「jax」の検索結果

Use DreamerV3 for world-model reinforcement learning training, embodied environment dataflow, JAX model internals, result operations, and troubleshooting.

日本語の概要は準備中です。原文の説明を表示しています。

Operating skill for the Equinox JAX neural-network and PyTree library.

日本語の概要は準備中です。原文の説明を表示しています。

Use ColabFold for protein structure prediction workflows: validate FASTA/CSV/A3M inputs, generate MSAs, plan batch predictions, inspect outputs, and troubleshoot optional AlphaFold, MMseqs2, JAX, GPU, and OpenMM dependencies.

日本語の概要は準備中です。原文の説明を表示しています。

Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.

日本語の概要は準備中です。原文の説明を表示しています。

WordPress performance code review and optimization analysis. Use when reviewing WordPress PHP code for performance issues, auditing themes/plugins for scalability, optimizing WP_Query, analyzing caching strategies, checking code before launch, or detecting anti-patterns, or when user mentions "performance review", "optimization audit", "slow WordPress", "slow queries", "high-traffic", "scale WordPress", "code review", "timeout", "500 error", "out of memory", or "site won't load". Detects anti-patterns in database queries, hooks, object caching, AJAX, and template loading.

日本語の概要は準備中です。原文の説明を表示しています。

Hotwire Turbo for Symfony UX -- SPA-like speed with zero JavaScript. Covers Drive (navigation), Frames (partial page sections), and Streams (multi-target updates). Use when building ajax navigation, lazy-loaded sections, inline editing, pagination without reload, modals from the server, flash messages via streams, or real-time updates via Mercure/SSE. Code triggers: turbo-frame, turbo-stream, data-turbo-frame, data-turbo, data-turbo-action, turbo-stream-source, TurboStreamResponse, <twig:Turbo:Frame>, <twig:Turbo:Stream:Append>, <twig:Turbo:Stream:Replace>, turbo:before-fetch-request. Also trigger when the user asks "how to update part of the page without reload", "how to make navigation feel like SPA", "how to lazy-load a section", "how to do inline editing", "how to push real-time updates from server", "how to use Mercure with Turbo". Do NOT trigger for client-side JS behavior (use stimulus), server-rendered reactive components (use live-component), or reusable static UI (use twig-component).

日本語の概要は準備中です。原文の説明を表示しています。

Symfony UX LiveComponent for reactive server-rendered UI -- components that re-render via AJAX on user interaction, zero JavaScript required. Use when building live search, real-time filtering, dynamic forms, inline validation, dependent selects, auto-save, polling, deferred/lazy rendering, or any UI that updates itself based on user input. Code triggers: AsLiveComponent, LiveProp, LiveAction, data-model, data-loading, data-live-action-url, ComponentWithFormTrait, LiveListener, emit, defer, lazy, polling. Also trigger when the user asks "how to build a search that filters as I type", "how to validate a form in real-time", "how to make a reactive component in PHP", "how to build dependent selects", "how to defer component rendering", "how to communicate between components via emit", "how to bind a form to a LiveComponent". Do NOT trigger for static reusable UI without reactivity (use twig-component), pure client-side JS behavior (use stimulus), or page-level navigation (use turbo).

日本語の概要は準備中です。原文の説明を表示しています。

Use when building or extending a Java service with Quarkus - layered architecture, JAX-RS resources, CDI beans, Panache persistence, and native-friendly patterns

日本語の概要は準備中です。原文の説明を表示しています。

基于项目描述与仓库证据生成目标简历、完整核心源码课程与项目面经，含学习路线和第一人称回答。 适用于项目分析、面试准备、简历亮点和导学材料。

日本語の概要は準備中です。原文の説明を表示しています。

MLX↔JAX bidirectional beacon-repeater with spectral gap random walk, justfile orchestration, catp flow validation, and adversarial malleability for life pattern resurrection.

日本語の概要は準備中です。原文の説明を表示しています。

Bridge active inference theory with robot control using K-Scale's JAX/MuJoCo stack. Use when connecting predictive coding to locomotion policies, mapping KL divergence minimization to RL training, applying mean field approximation to robotics state estimation, or implementing sim2real as inference about future observations.

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user says 'why is my checkout broken', 'audit my woocommerce store', 'cart problems woocommerce', or 'losing sales woocommerce'. Diagnoses checkout and cart failures, AJAX mismatches, caching conflicts, payment gateway setup, and SSL enforcement.

日本語の概要は準備中です。原文の説明を表示しています。

Use when building dynamic Django web apps with htmx - partial rendering, HTMX responses, querystring tag, CSP

日本語の概要は準備中です。原文の説明を表示しています。

Instrument browser/web apps with the Application Insights JavaScript SDK (@microsoft/applicationinsights-web). Use for Real User Monitoring (RUM) — page views, clicks, AJAX/fetch dependencies, exceptions, custom events...

日本語の概要は準備中です。原文の説明を表示しています。

Analyzes UEFI bootkit persistence mechanisms including firmware implants in SPI flash, EFI System Partition (ESP) modifications, Secure Boot bypass techniques, and UEFI variable manipulation. Covers detection of known bootkit families (BlackLotus, LoJax, MosaicRegressor, MoonBounce, CosmicStrand), ESP partition forensic inspection, chipsec-based firmware integrity verification, and Secure Boot configuration auditing. Activates for requests involving UEFI malware analysis, firmware persistence investigation, boot chain integrity verification, or Secure Boot bypass detection.

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Builds a WooCommerce-style AJAX product search select (the selectWoo / wooselect dropdown) — class="wc-product-search" + the data-action attribute pointing to woocommerce_json_search_products (products only) or woocommerce_json_search_products_and_variations (products AND variations, often the right choice). Pre-selected items rendered server-side as <option selected> via wc_get_product() + get_formatted_name(); WC's wc-enhanced-select script auto-enqueued on WC admin screens, explicit enqueue required on non-WC pages. Solves the "load 20k products into a select" antipattern AI assistants commonly emit. Use when adding a product picker meta box, a custom WC admin page selector, or any UI where the user must search and pick from many products. Triggers on wc-product-search, woocommerce_json_search_products, woocommerce_json_search_products_and_variations, selectWoo, wc-enhanced-select, "select2 products in WooCommerce", or product-picker meta box scaffolding.

日本語の概要は準備中です。原文の説明を表示しています。

Use WooCommerce's customer and session APIs correctly — WC ships its own session handler (WC_Session_Handler, single wp_woocommerce_sessions table for both guests and logged-in users) and its own customer object (WC_Customer via WC()->customer), separate from $_SESSION / native PHP sessions / wp_get_current_user. AI consistently reaches for $_SESSION (breaks with page caching, setcookie (no signing, no namespacing), or user_meta for guests (impossible). The correct path is WC()->session->set/get for ephemeral per-visitor data, WC()->customer->get_billing_* for the active customer context including guests, and new WC_Customer($user_id) for one-off loads. Important — WC()->session is auto-initialized only on frontend requests (incl. admin-ajax); REST, cron, and WP-CLI must call wc_load_cart() explicitly. Use when storing per-visitor state, reading the current visitor's billing data, or debugging session loss across page caches / REST / cron. Triggers on WC_Session, WC_Session_Handler,...

日本語の概要は準備中です。原文の説明を表示しています。

Serverless form handling on Netlify-hosted sites — detects HTML forms at deploy time, stores submissions, filters spam, and sends notifications. Use when adding a contact form, lead-capture form, file-upload form, or newsletter signup to a Netlify site; wiring AJAX form submission; setting up a custom thank-you page; adding a honeypot or reCAPTCHA to a form; getting forms working in Next.js, Nuxt, SvelteKit, Astro, or Gatsby; reading form submissions via the Netlify API; or debugging missing submissions and forms that silently fail to register.

日本語の概要は準備中です。原文の説明を表示しています。

Machine learning development with JAX, functional programming patterns, and high-performance computing.

日本語の概要は準備中です。原文の説明を表示しています。

Analyzes UEFI bootkit persistence mechanisms including firmware implants in SPI flash, EFI System Partition (ESP) modifications, Secure Boot bypass techniques, and UEFI variable manipulation. Covers detection of known bootkit families (BlackLotus, LoJax, MosaicRegressor, MoonBounce, CosmicStrand), ESP partition forensic inspection, chipsec-based firmware integrity verification, and Secure Boot configuration auditing. Activates for requests involving UEFI malware analysis, firmware persistence investigation, boot chain integrity verification, or Secure Boot bypass detection.

日本語の概要は準備中です。原文の説明を表示しています。

Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with PyTorch/JAX/TensorFlow. For hardware-specific optimizations use qiskit (IBM) or cirq (Google); for open quantum systems use qutip.

日本語の概要は準備中です。原文の説明を表示しています。

This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.

日本語の概要は準備中です。原文の説明を表示しています。

Patterns for loading PDE simulation datasets (PDEBench, PhiFlow, JAX-CFD) from HDF5 files. Handles layout detection (single tensor vs separate variables), spatial/temporal downsampling, multi-variable systems, HuggingFace and DaRUS data sources, and efficient PyTorch DataLoader creation. Use when preparing PDE data for neural operator training.

日本語の概要は準備中です。原文の説明を表示しています。

Use when you need to design, review, or improve validation in Quarkus applications — including Bean Validation on JAX-RS resources, @Valid on parameters and CDI beans, constraint groups, @ConfigMapping validation, custom constraints, nested DTO validation, and ExceptionMapper-based error mapping. This should trigger for requests such as Add validation support in Quarkus; Review Quarkus validation rules; Improve request validation in Quarkus REST APIs; Add custom validation constraints in Quarkus; Validate Quarkus @ConfigMapping properties. Part of cursor-rules-java project

日本語の概要は準備中です。原文の説明を表示しています。