rspack

Use when optimizing performance for user-specified files, features, compilation stages, Rust crates, JavaScript plugins, graph processing, parser work, chunking, code generation, or memory/CPU hot paths in Rspack.

Create or update draft GitHub releases for the current project's main GitHub repository, then organize GitHub-generated release notes into user-friendly sections without rewriting release note items. Use for preparing, formatting, categorizing, creating, or updating GitHub release notes or draft releases.

Create the official Rspack release pull request for a stable or pre-release version bump. Use when the task is to prepare a formal release branch from a clean checkout, sync to the latest origin/main, run `./x version` with an optional `--pre alpha|beta|rc`, confirm the resulting JavaScript and Rust versions with the user, open the release PR, trigger Ecosystem CI, and report the PR plus workflow URLs.

Use sftrace, which is based on LLVM Xray instrumentation, to trace all Rust function calls. This can be used for performance analysis and troubleshooting.

Run Rspack's perf-guided optimization loop for `cases/all` and similar workloads: create an isolated worktree, build a profiling binding, benchmark with `RSPACK_BINDING`, collect and compare `perf` hotspots, implement small Rust changes, validate, commit, push, and trigger the Ecosystem Benchmark workflow after each pushed commit. Use this when the goal is iterative performance work, not just one-off profiling.

Run Rspack performance profiling on Linux using perf (with DWARF call stacks), generate perf.data, and analyze hotspots. Use when you need CPU-level bottlenecks, kernel symbol resolution, or repeatable profiling for rspack build/bench cases. Includes optional samply import with per-CPU threads for visualization, but primary analysis is perf-based.