How does it identify relevant files without running the code?

It utilizes repository maps, grep-based searches for entrypoints, and analysis of import/export chains to determine the most likely implementation paths for a given scenario.

What is the primary use of the perf-code-paths skill?

It is used to identify and map out the specific files and functions likely involved in a performance bottleneck before you start active profiling, saving time during the optimization process.

Does this skill replace a traditional profiler?

No, it acts as a precursor to profiling. It helps you understand where to point your profiling tools (like pprof, flamegraphs, or async-profiler) for the most relevant data.

Which programming languages are supported by this skill?

The skill currently supports Rust, Java, JavaScript/TypeScript, Go, and Python.

Performance Code Path Mapper

Name: Performance Code Path Mapper
Author: avifenesh

byavifenesh

•

311

分析と監視

Identifies critical implementation paths, entrypoints, and high-traffic files to streamline performance profiling and optimization tasks.

概要

The perf-code-paths skill helps developers navigate complex codebases by mapping out the most relevant implementation paths for specific performance scenarios. By identifying entrypoints, handlers, and call chains before profiling begins, it allows engineers to focus their optimization efforts on the 'hot' files and symbols that most significantly impact system latency or throughput. It integrates with existing repository maps and search tools to provide a concise list of candidate files across supported languages like Rust, Go, Python, and TypeScript, ensuring that performance audits are targeted and efficient.

主な機能

Support for major languages including Rust, Java, JS/TS, Go, and Python
Strict relevance filtering limited to the most impactful 10-15 files
311 GitHub stars
Automated entrypoint and handler identification via repository mapping
Structured output featuring symbols and evidence for file selections
Contextual call chain tracing for performance-critical scenarios

ユースケース

Mapping request-to-response paths in unfamiliar backend architectures
Pre-profiling analysis to narrow down investigation areas in large codebases
Identifying bottleneck candidates during latency optimization sprints

概要

主な機能

Support for major languages including Rust, Java, JS/TS, Go, and Python
Strict relevance filtering limited to the most impactful 10-15 files
311 GitHub stars
Automated entrypoint and handler identification via repository mapping
Structured output featuring symbols and evidence for file selections
Contextual call chain tracing for performance-critical scenarios

ユースケース

Mapping request-to-response paths in unfamiliar backend architectures
Pre-profiling analysis to narrow down investigation areas in large codebases
Identifying bottleneck candidates during latency optimization sprints