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전산 화학

분자 시뮬레이션, 신약 발견, ADME 분석, 화합물 스크리닝을 위한 에이전트 스킬을 탐색하세요.

healthcare-cdss-patterns

Clinical Decision Support System (CDSS) development patterns. Drug interaction checking, dose validation, clinical scoring (NEWS2, qSOFA), alert severity classification, and integration into EMR workflows.

qdrant-vector-search

NousResearch/hermes-agent

High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.

NousResearch/hermes-agent

Pharmaceutical research assistant for drug discovery workflows. Search bioactive compounds on ChEMBL, calculate drug-likeness (Lipinski Ro5, QED, TPSA, synthetic accessibility), look up drug-drug interactions via OpenFDA, interpret ADMET profiles, and assist with lead optimization. Use for medicinal chemistry questions, molecule property analysis, clinical pharmacology, and open-science drug research.

NousResearch/hermes-agent

Search personal knowledge bases, notes, docs, and meeting transcripts locally using qmd — a hybrid retrieval engine with BM25, vector search, and LLM reranking. Supports CLI and MCP integration.

evaluating-llms-harness

NousResearch/hermes-agent

lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).

ruview-advanced-sensing

Advanced RuView capabilities — RuvSense multistatic sensing (attention-weighted fusion, geometric diversity, persistent field model), cross-viewpoint fusion across multiple nodes, RF tomography (ISTA L1 solver, voxel grids), longitudinal biomechanics drift, pre-movement intention signals, adversarial signal detection, and multistatic mesh security hardening. Use for research-grade or multi-node deployments.

ruview-applications

Run RuView sensing applications — presence/occupancy, breathing & heart rate, activity & fall detection, 17-keypoint pose estimation (WiFlow), sleep monitoring & apnea screening, environment mapping, Mass Casualty Assessment (MAT), and the 3D point-cloud fusion demo. Use when someone wants to actually *do* something with a working RuView setup.

Set up and run RuView mmWave / FMCW radar sensing — ESP32-C6 + Seeed MR60BHA2 (60 GHz, heart rate / breathing rate / presence) and HLK-LD2410 (24 GHz, presence + distance), plus mmWave↔WiFi-CSI sensor fusion (48-byte fused vitals, MR60BHA2/LD2410 auto-detect, v0.5.0+). Use when the deployment includes a millimetre-wave radar alongside or instead of WiFi CSI.

nexu-io/open-design

GLSL shader techniques for ray marching, fluid simulation, particle systems, and procedural generation. Useful for hero visuals and motion stills.

cli-anything-unimol-tools

HKUDS/CLI-Anything

Interactive CLI for Uni-Mol molecular property prediction training and inference workflows.

cli-anything-unimol-tools

HKUDS/CLI-Anything

Interactive CLI for Uni-Mol molecular property prediction training and inference workflows.

azure-ai-anomalydetector-java

sickn33/antigravity-awesome-skills

Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.

sickn33/antigravity-awesome-skills

Cirq is Google Quantum AI's open-source framework for designing, simulating, and running quantum circuits on quantum computers and simulators.

options-flow-analyzer

sickn33/antigravity-awesome-skills

Real vs lottery call separation for options P/C ratio analysis — prevents signal inversion from deep OTM noise

superpowers-lab

sickn33/antigravity-awesome-skills

Lab environment for Claude superpowers

sickn33/antigravity-awesome-skills

Psychological compatibility matching — find cofounders, collaborators, and friends through validated psychometrics

subagent-orchestrator

sickn33/antigravity-awesome-skills

Coordinate quota-aware parallel subagents for large, multi-file Antigravity tasks.

attack-tree-construction

wshobson/agents

Build comprehensive attack trees to visualize threat paths. Use when mapping attack scenarios, identifying defense gaps, or communicating security risks to stakeholders.

K-Dense-AI/scientific-agent-skills

How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.

K-Dense-AI/scientific-agent-skills

Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.

K-Dense-AI/scientific-agent-skills

Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including 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.

K-Dense-AI/scientific-agent-skills

Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.

K-Dense-AI/scientific-agent-skills

Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores (CRISPR Chronos), drug sensitivity data, and gene effect profiles. Use for identifying cancer-specific vulnerabilities, synthetic lethal interactions, and validating oncology drug targets.

K-Dense-AI/scientific-agent-skills

DiffDock and DiffDock-L molecular docking. Use for protein-small-molecule pose prediction from PDB or sequence plus SMILES/SDF/MOL2, batch docking, virtual screening, and pose-confidence interpretation. Not for binding affinity prediction.

K-Dense-AI/scientific-agent-skills

Lightweight WSI tile extraction and preprocessing. Use for basic slide processing, tissue detection, tile extraction, and stain normalization for H&E images. Best for simple pipelines, dataset preparation, and quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.

imaging-data-commons

K-Dense-AI/scientific-agent-skills

Query and download public cancer imaging data from NCI Imaging Data Commons using idc-index. Use for accessing large-scale radiology (CT, MR, PET) and pathology datasets for AI training or research. No authentication required. Query by metadata, visualize in browser, check licenses.

K-Dense-AI/scientific-agent-skills

Spectral similarity and compound identification for metabolomics. Use for comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries. Best for metabolite identification, spectral matching, library searching. For full LC-MS/MS proteomics pipelines use pyopenms.

K-Dense-AI/scientific-agent-skills

Medicinal chemistry filters for compound triage. Apply drug-likeness rules (Lipinski, Veber, CNS), structural alert catalogs (PAINS, NIBR, ChEMBL), complexity metrics, and the medchem query language for library filtering.

molecular-dynamics

K-Dense-AI/scientific-agent-skills

Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Set up protein/small molecule systems, define force fields, run energy minimization and production MD, analyze trajectories (RMSD, RMSF, contact maps, free energy surfaces). For structural biology, drug binding, and biophysics.

K-Dense-AI/scientific-agent-skills

Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.

K-Dense-AI/scientific-agent-skills

Complete mass spectrometry analysis platform. Use for proteomics and metabolomics workflows—feature detection, peptide/protein identification, label-free and isobaric quantification, adduct/accurate-mass annotation, and complex LC-MS/MS pipelines. Supports extensive file formats and algorithms. For simple spectral comparison and small-molecule library matching use matchms.

K-Dense-AI/scientific-agent-skills

Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.

K-Dense-AI/scientific-agent-skills

IBM quantum computing framework. Use when targeting IBM Quantum hardware, working with Qiskit Runtime for production workloads, or needing IBM optimization tools. Best for IBM hardware execution, quantum error mitigation, and enterprise quantum computing. For Google hardware use cirq; for gradient-based quantum ML use pennylane; for open quantum system simulations use qutip.

K-Dense-AI/scientific-agent-skills

Quantum physics simulation library for open quantum systems. Use when studying master equations, Lindblad dynamics, decoherence, quantum optics, or cavity QED. Best for physics research, open system dynamics, and educational simulations. NOT for circuit-based quantum computing—use qiskit, cirq, or pennylane for quantum algorithms and hardware execution.

K-Dense-AI/scientific-agent-skills

Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.

K-Dense-AI/scientific-agent-skills

Rowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API. Use for pKa and macropKa prediction, conformer and tautomer ensembles, docking and analogue docking, protein-ligand cofolding, MSA generation, molecular dynamics, permeability, descriptor workflows, and related small-molecule or protein modeling tasks. Ideal for programmatic batch screening, multi-step chemistry pipelines, and workflows that would otherwise require maintaining local HPC/GPU infrastructure.

stable-baselines3

K-Dense-AI/scientific-agent-skills

Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.

prioritize-assumptions

phuryn/pm-skills

Prioritize assumptions using an Impact × Risk matrix and suggest experiments for each. Use when triaging a list of assumptions, deciding what to test first, or applying the assumption prioritization canvas.

assessing-vector-and-embedding-weaknesses

mukul975/Anthropic-Cybersecurity-Skills

Test vector stores for embedding inversion, cross-tenant leakage, and poisoning.

conducting-spearphishing-simulation-campaign

mukul975/Anthropic-Cybersecurity-Skills

Spearphishing simulation is a targeted social engineering attack vector used by red teams to gain initial access. Unlike broad phishing campaigns, spearphishing uses OSINT-derived intelligence to craf

correlating-security-events-in-qradar

mukul975/Anthropic-Cybersecurity-Skills

Correlates security events in IBM QRadar SIEM using AQL (Ariel Query Language), custom rules, building blocks, and offense management to detect multi-stage attacks across network, endpoint, and application log sources. Use when SOC analysts need to investigate QRadar offenses, build correlation rules, or tune detection logic for reducing false positives.

detecting-indirect-prompt-injection

mukul975/Anthropic-Cybersecurity-Skills

Detect and defend against prompt injection hidden in documents, web pages, and images consumed by an agent.

detecting-qr-code-phishing-with-email-security

mukul975/Anthropic-Cybersecurity-Skills

Detect and prevent QR code phishing (quishing) attacks that bypass traditional email security by embedding malicious URLs in QR code images within emails.

detecting-t1003-credential-dumping-with-edr

mukul975/Anthropic-Cybersecurity-Skills

Detect OS credential dumping techniques targeting LSASS memory, SAM database, NTDS.dit, and cached credentials using EDR telemetry, Sysmon process access monitoring, and Windows security event correlation.

detecting-wmi-persistence

mukul975/Anthropic-Cybersecurity-Skills

Detect WMI event subscription persistence by analyzing Sysmon Event IDs 19, 20, and 21 for malicious EventFilter, EventConsumer, and FilterToConsumerBinding creation.

extracting-credentials-from-memory-dump

mukul975/Anthropic-Cybersecurity-Skills

Extract cached credentials, password hashes, Kerberos tickets, and authentication tokens from memory dumps using Volatility and Mimikatz for forensic investigation.

hunting-for-living-off-the-land-binaries

mukul975/Anthropic-Cybersecurity-Skills

Proactively hunt for adversary abuse of legitimate system binaries (LOLBins) to execute malicious payloads while evading detection.

hunting-for-persistence-via-wmi-subscriptions

mukul975/Anthropic-Cybersecurity-Skills

Hunt for adversary persistence through Windows Management Instrumentation event subscriptions by monitoring WMI consumer, filter, and binding creation events that execute malicious code triggered by system events.

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