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Found 213 Skills
Predict patient response to immune checkpoint inhibitors (ICIs) using multi-biomarker integration. Given a cancer type, somatic mutations, and optional biomarkers (TMB, PD-L1, MSI status), performs systematic analysis across 11 phases covering TMB classification, neoantigen burden estimation, MSI/MMR assessment, PD-L1 evaluation, immune microenvironment profiling, mutation-based resistance/sensitivity prediction, clinical evidence retrieval, and multi-biomarker score integration. Generates a quantitative ICI Response Score (0-100), response likelihood tier, specific ICI drug recommendations with evidence, resistance risk factors, and a monitoring plan. Use when oncologists ask about immunotherapy eligibility, checkpoint inhibitor selection, or biomarker-guided ICI treatment decisions.
Valgrind profiler skill for memory error detection and cache profiling. Use when running Memcheck to find heap corruption, use-after-free, memory leaks, or uninitialised reads; or Cachegrind/Callgrind for cache simulation and function-level profiling. Activates on queries about valgrind, memcheck, heap leaks, use-after-free without sanitizers, cachegrind, callgrind, KCachegrind, or massif memory profiling.
Swift 6.2 and SwiftUI performance optimization for iOS 26 clinic architecture codebases. Covers async/await concurrency patterns, Sendable/actor isolation, view/render performance, and animation performance while preserving modular MVVM-C boundaries across App, Feature, Domain, and Data layers. Use when profiling or optimizing Swift/SwiftUI behavior in clinic modules.
Optimize application performance and scalability. Use when investigating slow applications, scaling bottlenecks, or improving response times. Use for profiling, caching, database optimization, frontend performance, and backend tuning.
Browser automation via Puppeteer CLI scripts (JSON output). Capabilities: screenshots, PDF generation, web scraping, form automation, network monitoring, performance profiling, JavaScript debugging, headless browsing. Actions: screenshot, scrape, automate, test, profile, monitor, debug browser. Keywords: Puppeteer, headless Chrome, screenshot, PDF, web scraping, form fill, click, navigate, network traffic, performance audit, Lighthouse, console logs, DOM manipulation, element selector, wait, scroll, automation script. Use when: taking screenshots, generating PDFs from web, scraping websites, automating form submissions, monitoring network requests, profiling page performance, debugging JavaScript, testing web UIs.
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.
Python performance profiling with cProfile, tracemalloc, and line_profiler. Use for identifying bottlenecks and memory issues. USE WHEN: user mentions "Python profiling", "cProfile", "memory profiling", asks about "Python performance", "tracemalloc", "line_profiler", "py-spy", "Python optimization", "Python memory leak" DO NOT USE FOR: Java/Node.js profiling - use respective skills instead
Use this skill when implementing data validation, data quality monitoring, data lineage tracking, data contracts, or Great Expectations test suites. Triggers on schema validation, data profiling, freshness checks, row-count anomalies, column drift, expectation suites, contract testing between producers and consumers, lineage graphs, data observability, and any task requiring data integrity enforcement across pipelines.
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.
Evaluates NVIDIA Cosmos Policy on LIBERO and RoboCasa simulation environments. Use when setting up cosmos-policy for robot manipulation evaluation, running headless GPU evaluations with EGL rendering, or profiling inference latency on cluster or local GPU machines.
Activate when a project needs competitive analysis, audience profiling, or positioning gaps before design begins.