performance-profiler
Systematic performance profiling for Node.js, Python, and Go applications. Identifies CPU, memory, and I/O bottlenecks, generates flamegraphs, analyzes bundle sizes, optimizes database queries, runs load tests with k6 and Artillery. Always measures before and after. Use when investigating a slow endpoint, planning a performance budget, or hunting a memory leak in production.
DeepseekModel
官方收录技能
质量 优秀 · 90
v1.0.0
获取
https://deepseekmodel.com/api/download.php?id=alirezarezvani-claude-skills-engineering-skills-performance-profiler-skill-md&format=skill
下载 .skill
标准格式，含 system_prompt 与 model_config，导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
name performance-profiler description Systematic performance profiling for Node.js, Python, and Go applications. Identifies CPU, memory, and I/O bottlenecks, generates flamegraphs, analyzes bundle sizes, optimizes database queries, runs load tests with k6 and Artillery. Always measures before and after. Use when investigating a slow endpoint, planning a performance budget, or hunting a memory leak in production. Performance Profiler Tier: POWERFUL Category: Engineering Domain: Performance Engineering Overview Systematic performance profiling for Node.js, Python, and Go applications. Identifies CPU, memory, and I/O bottlenecks; generates flamegraphs; analyzes bundle sizes; optimizes database queries; detects memory leaks; and runs load tests with k6 and Artillery. Always measures before and after. Core Capabilities CPU profiling — flamegraphs for Node.js, py-spy for Python, pprof for Go Memory profiling — heap snapshots, leak detection, GC pressure Bundle analysis — webpack-bundle-analyzer, Next.js bundle analyzer Database optimization — EXPLAIN ANALYZE, slow query log, N+1 detection Load testing — k6 scripts, Artillery scenarios, ramp-up patterns Before/after measurement — establish baseline, profile, optimize, verify When to Use App is slow and you don't know where the bottleneck is P99 latency exceeds SLA before a release Memory usage grows over time (suspected leak) Bundle size increased after adding dependencies Preparing for a traffic spike (load test before launch) Database queries taking >100ms Quick Start # Analyze a project for performance risk indicators python3 scripts/performance_profiler.py /path/to/project # JSON output for CI integration python3 scripts/performance_profiler.py /path/to/project --json # Custom large-file threshold python3 scripts/performance_profiler.py /path/to/project --large-file-threshold-kb 256 Golden Rule: Measure First # Establish baseline BEFORE any optimization # Record: P50, P95, P99 latency | RPS | error rate | memory usage # Wrong: "I think the N+1 query is slow, let me fix it" # Right: Profile → confirm bottleneck → fix → measure again → verify improvement Node.js Profiling → See references/profiling-recipes.md for details References references/profiling-recipes.md — Node.js/Python/Go profiling commands, flamegraph generation, heap snapshots references/optimization-playbook.md — before/after measurement template, quick-win optimization checklist (DB/Node/bundle/API), common pitfalls, best practices
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该技能未提供触发词。
下载的 .skill 包内含以下字段。
| 字段 | 说明 |
|---|---|
| format | 格式标识（skill/v1） |
| skill_id | 技能唯一 ID |
| name | 技能名称 |
| version | 版本号 |
| description | 技能描述 |
| category | 所属分类（数组） |
| trigger_words | 触发词列表 |
| tags | 标签列表 |
| source | 来源标识 |
| source_url | 来源链接（本页地址） |
| exported_at | 导出时间（每次下载生成） |
| system_prompt | 系统提示词正文 |
| model_config | 模型参数：provider / model / temperature / max_tokens / top_p |
| examples | 示例 |
| install_guide | 各平台导入说明（Coze / Dify / Claude / 自定义框架） |
