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Found 819 Skills
Build trading systems in the style of Two Sigma, the systematic investment manager pioneering machine learning at scale. Emphasizes alternative data, distributed computing, feature engineering, and rigorous ML infrastructure. Use when building ML pipelines for alpha research, feature stores, or large-scale backtesting systems.
Adds internationalization (i18n) infrastructure with translation plumbing, scalable key strategy, formatters for dates/numbers/currency, plural rules, and language switching. Use when implementing "internationalization", "translations", "multi-language support", or "i18n".
Multi-client outbound for lead gen agencies — infrastructure architecture, client isolation, domain strategy, warmup at scale, white-labeling, unified reporting, client onboarding playbooks, and cross-client operations. Use when setting up agency outbound infrastructure, onboarding new clients, isolating sending domains per client, managing warmup across 20+ mailboxes, building repeatable client onboarding processes, or designing agency-wide reporting. Do NOT use for Smartlead-specific platform config (use /sales-smartlead), single-domain deliverability (use /sales-deliverability), or individual campaign strategy (use /sales-cadence).
On-demand and reserved GPU clusters (H100, H200, B200) on Together AI with Kubernetes or Slurm orchestration, shared storage, credential management, and cluster scaling for ML and HPC jobs. Reach for it when the user needs multi-node compute or infrastructure control rather than a managed model endpoint.
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.
Run GPU workloads on Modal's serverless infrastructure. Use when the user needs remote GPU compute for training, inference, benchmarks, or batch processing and Modal CLI is available.
World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.
Launch and configure EC2 instances with security groups, IAM roles, key pairs, AMIs, and auto-scaling. Use for virtual servers and managed infrastructure.
Expertise in compiler development using LLVM infrastructure including frontend design, IR generation, optimization passes, and code generation. Use this skill when building custom programming languages, implementing DSL compilers, or working on compiler internals.
Integrate Creem payment infrastructure for checkouts, subscriptions, licenses, and webhooks. Supports one-time payments, recurring billing, and MoR compliance.
MUST activate when the project contains a uiBundles/*/src/ directory and the task involves creating or configuring site infrastructure. Use this skill when creating or configuring a Salesforce Digital Experience Site for hosting a UI bundle. Activate when files matching digitalExperiences/, networks/, customSite/, or DigitalExperienceBundle exist and need modification, or when the user wants to publish, host, or configure guest access for their app.
Shopping price comparison using Bright Data's web scraping infrastructure. Finds where a product is sold, for how much, and whether it's in stock — across Amazon, Walmart, eBay, Best Buy, Google Shopping, and any retailer URL — then ranks the offers into a single buy-recommendation table. Use this skill when the user wants to compare prices, find the cheapest place to buy something, do a price check, see "how much does X cost on Amazon vs Walmart", track an item's price, or decide where to buy a product. Handles product names, ASINs, and direct URLs, and is region-aware (country affects price, availability, and which retailers apply). This is consumer purchase-decision research — for analyzing a competitor's pricing *strategy*, use competitive-intel instead.