「callbacks」の検索結果

Fix Riverpod "Cannot use Ref or modify other providers inside life-cycles/selectors" crash in @riverpod provider bodies. Use when: (1) Crash during provider disposal with this exact error message, (2) Using keepAlive() with timer-based disposal, (3) Async callbacks (.then, .catchError) try to use ref.read() or ref.invalidateSelf(), (4) Error occurs after ref.onCancel/onResume/onDispose callbacks fire. Solution: wrap ref operations in try-catch or avoid using ref in async callbacks entirely.

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Register a custom Listings callback for JetEngine — the per-field transform fired when a Dynamic Field widget renders meta values (Format date, Format number, Get post title, Convert units, …). Two registration paths are supported. Legacy 3-filter via jet-engine/listings/allowed-callbacks (label), jet-engine/listings/allowed-callbacks-args (control schema), and jet-engine/listing/dynamic-field/callback-args (positional args at runtime). Modern single-call $manager->register_callback($name, $label, $args) hooked on jet-engine/callbacks/register fires the three filters internally for you. Critical contract — the callback identifier you register MUST be a real PHP callable string (a global function name OR `Fully\\Qualified\\Class::method`). Bare static method names like `'unit_converter'` fail JE's is_callable() gate at apply_callback() and the rendered field silently becomes empty. Use when scaffolding a JetEngine companion plugin's listing-field transform, when seeing "callback appl...

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High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.

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High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.

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Authors and edits AWS Step Functions state machines: writes Amazon States Language (ASL) in JSONata, and chooses and structures state types (Task, Choice, Map, Parallel, Pass, Wait, Succeed, Fail). Covers ASL syntax, JSONata data transformation and variables, Retry/Catch error handling, service integrations (.sync, waitForTaskToken callbacks), Distributed Map for large-scale S3/CSV processing, saga/compensation patterns, Standard vs Express workflow choice, TestState API unit testing, and migrating state machines from JSONPath to JSONata. Use when the user is building, authoring, debugging, or migrating a Step Functions state machine or ASL definition, or orchestrating multi-step workflows with branching, retries, or human-approval callbacks, even if they don't say 'Step Functions.' Do NOT use for general Lambda function code, API Gateway, EventBridge wiring, or SAM/CDK application packaging.

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Implement event-driven patterns with Claude API: streaming SSE events, Message Batches callbacks, and async processing architectures. Use when building real-time Claude integrations or processing batch results. Trigger with phrases like "anthropic events", "claude streaming events", "anthropic async processing", "claude batch callbacks".

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Implement and verify callbacks only from an approved tenant-specific Abridge event contract, with replay and PHI protections. Use when vendor implementation documents authorize event delivery. Trigger with "build the Abridge event handler".

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Detects command-and-control beacons in network logs by analyzing connection timing: measuring inter-arrival intervals, accounting for jitter, and scoring regularity per source/destination pair to surface periodic callbacks. Activates for requests to hunt C2 beaconing, find periodic callbacks, or analyze connection-interval regularity.

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Framework PyTorch de alto nível com classe Trainer, treinamento distribuído automático (DDP/FSDP/DeepSpeed), sistema de callbacks e boilerplate mínimo. Escala de laptop para supercomputador com o mesmo código. Use quando quiser loops de treinamento limpos com boas práticas integradas.

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High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.

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Upstash サーバーレスデータプラットフォームリファレンス。 @upstash/redis — REST API、pipelining、transactions、JSON、Search、グローバルレプリケーション。 @upstash/ratelimit — Fixed Window、Sliding Window、Token Bucket、limit、blockUntilReady。 QStash — publishJSON、schedules、queues、DLQ、URL Groups、callbacks、flow-control。 @upstash/vector — upsert、query、ANN、hybrid index、sparse index、embedding models、namespace。 Rust 製ローカル vector DB エンジン fandhe-db とは別（こちらはマネージド SaaS の JS SDK）。 @upstash/workflow — durable execution、serve、context.run/sleep/call/invoke、waitForEvent、parallel steps、agents。 Upstash Search — search、upsert、fetch、range、filtering、reranking、algorithm、@upstash/search。 Upstash Box — サンドボックス、agent、filesystem、git、browser（CDP/AI actions/recordings）、network policy、snapshots、schedules。

High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.

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ローカルで動く開発サーバーやWebhook受信先を、SSH経由でインターネットに公開するスキル。専用ソフトの追加なしで、共有用URLの取得やアクセス制限を行えます。

• 実行中の開発サーバーを共有したいとき
• Webhookをローカルで受信したいとき
• ローカルのHTTP型MCPサーバーへの外部接続

Trains and evaluates single-agent reinforcement learning with Stable Baselines3 (PPO, SAC, DQN, TD3, DDPG, A2C), Gymnasium custom environments, vectorized rollouts, callbacks, and checkpoint normalization. Applies to reproducible RL experiments, continuous control, discrete actions, and SB3-Contrib recurrent or masked policies.

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Deep learning framework (PyTorch Lightning / lightning package). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard, MLflow), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.

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Performs statistical analysis of Zeek conn.log connection intervals to detect C2 beaconing patterns. Uses the ZAT library to load Zeek logs into Pandas DataFrames, calculates inter-arrival time standard deviation, and flags periodic connections with low jitter. Use when hunting for command-and-control callbacks in network data.

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Zoom Virtual Agent Android integration via WebView. Use for Java/Kotlin bridge callbacks, native URL handling, support_handoff relay, and lifecycle-safe embedding.

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Reference skill for Zoom Contact Center. Use after routing to a contact-center workflow when implementing app, web, or native integrations; engagement context and state handling; campaigns; callbacks; or version-drift troubleshooting.

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Use when writing or reviewing asynchronous C++ networking code with Boost.Asio or standalone Asio — TCP/UDP servers and clients, SSL/TLS, timers, strands, io_context, co_spawn, awaitable, async_read/async_write, asio::spawn, yield_context, or pre-C++20 completion-handler callbacks.

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Deep-dive offensive methodology for dependency confusion and namespace attacks across all major package ecosystems. Covers npm scope confusion exploiting the gap between public and private scoped packages and .npmrc misconfigurations where registry mappings fail to pin internal scopes exclusively. Addresses PyPI namespace attacks through --extra-index-url resolution ordering, NuGet feed priority exploitation when multiple package sources are configured without clear directives, Maven and Gradle repository ordering where artifact resolution traverses repositories sequentially, Go module proxy abuse through GOPROXY misconfiguration, Ruby gems namespace squatting, and Docker image tag confusion with unqualified image references. Provides complete proof-of-concept methodology using safe callbacks including DNS canary via interactsh or Burp Collaborator and HTTP beacon with no destructive payload. Covers reconnaissance techniques for discovering internal package names through GitHub repository analysis, error message harvesting, JavaScript source map extraction, lock file parsing, job postings mentioning internal tools, and package manifest inspection. Directly references and builds upon Alex Birsan's seminal 2021 dependency confusion research. Each ecosystem section includes registry-specific exploitation mechanics, configuration vulnerabilities, and defensive countermeasures for engagement reporting.

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Review Apache Pinot concurrency and state safety when locks, shared callbacks, lifecycle, or distributed metadata change.

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This skill should be used when the user wants to "write agent code", "build an agent with ADK", "add a tool", "create a callback", "define an agent", "use state management" — in a project that needs ADK (Agent Development Kit) API patterns and code examples. It provides a quick reference for agent types, tool definitions, orchestration patterns, callbacks, state management, the graph Workflow API, and reference recipes to study. Do NOT use for scaffolding (use google-agents-cli-scaffold) or deployment (use google-agents-cli-deploy).

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Implement and review React, React Native, and TypeScript UI for maximum practical render performance through leaf-local updates, stable identities, honest Hook semantics, and minimal effects. Use for components, hooks, callbacks, context, external stores, memoization, list rows, large dependency arrays, callback or state fanout, re-render reduction, and React Compiler-aware performance work.

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Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network 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。