience/chemclaw/pka-predictor)
Your own site
<a href="https://agentmods.dev/skills/internscience/chemclaw/pka-predictor"><img src="https://agentmods.dev/badge/skills/internscience/chemclaw/pka-predictor.svg" alt="Measured on agentmods" height="20"></a>
Per session 39 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,921 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00039 $0.01921
Opus 5 $0.00019 $0.00960
Sonnet 5 $0.00008 $0.00384
Haiku 4.5 $0.00004 $0.00192

Measured 5d ago against content hash 801060ff7d7a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

pka_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 5d ago.

The scan reads SKILL.md. This mod also ships 34 executable files (assets/Uni-Core/.github/workflows/cuda/cu102-Linux-env.sh, assets/Uni-Core/.github/workflows/cuda/cu102-Linux.sh, assets/Uni-Core/.github/workflows/cuda/cu113-Linux-env.sh, …), 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/pka-predictor/SKILL.md · 219 lines

How it starts

The opening of the file, as written. The whole thing — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.

pKa Predictor

预测小分子的 pKa，支持两类后端：

  • custom：基于规则、官能团和可选自定义模型的本地预测
  • unipka：基于 Uni-pKa Bohrium notebook 单文件权重路线 的微观态枚举 + 自由能汇总预测

⚡ 快速开始（首次使用必读）

1. 安装依赖

cd skills/pka-predictor
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

2. 下载 Uni-pKa 模型权重（仅 unipka 后端需要）

使用 Hugging Face CLI（推荐）：

# 安装 huggingface_hub（如果尚未安装）
pip install huggingface_hub

# 下载模型权重文件
mkdir -p skills/pka-predictor/assets/Uni-pKa/uni-pka-ckpt_v2
hf download Lai-ao/uni-pka-ckpt_v2 t_dwar_v_novartis_a_b.pt \
  --repo-type model \
  --local-dir skills/pka-predictor/assets/Uni-pKa/uni-pka-ckpt_v2

# 下载模板文件（如果本地没有）
hf download Lai-ao/uni-pka-ckpt_v2 smarts_pattern.tsv \
  --repo-type model \
  --local-dir skills/pka-predictor/assets/Uni-pKa/uni-pka-ckpt_v2

hf download Lai-ao/uni-pka-ckpt_v2 simple_smarts_pattern.tsv \
  --repo-type model \
  --local-dir skills/pka-predictor/assets/Uni-pKa/uni-pka-ckpt_v2

或手动下载：

访问 https://huggingface.co/Lai-ao/uni-pka-ckpt_v2 下载以下文件到 assets/Uni-pKa/uni-pka-ckpt_v2/ 目录：

  • t_dwar_v_novartis_a_b.pt（~571MB，模型权重）
  • smarts_pattern.tsv（SMARTS 模板）
  • simple_smarts_pattern.tsv（简化 SMARTS 模板）

3. 验证安装

# 测试 custom 后端
./scripts/run_with_venv.sh --smiles "CC(=O)O" --name "乙酸" --backend custom

# 测试 unipka 后端
./scripts/run_with_venv.sh --smiles "CC(=O)O" --name "乙酸" --backend unipka --cpu

触发条件

当用户有以下需求时适合调用：

  • 预测某个分子的 pKa
  • 估算 strongest acidic/basic pKa
  • 比较几个分子的电离性质
  • 查看不同电荷态之间的 pKa 转换
  • 分析质子化 / 去质子化行为

功能

  • ✅ 支持单个 SMILES 输入
  • ✅ 支持批量文件输入（.smi / .txt / .csv / .json）
  • ✅ 支持 JSON / CSV / TXT 输出
  • ✅ custom 后端支持官能团识别与启发式预测
  • ✅ unipka 后端支持模板枚举微观态并基于单文件权重做自由能预测
  • ✅ 返回结构化结果，便于 OpenClaw 解析

后端说明

custom

适合本地快速使用，不依赖 Uni-pKa 环境。
输出主要是启发式 strongest acidic/basic pKa。

unipka

基于 Bohrium notebook 单权重流程，而不是 GitHub 仓库中 infer_pka.sh 的 5-fold 推理流程。
它的基本逻辑是：

  1. 使用模板文件枚举相邻电荷态的微观态
  2. 用单文件权重模型预测各微观态自由能
  3. 对相邻电荷态使用 Boltzmann / log-sum-exp 汇总
  4. 得到相邻电荷态之间的 macro pKa transitions

Read the full file on GitHub · 219 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. 5d ago First seen · 219 lines · 39 tokens per session scan A 801060ff7d7a

Subscribe to this mod's changes

pka_predictor is a skill published in the GitHub repository InternScience/ChemClaw (52 stars, last pushed 5mo ago), licensed MIT. It adds 39 tokens to every session and 1,921 once invoked, about $0.0002 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