Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.

Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.

Comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival. Use this skill when working with censored survival data, performing time-to-event analysis, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating survival predictions with concordance index or Brier score, handling competing risks, or implementing any survival analysis workflow with the scikit-survival library.

RNA velocity analysis with scVelo. Estimate cell state transitions from unspliced/spliced mRNA dynamics, infer trajectory directions, compute latent time, and identify driver genes in single-cell RNA-seq data. Complements Scanpy/scVI-tools for trajectory inference.

Deep generative models for single-cell omics. Use when you need probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, or multi-modal integration (TOTALVI, MultiVI). Best for advanced modeling, batch effects, multimodal data. For standard analysis pipelines use scanpy.

Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.

Process-based discrete-event simulation framework in Python. Use this skill when building simulations of systems with processes, queues, resources, and time-based events such as manufacturing systems, service operations, network traffic, logistics, or any system where entities interact with shared resources over time.

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.

Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.

Use this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integrals, limits), manipulating algebraic expressions, working with matrices symbolically, physics calculations, number theory problems, geometry computations, and generating executable code from mathematical expressions. Apply this skill when the user needs exact symbolic results rather than numerical approximations, or when working with mathematical formulas that contain variables and parameters.

Efficient storage and retrieval of genomic variant data using TileDB. Scalable VCF/BCF ingestion, incremental sample addition, compressed storage, parallel queries, and export capabilities for population genomics.

Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.

PyTorch-native graph neural networks for molecules and proteins. Use when building custom GNN architectures for drug discovery, protein modeling, or knowledge graph reasoning. Best for custom model development, protein property prediction, retrosynthesis. For pre-trained models and diverse featurizers use deepchem; for benchmark datasets use pytdc.

This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.

Generate concise (3-4 page), focused medical treatment plans in LaTeX/PDF format for all clinical specialties. Supports general medical treatment, rehabilitation therapy, mental health care, chronic disease management, perioperative care, and pain management. Includes SMART goal frameworks, evidence-based interventions with minimal text citations, regulatory compliance (HIPAA), and professional formatting. Prioritizes brevity and clinical actionability.

UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.

Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). This skill should be used when preparing manuscripts for journal submission, conference papers, research posters, or grant proposals and need venue-specific formatting requirements and templates.

Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.

Grilling session that challenges your plan against the existing domain model, sharpens terminology, and updates documentation (CONTEXT.md, ADRs) inline as decisions crystallise. Use when user wants to stress-test a plan against their project's language and documented decisions.

Triage GitHub issues through a label-based state machine. Use when user wants to create an issue, triage issues, review incoming bugs or feature requests, prepare issues for an AFK agent, or manage issue workflow.

Find deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.

Run an interactive QA session. The user describes problems they're encountering. You clarify, explore the codebase for context, and file GitHub issues that are durable, user-focused, and use the proje

Create a detailed refactor plan with tiny commits via user interview, then file it as a GitHub issue. Use when user wants to plan a refactor, create a refactoring RFC, or break a refactor into safe incremental steps.

Create exercise directory structures with sections, problems, solutions, and explainers that pass linting. Use when user wants to scaffold exercises, create exercise stubs, or set up a new course section.

Extract and formalize domain terminology from the current conversation into a consistent glossary, saved to a local file.

Create new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, or build a new skill.

MCPB is a local MCP server **packaged with its runtime**. The user installs one file; it runs without needing Node, Python, or any toolchain on their machine. It's the sanctioned way to distribute loc

> **Note:** The `.claude/commands/` directory is a legacy format. For new skills, use the `.claude/skills/<name>/SKILL.md` directory format. Both are loaded identically — the only difference is file l

You are guiding a developer through designing and building an MCP server that works seamlessly with Claude. MCP servers come in many forms — picking the wrong shape early causes painful rewrites later

Creates interactive HTML playgrounds — self-contained single-file explorers that let users configure something visually through controls, see a live preview, and copy out a prompt. Use when the user asks to make a playground, explorer, or interactive tool for a topic.

An MCP app is a standard MCP server that **also serves UI resources** — interactive components rendered inline in the chat surface. Build once, runs in Claude *and* ChatGPT and any other host that imp

'Automate React Native Windows integration with upstream React Native nightly versions. Use when: upgrading RNW to a newer React Native nightly, finding target nightly versions, preparing integration PRs, updating package dependencies.'

KubeSphere central controller Skill. Routes to specific Skills based on user requests: multi-cluster management (kubesphere-cluster-management), multi-tenant management (kubesphere-multi-tenant-management), extension management (kubesphere-extension-management). Also provides core architecture, API routing, and API utilities.

Use when configuring ArgoCD in KubeSphere DevOps, including GitOps deployments, application management, SSO setup, or troubleshooting ArgoCD issues

Use when managing credentials in KubeSphere DevOps, including repository credentials, kubeconfig, and API tokens

Use when configuring Jenkins in KubeSphere DevOps, including agent customization, LDAP/OIDC integration, build artifact retrieval, or troubleshooting Jenkins issues

Use when working with KubeSphere DevOps extension, CI/CD pipelines, Jenkins integration, or pipeline troubleshooting

Use when creating, running, or managing CI/CD pipelines in KubeSphere DevOps, including pipeline API operations and run monitoring

Use when operating KubeSphere DevOps as a namespace-scoped tenant with limited permissions, without cluster-admin access, or when accessing DevOps through KubeSphere APIs only

KubeSphere extension management Skill. Use when user requests to install, configure, upgrade, uninstall extensions, or query extension info/troubleshoot issues. Includes extension discovery, dependency management, install configuration, version management.

KubeSphere multi-tenant management Skill. Use when user requests to create users, workspaces, projects, or assign roles/permissions. Supports user lifecycle management, workspace configuration, project creation, role binding. Do not perform any delete operations, do not create custom roles.

KubeSphere OpenKruise management Skill. Use when user asks to install or enable OpenKruise, check OpenKruise status, view kruise pods/logs/CRDs, create or update SidecarSet, manage sidecar injection, create or update CloneSet, perform in-place update or batch rollout, uninstall or remove OpenKruise, or troubleshoot Kruise Pod, CRD, and webhook issues in KubeSphere.

KubeSphere Volcano job management Skill. Use when user asks to create, list, update, delete Jobs (Volcano Jobs), manage Queues, create PyTorch/TensorFlow/MPI training jobs, or troubleshoot Volcano scheduling issues in KubeSphere. Includes built-in YAML templates, scheduling policy recommendations, and best practices for resource configuration. Handles both KubeSphere API and kubectl operations.

Use when installing or configuring the WizTelemetry Data Pipeline (vector) extension for KubeSphere, which provides data collection, transformation, and routing for observability data including logs, auditing, events, and notifications

Use when working with WizTelemetry Logging extension for KubeSphere, including installation, configuration, and log query API

kOps is a command-line tool for creating, destroying, upgrading, and maintaining production-grade, highly available Kubernetes clusters. It is written in Go and supports multiple cloud providers, incl

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Text/image generation via Gemini Web API. Supports reference images and multi-turn conversations.

Posts text, images, videos, and long-form articles to X via real Chrome browser (bypasses anti-bot detection).