Analyze Node.js CPU profiles to identify bottlenecks and generate optimization recommendations.
npx skills add https://github.com/jankdc/ultraman-bench --skill profile
Please help me install this Agent Skill. Skill: profile Source: https://github.com/jankdc/ultraman-bench/tree/main/.claude/skills/profile Command: npx skills add https://github.com/jankdc/ultraman-bench --skill profile
This Skill eliminates the complex, time-consuming manual analysis of CPU profiles. It automatically identifies performance bottlenecks, high garbage collection, and excessive JS/WASM boundary crossing, making optimization efforts faster and more targeted.
First, collect a CPU profile for the 'simple-object' scenario using the command: SCENARIO=simple-object npm run profile:cpu. Then, analyze the generated profile by asking me to 'analyze the latest CPU profile for simple-object parsing and provide optimization recommendations'.
High-intent search queries and answers about installing and using this skill.
CPU profile analysis automatically detects hotspots, memory allocation issues, and JS/WASM boundary crossing overhead. Run SCENARIO=simple-object npm run profile:cpu to collect a profile, then analyze it to pinpoint exact functions causing slowdowns and receive optimization guidance.
Yes. This analyzes WASM CPU profiles by enriching them with detailed function symbols via wasm-objdump, providing disassembly for critical functions so you identify exactly which WASM operations consume the most CPU time.
Builds must have debug symbols enabled to yield meaningful WASM and function names in profiles. Dependencies include wabt and binaryen for symbol enrichment and analysis across Node.js profiling workflows.
After analysis, this generates data-driven suggestions targeting identified hotspots: it flags garbage collection overhead, boundary-crossing marshaling costs, and high-frequency functions, then recommends specific code changes to reduce execution time.
Yes. The analysis produces optional JSON output containing categorized hotspots, time percentages, GC and boundary-crossing issues, and optimization recommendations for programmatic downstream use.
This focuses on CPU profiles but flags GC overhead as a performance bottleneck within them. It identifies excessive garbage collection as a category of hotspot requiring optimization but does not perform dedicated memory profiling.
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