Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add an8079/take-skills --skill performance-reviewergit clone --depth 1 https://github.com/an8079/take-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/an8079/take-skills/performance-reviewer)<a href="https://agentmods.dev/skills/an8079/take-skills/performance-reviewer"><img src="https://agentmods.dev/badge/skills/an8079/take-skills/performance-reviewer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/an8079/take-skills/performance-reviewer"><img src="https://agentmods.dev/badge/skills/an8079/take-skills/performance-reviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.01567 |
| Opus 5 | $0.00000 | $0.00783 |
| Sonnet 5 | $0.00000 | $0.00313 |
| Haiku 4.5 | $0.00000 | $0.00157 |
Grade A, and why
performance-reviewer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.
performance-reviewer
性能专项审查技能 — 前端/后端性能问题识别、量化分析、优化建议
触发词
perf, performance, 性能审查, 慢, 优化, performance-review
概述
作为 performance-reviewer，专注于识别和量化性能问题。涵盖前端渲染性能、后端 API 响应、数据库查询、内存使用、网络请求等维度。
核心审查维度
1. 前端性能
渲染性能
- 首屏加载时间（LCP < 2.5s）
- 首次内容绘制（FCP < 1.8s）
- 交互准备时间（TTI < 3.8s）
- 累计布局偏移（CLS < 0.1）
- 阻塞时间（TBT < 200ms）
运行时性能
- React/Vue 组件重渲染分析
- 虚拟滚动实现检查（大列表）
- 防抖/节流正确使用
- 动画性能（使用 transform/opacity）
- 内存泄漏检测（未清理的定时器/订阅）
资源优化
- 图片优化（格式、WebP、懒加载）
- 字体优化（字体子集、display:swap）
- CSS/JS 打包优化（code splitting）
- CDN 使用检查
- 缓存策略（强缓存/协商缓存）
2. 后端性能
API 响应时间
- P50/P95/P99 延迟
- 超时配置合理性
- 并发处理能力
数据库性能
- 索引使用情况
- N+1 查询问题
- 全表扫描检测
- 慢查询识别（>100ms）
- 查询复杂度分析
缓存策略
- Redis/Memcached 命中率
- 缓存穿透/击穿/雪崩风险
- 缓存一致性
3. 网络性能
请求优化
- 请求合并
- 请求取消（AbortableFetch）
- 预加载/预连接
- HTTP/2 或 HTTP/3 使用
资源大小
- 响应体大小（gzip 后 < 500KB）
- 大文件 CDN 化
- 图片压缩质量
性能审查检查清单
[ ] Core Web Vitals
- [ ] LCP < 2.5s
- [ ] FCP < 1.8s
- [ ] TTI < 3.8s
- [ ] CLS < 0.1
- [ ] TBT < 200ms
[ ] 前端优化
- [ ] 图片使用正确格式（WebP/AVIF）
- [ ] 图片有懒加载
- [ ] 字体子集化 + display:swap
- [ ] JS/CSS code splitting
- [ ] 无大型内联脚本
- [ ] 第三方脚本异步加载
[ ] React/Vue 性能
- [ ] 大列表使用虚拟滚动
- [ ] useMemo/useCallback 合理使用
- [ ] Context 避免频繁更新
- [ ] 无内存泄漏（定时器/订阅清理）
[ ] 后端性能
- [ ] 数据库有适当索引
- [ ] 无 N+1 查询
- [ ] 慢查询 < 100ms
- [ ] 缓存命中率 > 80%（热点数据）
- [ ] API 有超时限制
[ ] 网络优化
- [ ] 静态资源使用 CDN
- [ ] 开启 gzip/brotli
- [ ] 有 HTTP/2 或 HTTP/3
- [ ] 预连接关键域名
执行流程
Step 1: 性能测量
前端性能测量
# Lighthouse CLI
npx lighthouse <url> --output=json --output-path=report.json
# WebPageTest（严重情况下）
# Chrome DevTools Protocol
后端性能测量
# 慢查询日志分析
# APM 工具（New Relic/Datadog/Sentry）
# 数据库 EXPLAIN 分析
Step 2: 代码分析
- 读取关键文件（路由、组件、数据库查询）
- 识别明显性能问题
- 分析算法复杂度
Step 3: 量化优先级
按性能影响排序：
P0: 阻塞首屏（用户直接看到）
P1: 影响核心功能（显著卡顿）
P2: 体验优化（轻微延迟）
P3: 未来优化（可以接受）
Step 4: 输出报告
## 性能审查报告
### 关键指标
| 指标 | 当前值 | 目标值 | 状态 |
|------|--------|--------|------|
| LCP | 4.2s | <2.5s | 🔴 |
| FCP | 2.1s | <1.8s | 🟡 |
| TTI | 5.8s | <3.8s | 🔴 |
### Top 性能问题
| 优先级 | 问题 | 影响 | 修复方案 |
|--------|------|------|----------|
| P0 | 图片无懒加载 | +1.8s LCP | 添加 loading="lazy" |
| P1 | N+1 查询 | 500ms+ | 使用 JOIN 或批量查询 |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 191 lines · 0 tokens per session scan A 98e5f77789af
performance-reviewer is a skill published in the GitHub repository an8079/take-skills (4 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,567 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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