「practice」の検索結果

Best practices for building and maintaining applications with PowerSync: Cloud and self-hosted setup, sync configuration, client SDK usage, backend integration (Supabase, custom Postgres, MongoDB, Azure DocumentDB, MySQL, MSSQL), schema changes, watch and reactive queries (useQuery), attachments, the upload queue, and debugging. Use this skill whenever the user mentions PowerSync, a @powersync/* package (@powersync/web, @powersync/react-native, @powersync/node), the powersync Flutter/Dart package, the powersync Rust crate, wa-sqlite, offline-first sync, local-first architecture, sync rules, sync streams, uploadData, fetchCredentials, disconnectAndClear, logout or user switching in a synced app, real-time data replication, Electric Cloud migration, Electric Shapes, an app stuck on Syncing, data not syncing, or wants to add offline-capable sync to a mobile or web app, even if they do not explicitly name PowerSync.

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Set up an end-to-end test suite in any repo, following practices that make e2e a reliable per-PR gate: real flows over bypass, layered assertions, a reusable auth/session helper, video+trace evidence, and a compounding suite. Use when a repo has no e2e (or weak e2e) and you want system-level tests — "set up e2e", "add end-to-end tests", "scaffold a test gate".

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Orchestrate Unity Editor via MCP (Model Context Protocol) tools and resources. Use when working with Unity projects through MCP for Unity - creating/modifying GameObjects, editing scripts, managing scenes, running tests, or any Unity Editor automation. Provides best practices, tool schemas, and workflow patterns for effective Unity-MCP integration.

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Use this skill when designing, generating an MVP blueprint for, auditing, troubleshooting, refactoring, or explaining an agentic harness for any domain. Covers provider-neutral agent architecture for OpenAI, Anthropic, and OpenAI-compatible APIs: agent loops, tool design, record provenance, interactive presentation, user-memory lifecycles, environment-adaptive tools, speculative tool execution, late-bound capabilities, permissions, system prompts, planning, goals, always-on agents and durable runtime, adaptive agent teams, context compaction, memory, skills, MCP/external connectors, public-board communications, hardware agents and board deployment, self-refining recursive harnesses, programmable context, continual refinement, observability, evals, prompt caching, agent-legible environments, feedback loops, and safety.

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Provides guidance for authoring Apache Airflow DAGs in Managed Service for Apache Airflow (MSAA; formerly Cloud Composer). Covers environment context discovery, Airflow 2 vs 3 compatibility, authoring best practices, and local/remote validation processes. Use when creating or extending an Airflow DAG. Don't use when authoring Python code unrelated to Airflow DAGs.

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Guides agents to interactively discover customer requirements for live, bidirectional multi-agent AI systems that process continuous streams of multimodal data for real-time technical guidance and safety monitoring. Generates a custom Google Cloud solution that uses opinionated best practices and architecture guidance. Use when users need agentic assistance to design and create a multi-product solution in the cloud for live bidirectional multimodal streaming workloads. Don't use for simple text-based chat applications or workloads without real-time streaming requirements.

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Provides expert guidance on authenticating and authorizing to Google Cloud services and APIs, covering human users, service identities, Application Default Credentials (ADC), and best practices for secure access.

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Designs a tailored multi-product agentic data science architecture on Google Cloud that incorporates opinionated best practices. Use when architecting multi-product solutions for agent-based data analytics or ML workloads. Don't use for simple queries, non-agentic pipelines, general cloud reviews, or writing agent code.

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Guides agents through a 6-step discovery process to design and deploy Google Cloud global external Application Load Balancers with Cloud CDN, Cloud Armor, and Service Extensions, mapping workload requirements to best-practice configurations. Use when: - Designing, configuring, or deploying a Google Cloud global external Application Load Balancer, Cloud CDN, Cloud Armor WAF, or Service Extensions. - Discovering existing Google Cloud resources (Cloud Storage, MIGs, GKE, Cloud Run) to use as backends. - Generating production-grade Terraform HCL or gcloud CLI scripts for global external Application Load Balancers. - Actuating deployments via Infrastructure Manager or bash scripts, including IAM pre-checks. - Detecting, analyzing, or reconciling configuration drift on deployed global external Application Load Balancers. Don't use for: - Non-Google Cloud load balancing or security configurations. - Purely regional or internal load balancing setups (unless part of a hybrid/failover global design).

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Manages scaling for GKE workloads using HPA and VPA. Use when configuring Horizontal Pod Autoscaler (HPA), configuring Vertical Pod Autoscaler (VPA), or applying best practices for GKE workload autoscaling. Do not use for cluster-level autoscaling (Cluster Autoscaler), static cluster sizing, or configuring node-level machine styles directly.

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Trigger on mention of GKE cluster autoscaler, node autoscaling, node pool auto-creation / node auto-provisioning. Provides guidance on enabling and optimizing cluster autoscaler, best practices, and troubleshooting issues such as nodes not scaling up or down, zonal stockouts, or capacity buffers. Do not use for ComputeClass-specific YAML generation or priority configuration (defer to gke-compute-classes skill).

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Processes GCP infrastructure design and deployment workflows within Application Design Center (ADC). Use when: - Designing GCP infrastructure with Terraform. - Validating local HCL. - Performing best-practice plan scans. - Importing templates to Application Design Center (ADC). - Deploying templates. - Troubleshooting deployment failures. Boundaries: - Only use for GCP-specific cloud infrastructure. - Only use for Terraform coding within the ADC context.

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Configures best-practice alerting policies for AI agents using OpenTelemetry (OTel) metrics, generating output as Terraform (.tf) configuration files. Use when analyzing, writing, or deploying alerting policies to monitor agent latency, error rates, token usage, and quality metrics. Don't use for standard infrastructure monitoring unrelated to AI agents, or when the agent is not instrumented with OpenTelemetry (for Reliability, Cost, Safety, Security alerts). NOTE: Reliability, Cost, Safety, and Security alerts use generic OTel metrics and work across runtimes (such as Cloud Run, Vertex AI). Quality alerts rely on Vertex AI Online Monitors and are strictly bound to Vertex AI deployments.

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Provides data-retrieval best practices, tool selection guidance, and performant SQL query syntax for BigQuery telemetry across INFORMATION_SCHEMA, Cloud Monitoring, and the REST API. Use when the telemetry to fetch is already known, selecting telemetry tools, writing performant INFORMATION_SCHEMA queries, retrieving telemetry for diagnosing single-job performance bottlenecks, investigating slot contention, job concurrency and queue latency, analyzing reservation capacity, utilization and autoscaling saturation, or auditing capacity-based and on-demand compute and storage resource billable usage. Don't use for root-cause diagnosis or symptom troubleshooting when the cause is unknown (use bigquery-troubleshooting first), or for writing or optimizing business logic SQL (use bigquery-optimization).

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Always use this skill before claiming any work is done, fixed, passing, or complete — even if it seems obvious. Also use when receiving code review feedback from any source (human or automated), when finishing a task or major feature, before committing or creating a PR, or when about to express satisfaction with work. Covers three practices: receiving feedback with technical rigor (no performative agreement), triggering code review via /code-review command or cook/fix pipelines, and verification gates (run the command, read the output, then make the claim). Never skip — the cost of false completion claims is higher than the cost of verifying. References: code-review-reception.md, requesting-code-review.md, verification-before-completion.md.

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Use when configuring or troubleshooting email deliverability for Marketing Cloud or Salesforce orgs: sender authentication (SPF, DKIM, DMARC), private sending domain setup, dedicated IP warm-up, list hygiene practices, and sender reputation monitoring. NOT for building a Marketing Cloud email — use admin/email-studio-administration. NOT for unsubscribe and consent — use admin/consent-management-marketing. Also covers the Salesforce Core deliverability surface: EmailAdministrationSettings, EmailAuthorizationSettings, EmailDomainKey (DKIM), EmailRelay, EmailDomainFilter, OrgWideEmailAddress verification, bounce management, and the 5,000-a-day outbound email cap.

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Review UI code for Web Interface Guidelines compliance. Use when asked to "review my UI", "check accessibility", "audit design", "review UX", or "check my site against best practices".

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React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

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Expert guidance for integrating and building applications with shadcn/ui components, including component discovery, installation, customization, and best practices.

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โค้ชสมาธิ + mindfulness — guided meditation script (5/15/30 min) + breathing technique (box, 4-7-8) + theme practice (focus, anxiety, sleep, loving-kindness)

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Self-help mental wellness coach — daily journaling prompts + gratitude practice + emotional regulation tools + thought reframing สำหรับคนทั่วไป (ไม่ใช่ clinical therapy)

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ครูสอนภาษาต่างประเทศ (Eng/Chi/Jpn/Kor) — ประเมิน CEFR + lesson plan + conversation practice + error correction + คำศัพท์เป็น chunk

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。

Application security specialist covering OWASP top 10, secure coding, and vulnerability prevention

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Git expert covering branching strategies, collaboration, conflict resolution, and best practices

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。