Overview
- Skill Key
- gitgoodordietrying/perf-profiler
- Author
- gitgoodordietrying
- Source Repo
- openclaw/skills
- Version
- -
- Source Path
- skills/gitgoodordietrying/perf-profiler
- Latest Commit SHA
- d4520ca6a51ce6cb9faba83982305b8e4d5c6800
Official OpenClaw
rules
36%
perf-profiler
Profile and optimize application performance. Use when diagnosing slow code, measuring CPU/memory usage, generating flame graphs, benchmarking functions, load testing APIs, finding memory leaks, or optimizing database queries.
Stars
0
Installs
0
Status
ACTIVE
Visibility
PUBLIC
安装方式
直接复制以下提示词，发送给你的 AI 助手即可完成安装。
请先检查是否已安装 SkillHub 商店，若未安装，请根据 https://skillhub-1388575217.cos.ap-guangzhou.myqcloud.com/install/skillhub.md 安装 SkillHub 商店，然后安装 perf-profiler 技能。 若已安装，则直接安装 perf-profiler 技能。
Extracted Content
SKILL.md excerpt
# Performance Profiler
Measure, profile, and optimize application performance. Covers CPU profiling, memory analysis, flame graphs, benchmarking, load testing, and language-specific optimization patterns.
## When to Use
- Diagnosing why an application or function is slow
- Measuring CPU and memory usage
- Generating flame graphs to visualize hot paths
- Benchmarking functions or endpoints
- Load testing APIs before deployment
- Finding and fixing memory leaks
- Optimizing database query performance
- Comparing performance before and after changes
## Quick Timing
### Command-line timing
```bash
# Time any command
time my-command --flag
# More precise: multiple runs with stats
for i in $(seq 1 10); do
/usr/bin/time -f "%e" my-command 2>&1
done | awk '{sum+=$1; sumsq+=$1*$1; count++} END {
avg=sum/count;
stddev=sqrt(sumsq/count - avg*avg);
printf "runs=%d avg=%.3fs stddev=%.3fs\n", count, avg, stddev
}'
# Hyperfine (better benchmarking tool)
# Install: https://github.com/sharkdp/hyperfine
hyperfine 'command-a' 'command-b'
hyperfine --warmup 3 --runs 20 'my-command'
hyperfine --export-json results.json 'old-version' 'new-version'
```
### Inline timing (any language)
```javascript
// Node.js
console.time('operation');
await doExpensiveThing();
console.timeEnd('operation'); // "operation: 142.3ms"
// High-resolution
const start = performance.now();
await doExpensiveThing();
const elapsed = performance.now() - start;
console.log(`Elapsed: ${elapsed.toFixed(2)}ms`);
```
```python
# Python
import time
start = time.perf_counter()
do_expensive_thing()
elapsed = time.perf_counter() - start
print(f"Elapsed: {elapsed:.4f}s")
# Context manager
from contextlib import contextmanager
@contextmanager
def timer(label=""):
start = time.perf_counter()
yield
elapsed = time.perf_counter() - start
print(f"{label}: {elapsed:.4f}s")
with timer("data processing"):
process_data()
```
```go
// Go
start := time.Now()
doExpensiveThing()
fmt.Printf("Elap...
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