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Found 300 Skills
Performance benchmarking for a deployed NVIDIA RAG Blueprint server: profiling pass + aiperf load test driven by a single YAML config. Not for accuracy / RAGAS scoring (use rag-eval) or for deploying / repairing services (use rag-blueprint).
Use this skill when writing competitor comparison or alternative pages for a website. Trigger phrases include: "write a competitor comparison page," "create an alternatives page," "write a [Product] vs [Competitor] page," "build a best [Competitor] alternatives page," "create a '[Your Product] vs [Competitor]' page," "write a competitor vs competitor page," "create a comparison landing page," or "write SEO content comparing us to competitors." This skill covers four formats: singular alternative, plural alternatives, you vs competitor, and competitor vs competitor. Distinct from competitor-profiling, which researches and documents a competitor for internal use.
Performance optimization guidance for .NET MAUI apps covering profiling, compiled bindings, layout efficiency, image optimization, resource dictionaries, startup time, trimming, and NativeAOT configuration. USE FOR: "performance optimization", "slow startup", "app performance", "compiled bindings", "layout optimization", "image optimization", "trimming", "NativeAOT", "profiling MAUI", "reduce app size", "startup time". DO NOT USE FOR: data binding syntax (use maui-data-binding), deprecated API migration (use maui-current-apis), or unit testing setup (use maui-unit-testing).
Full Sentry SDK setup for NestJS. Use when asked to "add Sentry to NestJS", "install @sentry/nestjs", "setup Sentry in NestJS", or configure error monitoring, tracing, profiling, logging, metrics, crons, or AI monitoring for NestJS applications. Supports Express and Fastify adapters, GraphQL, microservices, WebSockets, and background jobs.
Integrate and optimize Core ML models in iOS apps for on-device machine learning inference. Covers model loading (.mlmodelc, .mlpackage), predictions with auto-generated classes and MLFeatureProvider, compute unit configuration (CPU, GPU, Neural Engine), MLTensor, VNCoreMLRequest, MLComputePlan, multi-model pipelines, and deployment strategies. Use when loading Core ML models, making predictions, configuring compute units, or profiling model performance.
Query NVIDIA PTX ISA 9.1, CUDA Runtime API 13.1, Driver API 13.1, Programming Guide v13.1, Best Practices Guide, Nsight Compute, Nsight Systems local documentation. Debug and optimize GPU kernels with nsys/ncu/compute-sanitizer workflows. Use when writing, debugging, or optimizing CUDA code, GPU kernels, PTX instructions, inline PTX, TensorCore operations (WMMA, WGMMA, TMA, tcgen05), or when the user mentions CUDA API functions, error codes, device properties, memory management, profiling, GPU performance, compute capabilities, CUDA Graphs, Cooperative Groups, Unified Memory, dynamic parallelism, or CUDA programming model concepts.
Decision-first data analysis with statistical rigor gates. Use when analyzing CSV, JSON, database exports, API responses, logs, or any structured data to support a business decision. Handles: trend analysis, cohort comparison, A/B test evaluation, distribution profiling, anomaly detection. Do NOT use for codebase analysis (use codebase-analyzer), codebase exploration (use explore-pipeline), or ML model training.
Identify and fix common testing mistakes across unit, integration, and E2E test suites. Use when tests are flaky, brittle, over-mocked, order-dependent, slow, poorly named, or providing false confidence. Use for "test smell", "fragile test", "flaky test", "over-mocking", "test anti-pattern", or "skipped tests". Do NOT use for writing new tests from scratch (use test-driven-development), refactoring architecture (use systematic-refactoring), or performance profiling without a specific test quality symptom.
Shared optimization guidance plus cuTile Python DSL-specific overlays. Use when: (1) selecting optimizations for a cuTile Python DSL kernel, (2) checking cuTile-specific implementation traps, (3) deciding whether a profiling finding belongs in shared knowledge or a cuTile overlay, (4) updating cuTile Python DSL optimization docs, (5) reviewing how a shared pattern maps to cuTile.
Analyze non-coding RNAs (miRNAs, lncRNAs, circRNAs) using miRBase, LNCipedia, RNAcentral, Rfam, and target prediction databases. Covers ncRNA identification, target prediction, disease associations, expression profiling, and functional annotation. Use when asked about microRNAs, long non-coding RNAs, RNA interference, miRNA targets, lncRNA function, or ncRNA-disease associations.
TCGA/GDC cancer genomics analysis -- cohort construction, clinical metadata retrieval, somatic mutation profiling, copy number variation analysis, survival analysis, and clinical variant interpretation. Use when users ask about TCGA data, GDC cancer cohorts, somatic mutation frequencies, Kaplan-Meier survival, CNV profiles in cancer, or OncoKB interpretation of cancer variants.
Design and operate data quality programs for financial data — golden source architecture, validation rules, data lineage, exception management, profiling, and governance. Use when building validation rules for pricing or client data pipelines, designing a data quality monitoring framework, establishing golden source designations across systems, implementing data lineage for BCBS 239 or MiFID II, investigating reconciliation breaks or billing errors traced to bad data, preparing for regulatory exams on data accuracy, building data quality scorecards, or defining data stewardship roles. Trigger on: data quality, golden source, data lineage, data validation, data profiling, exception management, data governance, BCBS 239, data completeness, data accuracy, validation rules, data anomaly, data stewardship, data quality scorecard.