「high performance」の検索結果

Expert blueprint for physics queries using RayCast, ShapeCast, and DirectSpaceState. Covers hit detection, volume overlap, mouse picking, and high-performance server-side intersection queries. Use when implementing projectiles, LOS, terrain grounding, or AI sensors. Keywords raycast, shapecast, direct_space_state, intersect_ray, intersect_shape, PhysicsRayQueryParameters, collision mask, mouse picking.

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Build high-performance declarative UI with Jetpack Compose. Use when writing Composable functions, optimizing recomposition, hoisting state, or working with LazyColumn and side effects; defer deep-link and navigation routing to android-navigation.

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ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.

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Optimizing vector embeddings for RAG systems through model selection, chunking strategies, caching, and performance tuning. Use when building semantic search, RAG pipelines, or document retrieval systems that require cost-effective, high-quality embeddings.

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Invoke when the user asks to review, check, audit, or look over Qt6 C++ code — or suggest before committing. Runs deterministic linting (60+ rules) then six parallel deep- analysis agents covering model contracts, ownership, threading, API correctness, error handling, and performance. Reports only high-confidence issues (>80/100) with structured mitigations. Read-only — never modifies code.

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Invoke when the user asks to review, check, audit, or look over Qt6 QML code -- or suggest before committing. Runs deterministic linting (47+ rules) then six parallel deep- analysis agents covering bindings, layout, loaders, delegates, states, and performance. Optionally invokes system qmllint for type-level checks. Reports only high-confidence issues (>80/100) with structured mitigations. Read-only -- never modifies code.

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TimescaleDB - PostgreSQL extension for high-performance time-series and event data analytics, hypertables, continuous aggregates, compression, and real-time analytics

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Configure secure, high-performance connectivity between on-premises infrastructure and cloud platforms using VPN and dedicated connections. Use when building hybrid cloud architectures, connecting data centers to cloud, or implementing secure cross-premises networking.

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High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

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Master Python asyncio, concurrent programming, and async/await patterns for high-performance applications. Use when building async APIs, concurrent systems, or I/O-bound applications requiring non-blocking operations.

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Postgres best practices maintained by Supabase, for Postgres running anywhere. Load this skill BEFORE writing or changing anything that lives in a Postgres database: creating or altering tables and columns (including choosing column types), schema design, migrations and declarative schema files, RLS policies and the tests that verify them, indexes, triggers, database functions, queues and scheduled jobs (pg_cron, pgmq), vector/semantic search (pgvector), and restoring dumps (pg_restore) or importing data. Also load it when diagnosing slow queries, high CPU, timeouts, EXPLAIN plans, connection exhaustion, locking, bloat, or rows visible to the wrong user or tenant. This is not just a performance guide — schema, migration, security, and SQL authoring tasks need these rules too, even for a one-column change or a single query.

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Rust-backed Python library for fast genomic token arithmetic and BED processing. High-performance BED I/O, interval set ops (intersect, merge, complement, subtract), region tokenization against a universe, universe construction. Use for preprocessing large BED collections and ML token vocabularies.

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Use face.evoLVe for high-performance face recognition workflows across alignment, data preparation, PyTorch training, feature verification, and Paddle deployment.

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Comprehensive guidance for implementing asynchronous Python applications using asyncio, concurrent programming patterns, and async/await for building high-performance, non-blocking systems.

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Advanced JavaScript animation library skill for creating complex, high-performance web animations.

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Modern Zig project architecture guide. Use when creating Zig projects (systems programming, CLI tools, game dev, high-performance services). Covers explicit allocators, comptime, error handling, and build system.

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Diagnose slow or freezing VS Code-compatible editors with evidence-first, zero-hardcoded-assumption workflow. Use when the user reports editor lag, typing delay, UI freezes, extension host stalls, file watcher noise, high editor CPU/RSS, uses VS Code/Cursor as a file browser over a large folder, or wants a safe editor performance audit.

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Build institutional-grade comparable company analyses with operating metrics, valuation multiples, and statistical benchmarking in Excel/spreadsheet format. **Perfect for:** - Public company valuation (M&A, investment analysis) - Benchmarking performance vs. industry peers - Pricing IPOs or funding rounds - Identifying valuation outliers (over/under-valued) - Supporting investment committee presentations - Creating sector overview reports **Not ideal for:** - Private companies without comparable public peers - Highly diversified conglomerates - Distressed/bankrupt companies - Pre-revenue startups - Companies with unique business models

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Deploy AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded Coder). Covers two workflow patterns: (1) MathWorks-native or 3P-imported models rebuilt as dlnetwork for lean hardware (Cortex-M, DSP), (2) direct C/C++ code generation from PyTorch and LiteRT models for high-performance hardware (Cortex-A, x86, GPU). Trigger when: user wants to deploy AI to embedded targets; generate C/CUDA from neural networks; compress AI models for MCU/DSP; integrate AI in Simulink for system-level simulation; import PyTorch/ONNX/TensorFlow models for embedded deployment; optimize AI for resource-constrained hardware; or use loadPyTorchExportedProgram, importNetworkFromPyTorch, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models.

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Configures Envoy, the high-performance C++ edge and service proxy, as an API gateway, load balancer or service mesh sidecar with L4/L7 routing, retries, circuit breaking, rate limiting, TLS and observability. Use when a user asks to set up Envoy, write an envoy.yaml, route traffic to backends, add health checks or rate limits, do a canary split, read Envoy admin stats, or understand the proxy behind Istio and Envoy Gateway.

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Build high-performance APIs with Elysia on Bun. Use when someone asks to "create an API with Bun", "build a REST API with Elysia", "set up WebSocket server on Bun", "type-safe API with Eden Treaty", "Elysia plugin", or "fastest TypeScript HTTP framework". Covers REST routes, WebSocket, validation with TypeBox, Eden Treaty type-safe client, plugins, auth guards, and OpenAPI/Swagger generation.

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Expert guidance for Apache Arrow, the cross-language columnar memory format for analytics workloads. Helps developers use Arrow for high-performance data interchange between systems, zero-copy reads, and efficient columnar processing in Python (PyArrow) and JavaScript (Arrow JS).

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GraphQL gives clients exactly the data they need - no more, no less. One endpoint, typed schema, introspection. But the flexibility that makes it powerful also makes it dangerous. Without proper controls, clients can craft queries that bring down your server. This skill covers schema design, resolvers, DataLoader for N+1 prevention, federation for microservices, and client integration with Apollo/urql. Key insight: GraphQL is a contract. The schema is the API documentation. Design it carefully. 2025 lesson: GraphQL isn't always the answer. For simple CRUD, REST is simpler. For high-performance public APIs, REST with caching wins. Use GraphQL when you have complex data relationships and diverse client needs. Use when "graphql, graphql schema, graphql resolver, apollo server, apollo client, graphql federation, dataloader, graphql codegen, graphql query, graphql mutation, graphql, api, apollo, schema, resolvers, dataloader, federation, typescript" mentioned.

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Async Python Patterns workflow skill. Use this skill when the user needs Comprehensive guidance for implementing asynchronous Python applications using asyncio, concurrent programming patterns, and async/await for building high-performance, non-blocking systems and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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