Use when you need to set up Java application profiling to detect and measure performance issues — including trusted preinstalled async-profiler v4.x setup, problem-driven profiling (CPU, memory, threading, GC, I/O), interactive profiling scripts, JFR integration with Java 25 (JEP 518, JEP 520), or collecting profiling data with flamegraphs and JFR recordings. This should trigger for requests such as Improve the code with profiling; Apply Profiling; Refactor the code with profiling; Add profiling support; Collect JFR or async-profiler data for Java performance. Part of Plinth Toolkit
npx skills add https://github.com/jabrena/plinth --skill 161-java-profiling-detect
Set up the Java profiling detection phase using a trusted preinstalled async-profiler v4.x, problem-driven interactive profiling scripts, and comprehensive data collection for CPU hotspots, memory leaks, lock contention, GC issues, and I/O bottlenecks. Uses JEP 518 (Cooperative Sampling) and JEP 520 (Method Timing) for reduced overhead.
What is covered in this Skill?
Scope: Use the exact bash script templates without modification or interpretation.
Copy bash scripts exactly from templates. Ensure JVM flags are applied for profiling compatibility. Verify Java processes are running before attaching profiler.
ASYNC_PROFILER_HOME or profiler/current to point to a trusted, preinstalled async-profiler distributionRead references/161-java-profiling-detect.md and use script templates exactly as provided.
Create profiler/scripts and profiler/results, copy setup/profile scripts verbatim, and make scripts executable.
Start Java process with required profiling JVM flags and verify target process availability for profiler attachment.
Capture CPU/memory/lock/GC/I/O data and produce timestamped flamegraph and JFR outputs for analysis.
For detailed guidance, examples, and constraints, see references/161-java-profiling-detect.md.
Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation
Use when completing tasks, implementing major features, or before merging to verify work meets requirements
Execute git commit with conventional commit message analysis, intelligent staging, and message generation. Use when user asks to commit changes, create a git commit, or mentions "/commit". Supports: (1) Auto-detecting type and scope from changes, (2) Generating conventional commit messages from diff, (3) Interactive commit with optional type/scope/description overrides, (4) Intelligent file staging for logical grouping
Comprehensive GitHub code review with AI-powered swarm coordination
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
Use this skill to review code. It supports both local changes (staged or working tree) and remote Pull Requests (by ID or URL). It focuses on correctness, maintainability, and adherence to project standards.
Refactor bloated AGENTS.md, CLAUDE.md, or similar agent instruction files to follow progressive disclosure principles. Splits monolithic files into organized, linked documentation.
Create high-quality git commits: review/stage intended changes, split into logical commits, and write clear commit messages (including Conventional Commits). Use when the user asks to commit, craft a commit message, stage changes, or split work into multiple commits.
Take jabrena/161-java-profiling-detect from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.