'deep' 검색 결과

Universal deep research agent team. 13-agent pipeline for rigorous academic research on any topic. 7 modes: full research, quick brief, paper review, lit-review, fact-check, Socratic guided research dialogue, and systematic review with optional meta-analysis. Covers research question formulation, Socratic mentoring, methodology design, systematic literature search, source verification, cross-source synthesis, risk of bias assessment, meta-analysis, APA 7.0 report compilation, editorial review, devil's advocate challenges, ethics review, and post-research literature monitoring. Triggers on: research, deep research, literature review, systematic review, meta-analysis, PRISMA, evidence synthesis, fact-check, guide my research, help me think through, 研究, 深度研究, 文獻回顧, 文獻探討, 系統性回顧, 後設分析, 事實查核, 引導我的研究, 幫我釐清, 幫我想想, 我不確定要研究什麼, 研究方向, 研究主題.

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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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Use when working with Dagster Deep — dagster deep data orchestration management covering job and asset inventory, run monitoring, schedule and sensor health, partition status, resource configuration, and daemon health checks. Use when investigating run failures, analyzing asset materialization, monitoring schedule ticks, or auditing Dagster configurations.

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Use when working with Deno Deploy Deep — deep Deno Deploy analysis covering project inventory, deployment history, KV database usage, cron job schedules, custom domain mappings, analytics metrics, and environment variable auditing. Use for comprehensive Deno Deploy platform management.

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Use when working with Cloudformation Deep — aWS CloudFormation deep stack management. Covers stack operations, change sets, drift detection, nested stacks, stack sets, resource imports, template analysis, and rollback troubleshooting. Use when managing complex CloudFormation deployments, debugging stack failures, detecting drift, operating stack sets, or analyzing template dependencies.

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Use when working with Cloudflare Workers Deep — deep analysis of Cloudflare Workers deployments including script inventory, route mappings, KV namespace usage, Durable Objects, cron triggers, CPU time metrics, and error rate tracking. Use for comprehensive Workers platform health checks and optimization.

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Use when working with Bitbucket Deep — bitbucket deep platform management covering repository inventory, Pipelines CI/CD monitoring, pull request analysis, branch permission auditing, deployment environment tracking, and workspace member management. Use when auditing repository configurations, investigating pipeline failures, reviewing branch restrictions, or managing workspace access.

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Use when working with Cloudflare Dns Deep — advanced Cloudflare DNS management covering zone records, DNSSEC configuration, DNS analytics, load balancing pools, health checks, and DNS firewall. Use for deep DNS record management, DNSSEC troubleshooting, DNS traffic analytics, configuring DNS-based load balancing, or auditing DNS security settings.

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Use when working with Aws Organizations Deep — aWS Organizations deep management. Covers organizational unit hierarchy, account management, service control policies (SCPs), tag policies, backup policies, delegated administrators, and organizational service integrations. Use when managing multi-account AWS environments, reviewing SCPs, auditing OU structures, or analyzing organizational policies.

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Use when working with Azure Functions Deep — deep Azure Functions analysis covering function app inventory, trigger types, execution metrics, scaling behavior, slot deployments, application insights integration, and consumption plan cost tracking. Provides optimization guidance for cold starts and memory usage.

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Use when working with Azure Policy Deep — azure Policy deep compliance and governance management. Covers policy definitions, initiatives, assignments, compliance evaluation, exemptions, remediation tasks, custom policies, and regulatory compliance. Use when managing Azure governance at scale, reviewing compliance states, creating custom policies, or troubleshooting policy evaluation results.

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Use when working with Aws Config Deep — aWS Config deep rule and compliance management. Covers Config rules, conformance packs, aggregators, advanced queries, remediation actions, configuration history, and multi-account compliance dashboards. Use when auditing AWS resource compliance, managing Config rules, reviewing conformance pack results, or querying resource configurations across accounts.

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Use when working with Aws Lambda Deep — deep AWS Lambda analysis covering function inventory, cold start profiling, memory optimization, concurrency patterns, layer dependency auditing, dead letter queue health, event source mappings, and cost estimation. Goes beyond basic metrics to identify optimization opportunities.

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Use when working with Apache Airflow Deep — apache Airflow deep management covering DAG inventory, task instance monitoring, scheduler health, executor status, pool and variable management, connection auditing, and SLA miss tracking. Use when investigating DAG failures, analyzing task duration trends, monitoring scheduler performance, or auditing Airflow configurations.

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Use when working with Aws Cloudfront Deep — advanced AWS CloudFront management covering distribution lifecycle, behavior configuration, Lambda@Edge functions, origin groups for failover, real-time logs, field-level encryption, and cache policy optimization. Use for deep CloudFront troubleshooting, performance tuning, advanced origin configuration, or Lambda@Edge debugging.

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Use when working with Apache Druid Deep — apache Druid deep management — monitor cluster health, datasources, ingestion tasks, segments, query performance, and coordinator/overlord status. Use when debugging ingestion failures, inspecting segment distribution, optimizing query latency, or auditing cluster capacity.

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Use when working with Aws Rds Deep — aWS RDS deep analysis covering Performance Insights, event subscriptions, proxy management, and global database status. Covers PI counter metrics, wait event analysis, top SQL queries, read replica lag, storage autoscaling, and Aurora-specific diagnostics.

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Use when working with Aws Elasticache Deep — aWS ElastiCache deep analysis for Redis and Memcached clusters, replication health, failover analysis, and performance metrics. Covers node-level metrics, memory utilization, cache hit rates, eviction tracking, connection analysis, and engine-specific diagnostics.

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Shared vocabulary for designing deep modules. Use when the user wants to design or improve a module's interface, find deepening opportunities, decide where a seam goes, make code more testable or AI-navigable, or when another skill needs the deep-module vocabulary.

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NVIDIA DeepStream SDK development guidance for Python pyservicemaker pipelines, video analytics, TensorRT integration, and streaming inference workflows.

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Shared vocabulary for designing deep modules. Use when the user wants to design or improve a module's interface, find deepening opportunities, decide where a seam goes, make code more testable or AI-navigable, or when another skill needs the deep-module vocabulary.

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Deep literature review — PubMed, EuropePMC, bioRxiv preprints, citation networks, evidence synthesis. Disambiguates queries, runs collision-aware searches, grades evidence T1-T4, and produces structured reports. Use for systematic literature review, meta-analysis evidence collection, and detailed answer-with-citations workflows.

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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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Solve partial differential equations — finite differences, spectral methods, and physics-informed neural networks (PINNs via DeepXDE). Supports 1D/2D/3D, steady/transient, linear/nonlinear PDEs with Dirichlet, Neumann, and periodic boundary conditions.

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