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Found 1,235 Skills
Coaches end-to-end ML system design interviews covering inference pipelines, recommendation systems, RAG, feature stores, and monitoring. Use for L6+ design rounds, ML architecture whiteboarding, system design practice, serving tradeoff analysis. Activate on "ML system design", "ML interview", "recommendation system design", "RAG architecture", "feature store design", "model serving". NOT for coding interviews, behavioral questions, ML theory quizzes, or paper implementations.
Guide post-trade compliance monitoring and trade surveillance system design. Use when building alert logic to detect churning, front-running, cherry-picking, layering, spoofing, wash trading, or marking the close, implementing post-trade best execution review, evaluating allocation fairness with pro-rata verification or dispersion analysis, designing exception-based monitoring workflows with escalation paths, correlating trading with MNPI events for insider trading detection, building personal trading surveillance for preclearance and blackout enforcement, determining SAR or blue sheet or CAT reporting triggers, or tuning surveillance thresholds to reduce false positives. Also covers turnover ratios, cost-to-equity ratios, and investigation case management.
Execute a complete tax-loss harvesting workflow from candidate identification through post-harvest monitoring. Use when the user asks about finding TLH candidates, gain/loss budgeting, replacement security selection, wash-sale compliance, or harvest execution planning. Also trigger when users mention 'unrealized losses in my portfolio', 'swap ETFs for tax purposes', 'harvest losses before year-end', 'substantially identical security', 'wash-sale window', 'NIIT offset', 'loss carryforward', or ask how much tax they can save by harvesting.
Use when building or maintaining Laravel applications — Eloquent ORM, Blade, Livewire, queues, Pest testing, middleware, service providers, migrations. Trigger conditions: Laravel project setup, Eloquent model design, Blade or Livewire component creation, queue/job implementation, Pest test writing, middleware configuration, migration authoring, route definition, Form Request validation, policy authorization, Sanctum/Passport authentication, Horizon queue monitoring.
Structured web research framework for AI agents. Teaches your agent to conduct multi-source research, synthesize findings into actionable briefs, maintain a research library, and track evolving topics over time. Use when you need market research, competitor analysis, topic deep-dives, or ongoing monitoring of trends and news. Works with any agent that has web search capabilities.
Design error handling strategies for TypeScript and Python applications — exception hierarchies, Result/Either types, retry patterns, error boundaries, and structured error logging. Use when designing error handling architecture, choosing between exceptions and Result types, implementing retry logic, or building error recovery flows. Activate on "error handling", "exception hierarchy", "Result type", "retry pattern", "circuit breaker", "error boundary", "Pokemon exception". NOT for debugging specific runtime errors, logging infrastructure setup, or monitoring/alerting configuration.
DTC Data Dashboard & Health Check Engine — Full-link data analysis, KPI tracking, industry benchmarking, data health assessment, market trend monitoring. Use when user mentions: data health check, data audit, KPI, dashboard, metrics tracking, metrics, baseline, benchmark, data analysis, revenue report, channel data, advertising data, ROAS tracking.
Autonomous agent for tackling big projects. Create PRDs with user stories, then run them via the CLI. Sessions persist across restarts with pause/resume and real-time monitoring.
Expert at diagnosing and fixing performance bottlenecks across the stack. Covers Core Web Vitals, database optimization, caching strategies, bundle optimization, and performance monitoring. Knows when to measure vs optimize. Use when "slow page load, performance optimization, core web vitals, bundle size, lighthouse score, database slow, memory leak, optimize performance, speed up, reduce load time, performance, optimization, core-web-vitals, caching, profiling, bundle-size, database" mentioned.
Patterns for sharing code between macOS and iOS in SwiftUI apps. Covers project structure (70% shared / 15% macOS / 15% iOS), platform abstraction via protocols and #if os() conditional compilation, adaptive navigation (NavigationSplitView on Mac/iPad → NavigationStack on iPhone), shared components with platform styling, iOS-specific extensions (custom keyboard extension, interactive widgets, share extension, action extension, Control Center widget, lock screen widget), App Groups for data sharing with extensions, CloudKit sync monitoring, JSON export/import, schema versioning and migration, URL scheme deep linking, and the full macOS→iOS migration checklist. Use when building apps that target both macOS and iOS, when adding iOS support to a macOS app, when building widgets or keyboard extensions, or when setting up iCloud sync with SwiftData.
Elasticsearch and Elastic APM integration with Serilog structured logging for .NET applications. Use when: (1) Implementing or configuring Serilog with Elasticsearch sink, (2) Setting up Elastic APM with data streams and authentication, (3) Creating logging extension methods in Infrastructure layer, (4) Enriching logs with app-name and app-type properties, (5) Configuring log levels and environment-specific logging, (6) Questions about logging security (PII, credentials), or (7) Troubleshooting observability and monitoring setup.
Implement and review risk controls, position sizing, portfolio heat limits, stop losses, and risk monitoring. Use when implementing risk management, reviewing risk controls, calculating position sizes, or analyzing portfolio risk exposure.