computational-chemistry
33
pka-predictor
预测小分子的 pKa，支持 custom 启发式后端和 Uni-pKa 单文件权重后端（Bohrium notebook 路线）。
InternScience
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computational-chemistry
33
uv-vis-spectrum-simulation
UV-Vis spectrum from SMILES via UV-adVISor (https://spectra.collaborationspharma.com/). Input SMILES, auto-fetch, plot PNG.
InternScience
research
computational-chemistry
33
mol-paper-renderer
论文级分子渲染工具。使用 xyzrender 生成出版质量的 SVG、PNG、PDF 和 GIF 动画。支持过渡态、非共价相互作用、分子轨道、晶体结构等高级功能。
InternScience
research
computational-chemistry
33
geometry-optimizer
使用半经验方法 (xTB) 对分子三维结构进行几何优化，支持 SMILES 自动转 3D、XYZ 文件输入，输出优化后坐标、能量、收敛状态。
InternScience
research
computational-chemistry
33
chem-data-extractor
Extract structured chemical compound characterization data from chemistry supplementary material documents (PDF/Markdown). 从化学论文补充材料(PDF/Markdown)中提取结构化化合物表征数据。 Use when Kimi needs to extract compound properties including NMR spectra, HRMS, HPLC data, melting points, optical rotation, and yield information from chemistry research papers or supplementary materials. 支持提取NMR谱图、HRMS、HPLC数据、熔点、旋光度、产率等信息。 Supports both single compound extraction and batch extraction of all compounds. 支持单个化合物提取和批量提取所有化合物。
InternScience
research
computational-chemistry
33
adme-prediction
ADME 性质预测工具。预测分子的吸收、分布、代谢、排泄性质，包括 Caco-2 通透性、PAMPA、HIA、Pgp 抑制、生物利用度、亲脂性等。使用 Morgan 指纹 + Random Forest/XGBoost。当用户提到 ADME 预测、药物性质、通透性、吸收、代谢等时触发。
InternScience
research
computational-chemistry
33
boron-nmr-predict
Predict 11B (boron-11) NMR chemical shift for boron-containing molecules using a local CPU inference pipeline. Use when the user asks to predict boron NMR or 11B chemical shift and provides a molecule such as a SMILES string. The skill can download model weights from Hugging Face on first use, run local CPU inference, and generate a labeled molecule image so each predicted shift can be matched to a specific boron atom.
InternScience
research
computational-chemistry
33
xor-shader-techniques
Use when implementing shader effects like turbulence, fluid, fire, smoke, procedural noise, starfields, volumetric rendering, raymarching, glow, antialiasing, fractal texturing, or optimizing shader performance with cheap alternatives. Triggers on: shader techniques, shader tricks, shader optimization, turbulence shader, fluid shader, fire shader, smoke shader, procedural noise, dot noise, gyroid noise, efficient chaos, star field, particle scatter, fractal texturing, LOD texturing, texture scaling, volumetric rendering, raymarching, glow effects, antialiasing, analytic antialiasing, fwidth, shader performance, cheap shader effects, GLSL tricks, shader math, shader formulas, Xor shader, GM Shaders, mini.gmshaders.com
ZhangHanDong
research
computational-chemistry
32
pyhealth
Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).
lifangda
research
computational-chemistry
32
datamol
Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery: SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.
lifangda
research
computational-chemistry
32
pyucis
Python API and tools for working with UCIS (Unified Coverage Interoperability Standard) coverage databases. Use when working with hardware verification coverage data, converting coverage formats, merging coverage databases, generating coverage reports, or analyzing functional and code coverage metrics.
fvutils
research
computational-chemistry
32
meta-pattern-recognition
Spot patterns appearing in 3+ domains to find universal principles
fimoklei
research
computational-chemistry
32
unimol
A standardized CLI wrapper for Uni-Mol molecular ML workflows that handles representation extraction (embeddings), model training (regression/classification), and property prediction with built-in RDKit SMILES validation. USE WHEN you need to generate molecular embeddings, train machine learning models for chemical properties, or run predictions on SMILES datasets (.csv/.smi) using the Uni-Mol framework.
jinzhezenggroup
research
computational-chemistry
32
electronic
Prepare ABINIT electronic-analysis task inputs from prior converged context. Use when the user requests post-ground-state electronic analyses and needs prerequisite-aware setup.
jinzhezenggroup
research
computational-chemistry
32
zyfai-rebalancer
"ZK powered rebalancer agent that finds optimal low-risk yield opportunities across Base, Arbitrum, and Plasma chains."
jiayaoqijia
research
computational-chemistry
32
antechamber
A command-line tool in AmberTools for preparing small molecules or non-standard residues within GAFF/AMBER-compatible chemical space for molecular mechanics simulations, by automating atom/bond typing, charge generation or import, and force-field–compatible input generation. USE WHEN you are working in AMBER, dealing with molecules not covered by standard force fields, and already have a structure that can be processed (e.g., pdb, mol2, ac, gout). Typical use cases include parameterizing ligands or modified residues (assigning atom/bond types, generating or reading partial charges), converting structures from upstream tools into mol2/prepi formats, and preparing topology-ready inputs for downstream tools such as LEaP. DO NOT USE for standard residues, metal complexes, inorganic systems, or when no valid molecular structure is available (e.g., only SMILES).
jinzhezenggroup
research
computational-chemistry
32
lammps-deepmd
A tool and knowledge base for running molecular dynamics (MD) simulations in LAMMPS with the DeePMD-kit plugin. It handles input script preparation, ensemble selection (NVE/NVT/NPT), and job execution via `uv` or offline binaries. USE WHEN you need to set up, write, explain, or execute a LAMMPS molecular dynamics simulation using a DeePMD machine learning potential (e.g., `graph.pb`).
jinzhezenggroup
research
computational-chemistry
32
zinc-database
Access ZINC (230M+ purchasable compounds). Search by ZINC ID/SMILES, similarity searches, 3D-ready structures for docking, analog discovery, for virtual screening and drug discovery.
lifangda
research
computational-chemistry
32
fda-database
Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.
lifangda
research
computational-chemistry
32
lammps-reaxff
Run reactive molecular dynamics simulations in LAMMPS with the ReaxFF potential, including preparing input scripts (pair_style reaxff + fix qeq/reaxff), mapping LAMMPS atom types to elements via pair_coeff, choosing ensembles (NVE/NVT/NPT), and adding common ReaxFF diagnostics such as species analysis. Use when the user wants LAMMPS+ReaxFF workflows or needs a working, annotated `input.lammps` template.
jinzhezenggroup
research