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Found 301 Skills
Exploratory Data Analysis (EDA): profiling, visualization, correlation analysis, and data quality checks. Use when understanding dataset structure, distributions, relationships, or preparing for feature engineering and modeling.
Performance profiling and bottleneck detection for Node.js, Python, and browser apps
Activate when a project needs competitive analysis, audience profiling, or positioning gaps before design begins.
Grafana Pyroscope continuous profiling platform. Covers instrumentation of Go/Java/Python/Ruby/Node.js/ .NET/Rust apps via SDKs or eBPF (Alloy), flame graph analysis, ProfileQL queries, server configuration and architecture, Grafana Cloud Profiles integration, and trace-profile linking (Span Profiles). Use when working with profiling data, instrumenting apps for Pyroscope, analyzing performance profiles, or deploying Pyroscope server.
Systematic JavaScript/TypeScript performance audit and optimization using V8 profiling and runtime patterns. Use when (1) Users say 'optimize performance', 'audit performance', 'this is slow', 'reduce allocations', 'improve speed', 'check performance', (2) Analyzing code for performance anti-patterns (O(n²) complexity, excessive allocations, I/O blocking, template literal waste), (3) Optimizing functions regardless of current usage context - utilities, formatters, parsers are often called in hot paths even when they appear simple, (4) Fixing V8 deoptimization (monomorphic/polymorphic issues, inline caching). Audits ALL code for anti-patterns and reports findings with expected gains. Covers loops, caching, batching, memory locality, algorithmic complexity fixes with ❌/✅ patterns.
Expert in system optimization, profiling, and scalability. Specializes in eBPF, Flamegraphs, and kernel-level tuning.
Identifies API latency hotspots and bottlenecks with profiling tools, slow endpoint detection, suspected causes, and fix roadmap. Use for "latency profiling", "performance bottlenecks", "slow APIs", or "backend performance".
Apply systematic performance optimization techniques when writing or reviewing code. Use when optimizing hot paths, reducing latency, improving throughput, fixing performance regressions, or when the user mentions performance, optimization, speed, latency, throughput, profiling, or benchmarking.
Use when profiling CPU/memory hot paths, generating flame graphs, or capturing JFR/perf evidence.
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.