Individual skill
profiling-application-performance
Execute this skill enables AI assistant to profile application performance, analyzing cpu usage, memory consumption, and execution time. it is triggered when the user requests performance analysis, bottleneck identification, or optimization recommendations. the... Use when optimizing performance. Trigger with phrases like 'optimize', 'performance', or 'speed up'.
skills/.curated/profiling-application-performance/SKILL.md
Source commit 0500b275e963c027f6b410e8c49c0446945c574e
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- Matches the observed repository HEAD
- Yes
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- No
- Repository scan complete
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- Skill scan complete
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- Observed behavior
- Not checked yet
- Last checked
- Nov 12, 2026
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Repository context
- Repository stars (not skill ratings)
- 2,917
- Repository last updated
- Nov 12, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- ai Repository topic
- automation Repository topic
- claude-code Repository topic
- devops Repository topic
- mcp Repository topic
- ai-agents Repository topic
- anthropic Repository topic
- claude-code-plugins Repository topic
- developer-tools Repository topic
- llm Repository topic
- performance Declared skill tag
- profiling-application Declared skill tag
- engineering Derived category
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SKILL.mdcore · 3,576 bytesSKILL.mdcore · 3,576 bytesassets/README.mddoc · 59 bytesreferences/README.mddoc · 63 bytesscripts/README.mddoc · 403 bytesscripts/analyze_results.pyruns · 4,759 bytesscripts/generate_report.pyruns · 3,607 bytes
Read excerpt
# Application Profiler
Profile application performance across Node.js, Python, and Java stacks by analyzing CPU usage, memory allocation, and execution hotspots to pinpoint optimization targets.
## Overview
This skill empowers Claude to analyze application performance, pinpoint bottlenecks, and recommend optimizations. By leveraging the application-profiler plugin, it provides insights into CPU usage, memory allocation, and execution time, enabling targeted improvements.
## How It Works
1. **Identify Application Stack**: Determines the application's technology (e.g., Node.js, Python, Java).
2. **Locate Entry Points**: Identifies main application entry points and critical execution paths.
3. **Analyze Performance Metrics**: Examines CPU usage, memory allocation, and execution time to detect bottlenecks.
4. **Generate Profile**: Compiles the analysis into a comprehensive performance profile, highlighting areas for optimization.
