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skill-performance-optimization

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포크1

업데이트2026년 6월 16일 23:48

Profile systems, identify bottlenecks, optimize code and infrastructure for speed, throughput, and resource efficiency.

설치

Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.

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saitarrun

saitarrun/sdlc-ai-workflow

GitHub 저장소 열기 Creator 저장소 보기

다운로드

Manus에서 실행

Skill: Performance Optimization

Systematic approach to identifying and eliminating performance bottlenecks through profiling, benchmarking, and data-driven optimization.

Process

Establish Baselines — Measure current performance (latency, throughput, memory, CPU)
Profile — Identify where time/resources are spent (flame graphs, traces, logs)
Find Bottlenecks — Rank by impact (80/20 rule: fix the top bottlenecks first)
Optimize — Apply targeted fixes (algorithm, caching, parallelization, resource allocation)
Verify — Re-measure to confirm improvements
Prevent Regressions — Add performance tests, monitoring, budgets

Key Metrics

Latency: p50, p95, p99 response time
Throughput: Requests per second, transactions per minute
Resource Usage: CPU %, memory consumption, disk I/O
Cost: Infrastructure cost per unit work

Common Bottlenecks

Inefficient algorithms (O(n²) instead of O(n log n))
Missing indexes on database queries
Synchronous operations that should be async
Excessive logging or serialization
Memory leaks or inefficient data structures
Network calls without batching/caching
Single-threaded workloads that could parallelize

Optimization Strategies

Algorithmic: Better algorithm selection
Caching: Reduce computation, add layer caching
Async: Non-blocking operations, parallelization
Resource Allocation: CPU, memory, connections tuning
Infrastructure: Scaling, CDN, geographic distribution
Monitoring: Real-time alerting on perf regressions

Status: Ready for profiling, optimization, and performance tuning Best for: Latency reduction, throughput improvement, resource efficiency

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name	skill-performance-optimization
description	Profile systems, identify bottlenecks, optimize code and infrastructure for speed, throughput, and resource efficiency.
version	1.0.0

Skill: Performance Optimization

Systematic approach to identifying and eliminating performance bottlenecks through profiling, benchmarking, and data-driven optimization.

Process

Establish Baselines — Measure current performance (latency, throughput, memory, CPU)
Profile — Identify where time/resources are spent (flame graphs, traces, logs)
Find Bottlenecks — Rank by impact (80/20 rule: fix the top bottlenecks first)
Optimize — Apply targeted fixes (algorithm, caching, parallelization, resource allocation)
Verify — Re-measure to confirm improvements
Prevent Regressions — Add performance tests, monitoring, budgets

Key Metrics

Latency: p50, p95, p99 response time
Throughput: Requests per second, transactions per minute
Resource Usage: CPU %, memory consumption, disk I/O
Cost: Infrastructure cost per unit work

Common Bottlenecks

Inefficient algorithms (O(n²) instead of O(n log n))
Missing indexes on database queries
Synchronous operations that should be async
Excessive logging or serialization
Memory leaks or inefficient data structures
Network calls without batching/caching
Single-threaded workloads that could parallelize

Optimization Strategies

Algorithmic: Better algorithm selection
Caching: Reduce computation, add layer caching
Async: Non-blocking operations, parallelization
Resource Allocation: CPU, memory, connections tuning
Infrastructure: Scaling, CDN, geographic distribution
Monitoring: Real-time alerting on perf regressions

Status: Ready for profiling, optimization, and performance tuning Best for: Latency reduction, throughput improvement, resource efficiency