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Found 1,229 Skills
Docker containerization patterns for Python/React projects. Use when creating or modifying Dockerfiles, optimizing image size, setting up Docker Compose for local development, or hardening container security. Covers multi-stage builds for Python (python:3.12-slim) and React (node:20-alpine -> nginx:alpine), layer optimization, .dockerignore, non-root user, security scanning with Trivy, Docker Compose for dev (backend + frontend + PostgreSQL + Redis), and image tagging strategy. Does NOT cover deployment orchestration (use deployment-pipeline).
Guide for building full-stack web applications using Reflex, a Python framework that compiles to React frontend and FastAPI backend. Use when creating, modifying, or debugging Reflex apps - covers state management, event handlers, components, routing, styling, and data integration patterns.
Creates and maintains dlt (data load tool) pipelines from APIs, databases, and other sources. Use when the user wants to build or debug pipelines; use verified sources (e.g. Salesforce, GitHub, Stripe) or declarative REST API or custom Python; configure destinations (e.g. DuckDB, BigQuery, Snowflake); implement incremental loading; or edit .dlt config and secrets. Use when the user mentions data ingestion, dlt pipeline, dlt init, rest_api_source, incremental load, or pipeline dashboard.
Analyzes code to identify untested functions, low coverage areas, and missing edge cases. Use when reviewing test coverage or planning test improvements. Generates specific test suggestions with example templates following amplihack's testing pyramid (60% unit, 30% integration, 10% E2E). Can use coverage.py for Python projects.
This skill provides comprehensive guidance for SAP Business Application Studio (BAS), the cloud-based IDE on SAP BTP built on Code-OSS. Use when setting up BAS subscriptions, creating dev spaces, connecting to external systems, deploying MTA applications, troubleshooting connectivity issues, managing Git repositories, configuring runtime versions, or using the layout editor. Keywords: SAP Business Application Studio, BAS, SAP BTP, dev space, Cloud Foundry, MTA, multitarget application, SAP Fiori, CAP, HANA, destination, WebIDEEnabled, Cloud Connector, Service Center, Storyboard, Layout Editor, ABAP, OData, subscription, entitlements, role collection, Business_Application_Studio_Developer, Git, clone, push, pull, Gerrit, PAT, OAuth, asdf, runtime, Node.js, Java, Python, Task Explorer, CI/CD, Yeoman, generator, template wizard, mbt, mtar, debugging, breakpoint
SAP HANA Machine Learning Python Client (hana-ml) development skill. Use when: Building ML solutions with SAP HANA's in-database machine learning using Python hana-ml library for PAL/APL algorithms, DataFrame operations, AutoML, model persistence, and visualization. Keywords: hana-ml, SAP HANA, machine learning, PAL, APL, predictive analytics, HANA DataFrame, ConnectionContext, classification, regression, clustering, time series, ARIMA, gradient boosting, AutoML, SHAP, model storage
Multi-language code quality standards and review for TypeScript, Python, Go, and Rust. Enforces type safety, security, performance, and maintainability. Use when writing, reviewing, or refactoring code. Includes review process, checklist, and Python PEP 8 deep-dive.
Use when building MCP servers in TypeScript, Python, or C#; when implementing tools, resources, or prompts; when configuring Streamable HTTP transport; when migrating from SSE; when adding OAuth authentication; when seeing MCP protocol errors
Aspire orchestration for cloud-native distributed applications in any language (C#, Python, Node.js, Go). Handles dependency management, local dev with Docker, Azure deployment, service discovery, and observability dashboards. Use when setting up microservices, containerized apps, or polyglot distributed systems.
A Just-In-Time (JIT) compiler for Python that translates a subset of Python and NumPy code into fast machine code. Developed by Anaconda, Inc. Highly effective for accelerating loops, custom mathematical functions, and complex numerical algorithms. Use for @njit, @vectorize, prange, cuda.jit, numba.typed, JIT compilation, parallel loops, GPU acceleration with CUDA, Monte Carlo simulations, numerical algorithms, and high-performance Python computing.
Best practices for scikit-learn machine learning, model development, evaluation, and deployment in Python
Deep code analysis for pplx-sdk — parse Python AST, build dependency graphs, extract knowledge graphs, detect patterns, and generate actionable insights about code structure, complexity, and relationships. Use when analyzing code quality, mapping dependencies, or building understanding of the codebase.