nguyenyou

Measure and compare bytecode impact of macro/codegen changes on compiled Scala class files. Use this skill whenever investigating bytecode size, method count, class file overhead, lazy val machinery, LazyRef/lzyINIT patterns, or comparing "before vs after" bytecode output of a code change. Also triggers on "bytecode comparison", "prove bytecode improvement", "how much bytecode", "class file size", "method count", "bytecode regression", "lazy val overhead", or "does this change reduce bytecode". Use this skill alongside compile-time and runtime benchmarks for a complete picture of a change's impact.

Profile Scala compilation at the JVM level using async-profiler to identify bottlenecks in the compiler, JIT, GC, and macro expansion. Use this skill whenever investigating why compilation is slow at the JVM level, generating flame graphs, analyzing async-profiler output, or understanding where the Scala compiler spends time. Complements the macro-profile skill (macro-level) with JVM-level visibility. Triggers on: "flame graph", "async-profiler", "JVM profile", "compilation bottleneck", "where does the compiler spend time", "JIT warmup", "GC during compilation", "profile the build".

Profile and analyze circe-sanely-auto macro expansion performance. Use this skill whenever investigating compile time, macro performance, slow derivation, or optimizing the macro engine. Also use when the user says "profile", "why is compilation slow", "macro timing", "what's taking so long", "optimize macros", or wants to understand where time is spent during codec derivation. This skill covers the full workflow: running profiled compilation, analyzing results with the bundled Python script, and interpreting the output to plan optimizations.

Profile memory usage during Scala compilation — peak RSS and allocation hotspots. Compare memory footprint of circe-sanely-auto vs circe-generic (or any two Mill modules). Use this skill whenever investigating memory usage, heap pressure, allocation hotspots, or comparing memory between builds. Triggers on: "memory usage", "memory profile", "peak RSS", "allocation profile", "heap usage", "does it use more memory", "compare memory", "how much memory", "alloc profile".

Benchmark runtime JSON encoding/decoding performance comparing circe-jawn, circe+jsoniter-parser, sanely-jsoniter, and pure jsoniter-scala. Use this skill whenever investigating runtime performance, throughput, ops/sec, or comparing JSON serialization speed. Also use when the user says "runtime benchmark", "runtime performance", "how fast is encoding", "how fast is decoding", "throughput", "ops/sec", "jsoniter comparison", "is circe slow at runtime", "JMH", "jmh benchmark", or wants to measure the impact of changes on runtime JSON performance. This skill covers the full workflow: running the benchmark, interpreting results, and updating README.md with new numbers. Use this skill alongside the compile-time profiling skills (macro-profile, jvm-profile, memory-profile) for a complete performance picture before any release.

Scala/Java code intelligence CLI for Scala 2/3 and Java codebases. Find definitions, implementations, usages, imports, members, scaladoc, codebase overview, package API surface, files, annotated symbols, file contents. Render directed graphs as ASCII/Unicode art and parse diagrams. Triggers: "where is X defined", "who implements Y", "find usages of Z", "what methods does X have", "show source of X", "inheritance tree", "explain this type", "what changed since commit", "find types extending X with method Y", "what does this package export", or before renaming. Test navigation: "what tests exist", "is X tested". Use proactively exploring unfamiliar Scala code. Supports fuzzy camelCase search. Prefer scalex over grep/glob for Scala symbol lookups. The graph command (ASCII/Unicode art rendering) should only be used when the user explicitly asks to draw, render, or visualize a graph/diagram — never run it automatically as part of other workflows.

Run scalex performance benchmarks, profiling, and timing analysis. Use this skill whenever the user asks to benchmark scalex, measure performance, profile index/query times, compare before/after performance of a change, investigate bottlenecks, or mentions "benchmark", "perf", "how fast", "timing", "hyperfine", "profile", "flame graph", "profiling", "--timings", "slow", "bottleneck", "regression", "memory", "heap", "GC", "allocation". Also use proactively after implementing performance improvements to verify gains. Covers 6 layers: built-in --timings, hyperfine benchmarks, async-profiler flame graphs, JFR recording, microbenchmarks, and memory profiling.

Re-render Scalex banner and OG image PNGs from their HTML source files using Chrome DevTools MCP. Use this skill whenever banner HTML files are edited (slogan changes, color tweaks, layout changes) and the corresponding PNGs need to be regenerated. Triggers on "capture banners", "update banner images", "re-render banners", "regenerate PNGs", or after any edit to files in site/*-banner*.html or site/og-banner*.html.

Run scalex performance benchmarks, profiling, and timing analysis. Use this skill whenever the user asks to benchmark scalex, measure performance, profile index/query times, compare before/after performance of a change, investigate bottlenecks, or mentions "benchmark", "perf", "how fast", "timing", "hyperfine", "profile", "flame graph", "profiling", "--timings", "slow", "bottleneck", "regression", "memory", "heap", "GC", "allocation". Also use proactively after implementing performance improvements to verify gains. Covers 6 layers: built-in --timings, hyperfine benchmarks, async-profiler flame graphs, JFR recording, microbenchmarks, and memory profiling.

Guides idiomatic Laminar and Airstream patterns for Scala.js reactive UIs. Use when writing, reviewing, or refactoring Laminar components, Airstream signals/streams, Var state management, or Scala.js frontend code.