performance-profiler
Profile application performance including CPU, memory, and flame graph generation
Best use case
performance-profiler is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Profile application performance including CPU, memory, and flame graph generation
Teams using performance-profiler should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/performance-profiler/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How performance-profiler Compares
| Feature / Agent | performance-profiler | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Profile application performance including CPU, memory, and flame graph generation
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Performance Profiler Skill
## Overview
Profiles application performance including CPU profiling, memory profiling, flame graph generation, bottleneck identification, and APM tool integration.
## Capabilities
- CPU profiling
- Memory profiling and heap snapshots
- Flame graph generation
- Bottleneck identification
- Hot path analysis
- Integration with APM tools (DataDog, New Relic)
- Event loop analysis (Node.js)
- Garbage collection analysis
## Target Processes
- performance-optimization
## Input Schema
```json
{
"type": "object",
"required": ["target"],
"properties": {
"target": {
"type": "string",
"description": "Target process, script, or endpoint"
},
"mode": {
"type": "string",
"enum": ["cpu", "memory", "heap", "all"],
"default": "cpu"
},
"duration": {
"type": "number",
"default": 30,
"description": "Profiling duration in seconds"
},
"options": {
"type": "object",
"properties": {
"samplingInterval": {
"type": "number",
"default": 1000,
"description": "Sampling interval in microseconds"
},
"generateFlameGraph": {
"type": "boolean",
"default": true
},
"outputFormat": {
"type": "string",
"enum": ["json", "html", "svg"],
"default": "html"
}
}
}
}
}
```
## Output Schema
```json
{
"type": "object",
"properties": {
"profile": {
"type": "object",
"properties": {
"duration": { "type": "number" },
"samples": { "type": "number" }
}
},
"hotspots": {
"type": "array",
"items": {
"type": "object",
"properties": {
"function": { "type": "string" },
"selfTime": { "type": "number" },
"totalTime": { "type": "number" },
"percentage": { "type": "number" }
}
}
},
"flameGraphPath": {
"type": "string"
},
"memoryStats": {
"type": "object",
"properties": {
"heapUsed": { "type": "number" },
"heapTotal": { "type": "number" },
"external": { "type": "number" }
}
},
"recommendations": {
"type": "array",
"items": { "type": "string" }
}
}
}
```
## Usage Example
```javascript
{
kind: 'skill',
skill: {
name: 'performance-profiler',
context: {
target: 'npm run start',
mode: 'cpu',
duration: 30,
options: {
generateFlameGraph: true,
outputFormat: 'html'
}
}
}
}
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