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Found 220 Skills
Use when you need to verify Java performance optimizations by comparing profiling results before and after refactoring — including baseline validation, post-refactoring report generation, quantitative before/after metrics comparison, side-by-side flamegraph analysis, regression detection, or creating profiling-comparison-analysis and profiling-final-results documentation. Part of the skills-for-java project
Guides efficient Haskell aligned with GHC practice -- laziness and strictness, purity, fusion, newtypes, pragmas, Core reading, and space-leak avoidance. Use when writing or reviewing Haskell, optimizing or profiling, debugging strictness or memory, or when the user mentions GHC, thunks, foldl vs foldl', list fusion, SPECIALIZE, or UNPACK.
C++ template skill for reading template errors and optimizing compile times. Use when deciphering template error stacks, setting -ftemplate-backtrace-limit, writing concepts and requires-clauses, understanding SFINAE vs concepts, or profiling template instantiation bottlenecks with Templight. Activates on queries about C++ templates, template error messages, concepts, requires expressions, SFINAE, template metaprogramming, or slow template compilation.
Full Sentry SDK setup for React Router Framework mode. Use when asked to "add Sentry to React Router Framework", "install @sentry/react-router", or configure error monitoring, tracing, profiling, session replay, logs, or user feedback for a React Router v7 framework app.
Unified LLM torch-profiler triage skill for `sglang`, `vllm`, and `TensorRT-LLM`. Use it to inspect an existing `trace.json(.gz)` or profile directory, or to drive live profiling against a running server and return one three-table report with kernel, overlap-opportunity, and fuse-pattern tables.
Diagnose, improve, and prevent performance regressions in Expo-based React Native apps using release-build profiling, KPI budgets, and targeted fixes across startup, rendering, lists, images, memory, and networking.
Profile CPU performance of tests and browser tests in elements package. Use when investigating performance issues, optimizing test execution, or when the user mentions profiling, performance analysis, hotspots, or slow tests.
Use when writing automation tests, functional tests, or any test in Unreal Engine. Also use when the user asks about "UE_LOG", logging, log categories, assertion, check, ensure, verify, DrawDebug, debug draw, console command, profiling, Unreal Insights, stat commands, or debugging techniques. See ue-module-build-system for test module setup, and ue-cpp-foundations for general C++ logging patterns.
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.
Use when debugging a Nemo Gym run or reward profiling job. Covers rollout collection failures, empty or partial JSONL outputs, stale materialized inputs, verifier/schema errors, Ray or Slurm issues, vLLM readiness, judge failures, tool/sandbox failures, cache problems, and throughput bottlenecks.
Redis observability guidance — which metrics to monitor (memory, connections, hit ratio, ops/sec, rejected connections), which built-in commands to reach for during incident triage (SLOWLOG, INFO, MEMORY DOCTOR, CLIENT LIST, FT.PROFILE), and when to use the Redis Insight GUI. Use when setting up monitoring or alerts for a Redis instance, diagnosing a performance regression, profiling a slow FT.SEARCH query, or wiring Redis metrics into Prometheus, Datadog, or similar.
Systematically investigates, diagnoses, and resolves complex Magento 2 technical problems. Use when debugging issues, investigating bugs, analyzing performance problems, resolving errors, or troubleshooting system failures. Masters log analysis, performance profiling, and root cause analysis.