Topic: typescript
2,004 skills in this topic.
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update-store
ストア情報の更新自動化 — スクリーンショット撮影（シミュレーター × モック画面 × Marionette MCP）とメタデータテキスト更新。ストア更新、スクショ更新、App Store / Google Play のメタデータ更新、リリースノート作成の際に使用すること。
K9i-0/ccpocket 572
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sim-preview
iOSシミュレーターでアプリをビルド・起動し、TrollVNC経由でiPhoneからリモートプレビューできるようにする。実装の確認をユーザーに依頼するとき、シミュレータープレビュー、VNCプレビュー、実機確認と言われたとき、UIの変更結果を見せたいときに使用する。
K9i-0/ccpocket 572
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flutter-ui-design
Flutter UI実装のアーキテクチャ規約・コンポーネント分割・状態管理ガイド（Bloc/Cubit版）
K9i-0/ccpocket 572
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merge
ブランチをメインにマージしてお掃除する
K9i-0/ccpocket 572
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test-flutter
Flutter App のテスト実行・静的解析・フォーマット・テスト記述ガイド
K9i-0/ccpocket 572
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release-app
アプリのリリース（バージョンbump + CHANGELOG + タグ → GH Actions で自動ビルド・配布）。iOS / Android / macOS の任意の組み合わせでリリースできる。「リリース」「バージョン上げて」「リリースして」と言われたときに使う。
K9i-0/ccpocket 572
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shorebird-patch
Shorebird OTA パッチの作成・staging 配布（stable 昇格はユーザー実施）
K9i-0/ccpocket 572
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test-bridge
Bridge Server (TypeScript) のテスト実行・型チェック・テスト記述ガイド
K9i-0/ccpocket 572
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mobile-automation
MCP (dart-mcp + Marionette) を使ったFlutterアプリのE2E自動化・UI検証ガイド。シミュレーターでのUI動作確認、モックプレビュー検証、Bridge経由のE2Eテスト、スクリーンショット撮影など、アプリの動作検証が必要なときに使う。「動作確認して」「UIを検証して」「E2Eテスト」「シミュレーターで確認」「モックで確認」と言われたときや、UI変更後の検証フェーズで使用すること。
K9i-0/ccpocket 572
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flutter-upgrade
Flutter SDKバージョンアップグレード対応。新バージョンのリリースノート・Breaking Changes調査、コードベース影響分析、mise/CI/Shorebird含むプロジェクト全体の対応タスクリスト作成と実行。「Flutterアップグレード」「Flutter X.Y.Zがリリースされた」「Flutter最新化」「Flutter更新」と言われたとき、またはFlutterの新バージョンについて言及されたときに使用する。
K9i-0/ccpocket 572
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ui-dev
This skill MUST be used whenever the task involves UI development, renderer code changes, adding or modifying components, creating modals or dialogs, working with CSS styles, building new UI features, or touching any file in src/renderer/. Use this skill when the user asks to "add a button", "create a modal", "add a dropdown", "update the sidebar", "style a component", "add a new UI feature", or any renderer/frontend work.
elirantutia/vibeyard 449
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api2cli
Generate a working CLI from any API, then wrap it in a Claude Code skill. Point it at API docs, a live URL, or a peek-api capture and get a dual-mode Commander.js CLI (human + agent output) plus a ready-to-use skill folder. Use when user wants to wrap an API in a CLI, generate a CLI from API docs, turn an API into a command-line tool, scaffold a CLI from discovered endpoints, or create a skill for an API.
alexknowshtml/api2cli 420
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building-forms
Builds form components and data collection interfaces including contact forms, registration flows, checkout processes, surveys, and settings pages. Includes 50+ input types, validation strategies, accessibility patterns (WCAG 2.1), multi-step wizards, and UX best practices. Provides decision trees from data type to component selection, validation timing guidance, and error handling patterns. Use when creating forms, collecting user input, building surveys, implementing validation, designing multi-step workflows, or ensuring form accessibility.
ancoleman/ai-design-components 333
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resource-tagging
Apply and enforce cloud resource tagging strategies across AWS, Azure, GCP, and Kubernetes for cost allocation, ownership tracking, compliance, and automation. Use when implementing cloud governance, optimizing costs, or automating infrastructure management.
ancoleman/ai-design-components 333
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implementing-service-mesh
Implement production-ready service mesh deployments with Istio, Linkerd, or Cilium. Configure mTLS, authorization policies, traffic routing, and progressive delivery patterns for secure, observable microservices. Use when setting up service-to-service communication, implementing zero-trust security, or enabling canary deployments.
ancoleman/ai-design-components 333
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using-vector-databases
Vector database implementation for AI/ML applications, semantic search, and RAG systems. Use when building chatbots, search engines, recommendation systems, or similarity-based retrieval. Covers Qdrant (primary), Pinecone, Milvus, pgvector, Chroma, embedding generation (OpenAI, Voyage, Cohere), chunking strategies, and hybrid search patterns.
ancoleman/ai-design-components 333
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deploying-on-aws
Selecting and implementing AWS services and architectural patterns. Use when designing AWS cloud architectures, choosing compute/storage/database services, implementing serverless or container patterns, or applying AWS Well-Architected Framework principles.
ancoleman/ai-design-components 333
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managing-configuration
Guide users through creating, managing, and testing server configuration automation using Ansible. When automating server configurations, deploying applications with Ansible playbooks, managing dynamic inventories for cloud environments, or testing roles with Molecule, this skill provides idempotency patterns, secrets management with ansible-vault and HashiCorp Vault, and GitOps workflows for configuration as code.
ancoleman/ai-design-components 333
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using-message-queues
Async communication patterns using message brokers and task queues. Use when building event-driven systems, background job processing, or service decoupling. Covers Kafka (event streaming), RabbitMQ (complex routing), NATS (cloud-native), Redis Streams, Celery (Python), BullMQ (TypeScript), Temporal (workflows), and event sourcing patterns.
ancoleman/ai-design-components 333
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designing-distributed-systems
When designing distributed systems for scalability, reliability, and consistency. Covers CAP/PACELC theorems, consistency models (strong, eventual, causal), replication patterns (leader-follower, multi-leader, leaderless), partitioning strategies (hash, range, geographic), transaction patterns (saga, event sourcing, CQRS), resilience patterns (circuit breaker, bulkhead), service discovery, and caching strategies for building fault-tolerant distributed architectures.
ancoleman/ai-design-components 333
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visualizing-data
Builds dashboards, reports, and data-driven interfaces requiring charts, graphs, or visual analytics. Provides systematic framework for selecting appropriate visualizations based on data characteristics and analytical purpose. Includes 24+ visualization types organized by purpose (trends, comparisons, distributions, relationships, flows, hierarchies, geospatial), accessibility patterns (WCAG 2.1 AA compliance), colorblind-safe palettes, and performance optimization strategies. Use when creating visualizations, choosing chart types, displaying data graphically, or designing data interfaces.
ancoleman/ai-design-components 333
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writing-dockerfiles
Writing optimized, secure, multi-stage Dockerfiles with language-specific patterns (Python, Node.js, Go, Rust), BuildKit features, and distroless images. Use when containerizing applications, optimizing existing Dockerfiles, or reducing image sizes.
ancoleman/ai-design-components 333
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architecting-security
Design comprehensive security architectures using defense-in-depth, zero trust principles, threat modeling (STRIDE, PASTA), and control frameworks (NIST CSF, CIS Controls, ISO 27001). Use when designing security for new systems, auditing existing architectures, or establishing security governance programs.
ancoleman/ai-design-components 333
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deploying-on-azure
Design and implement Azure cloud architectures using best practices for compute, storage, databases, AI services, networking, and governance. Use when building applications on Microsoft Azure or migrating workloads to Azure cloud platform.
ancoleman/ai-design-components 333