「stateful」の検索結果

Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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。

Architecture and systems design for building always-on AI agents with episodic memory. Covers the memory hierarchy (core/recall/archival), persistence layers, agent server infrastructure, vector stores, and framework selection. Provides concrete deployment patterns for agents that maintain identity and learn across sessions. Activate on: "always-on agent", "persistent agent architecture", "episodic memory system", "agent memory design", "long-running agent", "stateful agent", "agent that remembers", "MemGPT architecture", "Letta deployment", "/always-on-agent-architecture". NOT for: choosing what data to feed the agent (use always-on-agent-inputs), brainstorming applications (use always-on-agent-applications), safety and privacy concerns (use always-on-agent-safety), general agentic patterns (use agentic-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。

Expert em LangGraph - o framework de nível produção para construir aplicações de IA com múltiplos atores e estado. Aborda construção de grafos, gerenciamento de estado, ciclos e ramificações, persistência com checkpointers, padrões human-in-the-loop e o padrão ReAct. Usado em produção no LinkedIn, Uber e 400+ empresas. Esta é a abordagem recomendada pelo LangChain para construir agentes. Use quando: langgraph, langchain agent, stateful agent, agent graph, react agent.

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。

Apply when writing stateful logic, or when code branches a lot or repeats a shape assumption across files. Encode the domain in a structure instead of scattered conditionals.

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。

Foundation for writing Estuary derivations — what they are, language choice (SQL/TypeScript/Python), project layout, flowctl workflow, stateless vs stateful shuffling, and universal gotchas. Read this before any other derivation-* skill. Use when user says "write a derivation", "how do derivations work", "create a derivation", "derivation setup", or before starting any transformation work.

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。

Implements declarative Flutter routing using GoRouter v17 with URL-based navigation, deep linking, and authentication guards. Activates when configuring GoRoute path patterns, setting up StatefulShellRoute for persistent bottom navigation state, implementing ShellRoute for shared nested layouts, adding redirect guards with loop prevention (idempotent redirects), configuring deep links for iOS (FlutterDeepLinkingEnabled) or Android (intent-filters), debugging redirect loops or navigation stack issues, handling 404 error routes with errorBuilder, using named routes with path/query/extra parameters, or migrating from imperative Navigator.push to declarative routing. Ideal for Flutter web apps requiring URL bar synchronization, mobile apps with universal links or app links, or multi-level navigation hierarchies.

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AI-driven Flutter E2E testing via VM Service CLI. Control running Flutter apps in debug mode for smoke tests, regression checks, exploratory testing, and UI automation. Trigger when user mentions E2E testing, UI automation, smoke tests, integration testing, test automation, controlling Flutter apps, VM Service interaction, debugging UI flows, automated regression testing, CI sanity checks, or testing native dialogs/permissions/WebViews. Supports stateful named instances or stateless direct URI connections. Ideal for rapid test feedback, AI agent workflows, element discovery, screenshot capture, log collection, and scriptable UI interactions (tap, scroll, enterText). Use for troubleshooting flaky tests, automating repetitive manual tests, verifying app behavior after merges, or setting up automated QA pipelines.

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。

Provider (v6.x) state management for Flutter using ChangeNotifier and InheritedWidget with MVVM architecture pattern. Use this skill when implementing Provider-based state management, using Consumer/Consumer2 widgets for targeted rebuilds, accessing state with context.read/context.watch/context.select, setting up ChangeNotifierProvider or StateNotifierProvider, configuring MultiProvider for multiple providers, implementing ProxyProvider for dependent state, using Selector for optimized rebuilds, migrating from StatefulWidget to reactive state, maintaining legacy Provider codebases, debugging provider not found errors, preventing memory leaks with proper ChangeNotifier disposal, fixing context access issues in initState, or resolving .value constructor anti-patterns. Essential for apps with shared state across widgets (shopping carts, authentication, global settings, form state, theme management), when teaching state management fundamentals, or when debugging memory leaks and resource cleanup in Provider-based applications.

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。

Bu Flutter template'inde uctan uca yeni feature iskelesi kurar — lib/feature/<ad>/ klasor yapisi, Freezed state + Cubit, StatelessWidget ya da StatefulWidget+ViewModel view, BlocProvider yerlesimi (global state_initialize mi lokal mi), TypedGoRoute, LocaleKeys cevirileri, AppPaddings/textTheme/colorScheme token'lari ve zorunlu <feature>.md modul dokumani. Kullanici "yeni feature ekleyelim", "su sayfayi projeye gore yaz", "cubit + state + view kur", "feature iskeleti cikar", "scaffold a feature" dediginde kullan.

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。

Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern.

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。

Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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。

Uses Microsoft RESTler to perform stateful REST API fuzzing by automatically generating and executing test sequences that exercise API endpoints, discover producer-consumer dependencies between requests, and Bul: security and reliability bugs. The tester compiles an OpenAPI specification into a RESTler fuzzing grammar, configures authentication, runs test/fuzz-lean/fuzz modes, and analyzes results for 500 errors, authentication bypasses, resource leaks, and payload injection vulnerabilities. ...

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 a live Jupyter kernel for stateful, iterative Python execution via hamelnb. Load this skill when the task involves exploration, iteration, or inspecting intermediate results — data science, ML experimentation, API exploration, or building up complex code step-by-step. Uses terminal to run CLI commands against a live Jupyter kernel. No new tools required.

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。

Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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。

Create and review Cloudflare Durable Objects. Use when building stateful coordination (chat rooms, multiplayer games, booking systems), implementing RPC methods, SQLite storage, alarms, WebSockets, or reviewing DO code for best practices. Covers Workers integration, wrangler config, and testing with Vitest. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.

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。

Architecture and systems design for agents that retain bounded state across sessions. Covers durable event history, owned state, derived memory, retrieval indexes, framework evaluation, recovery, deletion, and rebuild behavior. Uses constructed implementation examples and workload calibration rather than universal vendor, latency, similarity, retention, or cost claims. Activate on: "always-on agent", "persistent agent architecture", "episodic memory system", "agent memory design", "long-running agent", "stateful agent", "agent that remembers", "MemGPT architecture", "Letta deployment", "/always-on-agent-architecture". NOT for: choosing what data to feed the agent (use always-on-agent-inputs), brainstorming applications (use always-on-agent-applications), safety and privacy concerns (use always-on-agent-safety), general agentic patterns (use agentic-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。

Build stateful Agents on Cloudflare Workers with current, source-verified SDK APIs. Covers durable state, RPC, chat, schedules, queues, fibers, Workflows, MCP, and channels while keeping authorization and external effects application-owned. NOT for assuming SDK helpers grant application authorization or effect approval.

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。