Generates pagination infrastructure with offset or cursor-based patterns, infinite scroll, and search support. Use when user wants to add paginated lists, infinite scrolling, or load-more functionality.
npx skills add https://github.com/rshankras/claude-code-apple-skills --skill pagination
Generate production pagination infrastructure supporting offset-based and cursor-based APIs, with infinite scroll SwiftUI views, state machine management, and optional search integration.
Use this skill when the user:
Search for existing networking code:
Glob: **/*API*.swift, **/*Client*.swift, **/*Endpoint*.swift
Grep: "APIClient" or "APIEndpoint"
If networking-layer generator was used, detect the APIEndpoint protocol and generate data sources that conform to it.
Search for existing pagination:
Glob: **/*Pagina*.swift, **/*LoadMore*.swift
Grep: "PaginationState" or "loadNextPage" or "hasMorePages"
If found, ask user whether to replace or extend.
Ask user via AskUserQuestion:
Read pagination-patterns.md for architecture guidance.
Read templates.md for production Swift code.
Generate these files:
PaginatedResponse.swift — Generic response models for offset and cursorPaginationState.swift — State machine (idle, loading, loaded, error, exhausted)PaginatedDataSource.swift — Protocol endpoints conform toPaginationManager.swift — @Observable manager with state transitionsBased on configuration:
SearchablePaginationManager.swift — If search selectedViews/PaginatedList.swift — Infinite scroll SwiftUI wrapperViews/LoadMoreButton.swift — Manual load-more buttonViews/PaginationStateView.swift — Empty/loading/error state viewsCheck project structure:
Sources/ exists → Sources/Pagination/App/ exists → App/Pagination/Pagination/After generation, provide:
Pagination/
├── PaginatedResponse.swift # Generic response models
├── PaginationState.swift # State machine enum
├── PaginatedDataSource.swift # Data source protocol
├── PaginationManager.swift # @Observable manager
├── SearchablePaginationManager.swift # Optional: search + pagination
└── Views/
├── PaginatedList.swift # Infinite scroll wrapper
├── LoadMoreButton.swift # Manual load-more
└── PaginationStateView.swift # Empty/loading/error states
Define a data source:
struct UsersDataSource: PaginatedDataSource {
typealias Item = User
let apiClient: APIClient
func fetch(page: PageRequest) async throws -> PaginatedResponse<User> {
try await apiClient.request(UsersEndpoint(page: page.page, size: page.size))
}
}
Use PaginationManager in a view model:
@Observable
final class UsersViewModel {
let pagination: PaginationManager<UsersDataSource>
init(apiClient: APIClient) {
pagination = PaginationManager(
dataSource: UsersDataSource(apiClient: apiClient)
)
}
}
With SwiftUI (infinite scroll):
struct UsersListView: View {
@State private var viewModel = UsersViewModel()
var body: some View {
PaginatedList(manager: viewModel.pagination) { user in
UserRow(user: user)
}
.task {
await viewModel.pagination.loadFirstPage()
}
}
}
With search:
struct SearchableUsersView: View {
@State private var searchManager = SearchablePaginationManager(
dataSource: UsersDataSource()
)
var body: some View {
PaginatedList(manager: searchManager.pagination) { user in
UserRow(user: user)
}
.searchable(text: $searchManager.query)
}
}
@Test
func loadFirstPagePopulatesItems() async throws {
let mockSource = MockDataSource(items: User.mockList(count: 20))
let manager = PaginationManager(dataSource: mockSource, pageSize: 10)
await manager.loadFirstPage()
#expect(manager.items.count == 10)
#expect(manager.state == .loaded)
#expect(manager.hasMore == true)
}
@Test
func loadAllPagesReachesExhausted() async throws {
let mockSource = MockDataSource(items: User.mockList(count: 15))
let manager = PaginationManager(dataSource: mockSource, pageSize: 10)
await manager.loadFirstPage()
await manager.loadNextPage()
#expect(manager.items.count == 15)
#expect(manager.state == .exhausted)
}
generators/networking-layer — Base networking layer for data sourcesgenerators/http-cache — Cache paginated responsesAssess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take rshankras/pagination from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.