142 件 ・ 関連度順
概要と使いどころ
Implement GitOps with ArgoCD for declarative Kubernetes deployments. Configure applications, manage sync policies, implement progressive delivery, and automate deployments from Git repositories. Use when implementing GitOps workflows or continuous deployment to Kubernetes.
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Use when adding a new messaging channel to Tel-Agent or changing an existing one — a transport under api/channels/, its official extension manifest, its settings card, tests, health and docs. Covers the file checklist, the two inbound shapes (dial-out and the public door), the credential and signature rules that are not negotiable, the declarative setup descriptor the generic card draws from, and the test shape every channel ships with.
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Build declarative 3D scenes with React Three Fiber (R3F) - a React renderer for Three.js. Use when building interactive 3D experiences in React applications with component-based architecture, state management, and reusable abstractions. Ideal for product configurators, portfolios, games, data visualization, and immersive web experiences.
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Fade Big — a composable AnimXYZ entrance via the xyz="fade big-50%" attribute. Declarative: mix fade + move + scale + rotate without writing keyframes. MIT. Honors prefers-reduced-motion.
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Rotate In — a composable AnimXYZ entrance via the xyz="fade rotate-right-45" attribute. Declarative: mix fade + move + scale + rotate without writing keyframes. MIT. Honors prefers-reduced-motion.
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Flip Up — a composable AnimXYZ entrance via the xyz="fade flip-up-90" attribute. Declarative: mix fade + move + scale + rotate without writing keyframes. MIT. Honors prefers-reduced-motion.
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Flip Left — a composable AnimXYZ entrance via the xyz="fade flip-left-90" attribute. Declarative: mix fade + move + scale + rotate without writing keyframes. MIT. Honors prefers-reduced-motion.
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Fade Down — a composable AnimXYZ entrance via the xyz="fade down-3" attribute. Declarative: mix fade + move + scale + rotate without writing keyframes. MIT. Honors prefers-reduced-motion.
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Fade Small — a composable AnimXYZ entrance via the xyz="fade small-50%" attribute. Declarative: mix fade + move + scale + rotate without writing keyframes. MIT. Honors prefers-reduced-motion.
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Rise Big — a composable AnimXYZ entrance via the xyz="fade up-5 big-25%" attribute. Declarative: mix fade + move + scale + rotate without writing keyframes. MIT. Honors prefers-reduced-motion.
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Fade Left — a composable AnimXYZ entrance via the xyz="fade left-3" attribute. Declarative: mix fade + move + scale + rotate without writing keyframes. MIT. Honors prefers-reduced-motion.
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。
Fade Up — a composable AnimXYZ entrance via the xyz="fade up-3" attribute. Declarative: mix fade + move + scale + rotate without writing keyframes. MIT. Honors prefers-reduced-motion.
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。
Fade Right — a composable AnimXYZ entrance via the xyz="fade right-3" attribute. Declarative: mix fade + move + scale + rotate without writing keyframes. MIT. Honors prefers-reduced-motion.
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Manage paid ads on AdManage.ai from declarative config - default schedules launches across Meta/TikTok/Snapchat/Pinterest/LinkedIn (always PAUSED); create provisions Meta campaigns and ad sets.
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Best practices for command definition files - size targets, declarative template, anti-patterns, and canonical examples based on research evidence
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Step-by-step workflow for converting bloated command files to lean declarative definitions
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Build high-performance declarative UI with Jetpack Compose. Use when writing Composable functions, optimizing recomposition, hoisting state, or working with LazyColumn and side effects; defer deep-link and navigation routing to android-navigation.
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Managing cloud infrastructure using declarative and imperative IaC tools. Use when provisioning cloud resources (Terraform/OpenTofu for multi-cloud, Pulumi for developer-centric workflows, AWS CDK for AWS-native infrastructure), designing reusable modules, implementing state management patterns, or establishing infrastructure deployment workflows.
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Operating SOP for DSPy (Stanford NLP) — the declarative framework for "programming, not prompting" language models. Activate when the user says any of: "use DSPy", "compile a prompt", "optimize prompts/programs", "MIPRO/MIPROv2", "BootstrapFewShot", "GEPA", "Signatures + Modules", "teleprompter", "auto-tune prompts for a different LM", or whenever a brittle hand-crafted prompt pipeline needs to be turned into a *compiled*, measurable, swappable program. Do NOT activate for one-shot prompt tweaks, no-metric exploratory work, or pipelines where prompts must remain human-authored verbatim — use raw prompting or LangChain templates instead.
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Use when doing ANY task involving Supabase. Triggers: Supabase products (Database, Auth, Edge Functions, Realtime, Storage, Vectors, Cron, Queues); client libraries and SSR integrations (supabase-js, @supabase/ssr) in Next.js, React, SvelteKit, Astro, Remix; auth issues (login, logout, sessions, JWT, cookies, getSession, getUser, getClaims, RLS); Supabase CLI or MCP server; schema changes, migrations, declarative schemas, security audits, Postgres extensions (pg_graphql, pg_cron, pg_vector); debugging and troubleshooting errors or unexpected behavior on Supabase projects (HTTP errors, Postgres errors, RLS surprises, permission denied, schema cache issues, timeouts, Edge Function crashes, Realtime drops, Storage failures) and reading or querying logs (Logs Explorer, ClickHouse).
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Postgres best practices maintained by Supabase, for Postgres running anywhere. Load this skill BEFORE writing or changing anything that lives in a Postgres database: creating or altering tables and columns (including choosing column types), schema design, migrations and declarative schema files, RLS policies and the tests that verify them, indexes, triggers, database functions, queues and scheduled jobs (pg_cron, pgmq), vector/semantic search (pgvector), and restoring dumps (pg_restore) or importing data. Also load it when diagnosing slow queries, high CPU, timeouts, EXPLAIN plans, connection exhaustion, locking, bloat, or rows visible to the wrong user or tenant. This is not just a performance guide — schema, migration, security, and SQL authoring tasks need these rules too, even for a one-column change or a single query.
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Provides patterns to build declarative AI Services with LangChain4j for LLM integration, chatbot development, AI agent implementation, and conversational AI in Java. Generates type-safe AI services using interface-based patterns, annotations, memory management, and tools integration. Use when creating AI-powered Java applications with minimal boilerplate, implementing conversational AI with memory, or building AI agents with function calling.
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Serverless compute for Databricks jobs, Lakeflow pipelines and Declarative Automation Bundles (DABs), with STANDARD or PERFORMANCE_OPTIMIZED and classic only where serverless cannot run the workload. Use when creating, deploying, scheduling or editing a job or pipeline, or deciding its compute. Invoke BEFORE writing a job spec. For migrating existing classic workloads, use databricks-serverless-migration.
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Train ML models on Databricks. Use for: classification/regression/deep-learning (XGBoost, scikit-learn, LightGBM, PyTorch) with Optuna, @prod/@challenger aliases, batch scoring (spark_udf for plain models, fe.score_batch for feature-store-backed), custom PyFunc, custom ResponsesAgent (LangGraph + UC Function/Vector Search); UC feature tables + FeatureLookup + point-in-time joins + Lakebase online store; declarative Feature Views (create_feature, DeltaTableSource, RollingWindow/SlidingWindow/TumblingWindow, materialize_features, streaming Kafka features). NOT for: endpoint ops (databricks-model-serving), MLflow evaluation (databricks-mlflow-evaluation).
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