Generates protocol-based error/crash monitoring with swappable providers (Sentry, Crashlytics). Use when user wants to add crash reporting, error tracking, or production monitoring.
npx skills add https://github.com/rshankras/claude-code-apple-skills --skill error-monitoring
Generates a production-ready error monitoring infrastructure with protocol-based architecture for easy provider swapping.
Before generating, ALWAYS check:
# Check for existing crash reporting
rg -l "Sentry|Crashlytics|CrashReporter" --type swift
# Check Package.swift for existing SDKs
cat Package.swift | grep -i "sentry\|firebase\|crashlytics"
# Check for existing error handling patterns
rg "captureError|recordError|logError" --type swift | head -5
If existing crash reporting found:
Ask user via AskUserQuestion:
Always generate:
Sources/ErrorMonitoring/
├── ErrorMonitoringService.swift # Protocol
├── ErrorContext.swift # Breadcrumbs, user info
└── NoOpErrorMonitoring.swift # Testing/privacy
Based on provider selection:
Sources/ErrorMonitoring/Providers/
├── SentryErrorMonitoring.swift # If Sentry selected
└── CrashlyticsErrorMonitoring.swift # If Crashlytics selected
Read templates from this skill:
templates/ErrorMonitoringService.swifttemplates/ErrorContext.swifttemplates/NoOpErrorMonitoring.swifttemplates/SentryErrorMonitoring.swift (if selected)templates/CrashlyticsErrorMonitoring.swift (if selected)Adapt templates to match:
In App.swift:
import SwiftUI
@main
struct MyApp: App {
init() {
// Configure error monitoring
ErrorMonitoring.shared.configure()
}
var body: some Scene {
WindowGroup {
ContentView()
.environment(\.errorMonitoring, ErrorMonitoring.shared.service)
}
}
}
Capturing errors:
do {
try await riskyOperation()
} catch {
ErrorMonitoring.shared.service.captureError(error)
}
Adding breadcrumbs:
ErrorMonitoring.shared.service.addBreadcrumb(
Breadcrumb(category: "navigation", message: "Opened settings")
)
.package(url: "https://github.com/getsentry/sentry-cocoa", from: "8.0.0")
protocol ErrorMonitoringService: Sendable {
func configure()
func captureError(_ error: Error, context: ErrorContext?)
func captureMessage(_ message: String, level: ErrorLevel)
func addBreadcrumb(_ breadcrumb: Breadcrumb)
func setUser(_ user: MonitoringUser?)
func reset()
}
// In ErrorMonitoring.swift
final class ErrorMonitoring {
static let shared = ErrorMonitoring()
// Change this ONE line to swap providers:
let service: ErrorMonitoringService = SentryErrorMonitoring()
// let service: ErrorMonitoringService = CrashlyticsErrorMonitoring()
// let service: ErrorMonitoringService = NoOpErrorMonitoring()
}
// Automatic navigation breadcrumbs
struct ContentView: View {
@Environment(\.errorMonitoring) var errorMonitoring
var body: some View {
Button("Open Details") {
errorMonitoring.addBreadcrumb(
Breadcrumb(category: "ui", message: "Tapped details button")
)
showDetails = true
}
}
}
After generation, verify:
NoOpErrorMonitoring for EU users who opt outAdd to PrivacyInfo.xcprivacy if using Sentry/Crashlytics:
<key>NSPrivacyCollectedDataTypes</key>
<array>
<dict>
<key>NSPrivacyCollectedDataType</key>
<string>NSPrivacyCollectedDataTypeCrashData</string>
<key>NSPrivacyCollectedDataTypeLinked</key>
<false/>
<key>NSPrivacyCollectedDataTypeTracking</key>
<false/>
<key>NSPrivacyCollectedDataTypePurposes</key>
<array>
<string>NSPrivacyCollectedDataTypePurposeAppFunctionality</string>
</array>
</dict>
</array>
enum AppError: Error {
case networkFailure(URLError)
case decodingFailure(DecodingError)
case authenticationRequired
var context: ErrorContext {
ErrorContext(
tags: ["error_type": String(describing: self)],
extra: ["recoverable": isRecoverable]
)
}
}
// Capture with context
errorMonitoring.captureError(error, context: error.context)
// Sentry supports performance monitoring
let transaction = SentrySDK.startTransaction(name: "Load Data", operation: "http")
defer { transaction.finish() }
let data = try await fetchData()
// Include version info
SentrySDK.start { options in
options.dsn = "YOUR_DSN"
options.releaseName = "\(Bundle.main.appVersion)-\(Bundle.main.buildNumber)"
}
analytics-setup - Often combined with error monitoringlogging-setup - Use Logger for debug, error monitoring for productionAssess 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/error-monitoring 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.