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Found 2,728 Skills
Read and write large cuPyNumeric arrays to HDF5 with Legate's parallel, distributed HDF5 I/O (legate.io.hdf5: to_file, from_file, from_file_batched). Use when a developer needs to save a cuPyNumeric array to an .h5/.hdf5 file, load an HDF5 dataset into a distributed cuPyNumeric array, read a large HDF5 dataset in chunks, hand arrays to an HPC pipeline as a single file, or accelerate HDF5 disk I/O with GPUDirect Storage (GDS). Do not use it for Parquet/cuDF/raw-binary or other sharded/custom layouts (see the cupynumeric-parallel-data-load skill), Zarr or object-store/S3 output, .npz or pickled archives, plain h5py without cuPyNumeric, or pure array compute such as FFT, matmul, or reductions.
Design Amazon product bundles and multipacks that lift average order value and margin. Covers virtual bundles, physical bundles, multipacks, the right pairing logic, bundle pricing, and listing setup. Use when a user asks about bundling products, virtual bundles, multipacks, what to bundle, bundle pricing, raising average order value, or creating a set. Trigger phrases: "product bundle", "virtual bundle", "multipack", "what should I bundle", "bundle pricing", "raise AOV", "create a set". Works with zero tools.
Ruzzy is a coverage-guided Ruby fuzzer by Trail of Bits. Use for fuzzing pure Ruby code and Ruby C extensions.
Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when: keywords, file_patterns, code_patterns.
Run LLMs and AI models on Cloudflare's GPU network with Workers AI. Includes Llama 4, Gemma 3, Mistral 3.1, Flux images, BGE embeddings, streaming, and AI Gateway. Handles 2025 breaking changes. Prevents 7 documented errors. Use when: implementing LLM inference, images, RAG, or troubleshooting AI_ERROR, rate limits, max_tokens, BGE pooling, context window, neuron billing, Miniflare AI binding, NSFW filter, num_steps.
Guide for writing GraphQL operations (queries, mutations, fragments) following best practices. Use this skill when: (1) writing GraphQL queries or mutations, (2) organizing operations with fragments, (3) optimizing data fetching patterns, (4) setting up type generation or linting, (5) reviewing operations for efficiency.
Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.
Add Vitest testing infrastructure and GitHub Actions CI/CD to any TypeScript project. Supports Next.js, NestJS, and React projects with 80% coverage thresholds. Use this skill when setting up tests for a new project or adding CI/CD pipelines.
Configure LangChain4J vector stores for RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/pgvector, Pinecone, MongoDB, Milvus, Neo4j), implementing embedding storage/retrieval, setting up hybrid search, or optimizing vector database performance for production AI applications.
Expert quality gate decisions for iOS/tvOS: which gates matter for your project size, threshold calibration that catches bugs without blocking velocity, SwiftLint rule selection, and CI integration patterns. Use when setting up linting, configuring CI pipelines, or calibrating coverage thresholds. Trigger keywords: SwiftLint, SwiftFormat, coverage, CI, quality gate, lint, static analysis, pre-commit, threshold, warning
Expert in managing the "Memory" of AI systems. Specializes in Vector Databases (RAG), Short/Long-term memory architectures, and Context Window optimization. Use when designing AI memory systems, optimizing context usage, or implementing conversation history management.
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.