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 agentmods add skills/misonl/ling/performance-profilingnpx skills add MisonL/Ling --skill performance-profilinggit clone --depth 1 https://github.com/MisonL/LingWhat 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 | $0.00017 | $0.01420 |
| Opus 5 | $0.00009 | $0.00710 |
| Sonnet 5 | $0.00003 | $0.00284 |
| Haiku 4.5 | $0.00002 | $0.00142 |
Grade A, and why
performance-profiling 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 2d 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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
性能分析
测量、分析、优化 —— 必须严格遵循此顺序。
运行脚本
执行以下脚本进行自动化性能分析：
| 脚本 | 用途 | 执行命令 |
|---|---|---|
scripts/lighthouse_audit.py |
Lighthouse（性能审计工具）审计 | python scripts/lighthouse_audit.py https://example.com |
1. 核心 Web 指标
核心 Web 指标（Core Web Vitals）用于衡量加载与交互质量。
目标值
| 指标 | 优（Good） | 劣（Poor） | 衡量维度 |
|---|---|---|---|
| LCP | < 2.5s | > 4.0s | 加载体验 |
| INP | < 200ms | > 500ms | 交互响应 |
| CLS | < 0.1 | > 0.25 | 视觉稳定性 |
何时进行测量
| 阶段 | 工具选择 |
|---|---|
| 开发环境（Development） | 本地 Lighthouse |
| CI/CD 流程 | Lighthouse CI（持续集成） |
| 生产环境（Production） | RUM（真实用户监控，Real User Monitoring） |
2. 性能分析工作流
四步走流程
1. 建立基准（Baseline）-> 测量当前状态
2. 识别瓶颈（Identify）-> 找出性能卡点
3. 实施修复（Fix） -> 进行针对性改动
4. 验证改进（Validate）-> 确认性能提升
分析工具选择建议
| 待解决问题 | 推荐工具 |
|---|---|
| 页面加载速度 | Lighthouse |
| 打包体积（Bundle size） | Bundle analyzer（包分析器） |
| 运行时性能 | DevTools（开发者工具）Performance（性能）面板 |
| 内存占用 | DevTools（开发者工具）Memory（内存）面板 |
| 网络请求 | DevTools（开发者工具）Network（网络）面板 |
3. 打包产物分析
观察重点
| 潜在问题 | 识别指标 |
|---|---|
| 巨大的外部依赖 | 位于打包产物顶部 |
| 冗余/重复代码 | 存在于多个 Chunk（代码块）中 |
| 未使用的代码 | 低覆盖率（Coverage） |
| 缺失分包（Splitting） | 产物呈现为一个巨大的单体文件 |
优化手段
| 发现的问题 | 对应动作 |
|---|---|
| 库文件过大 | 按需导入（Import specific modules） |
| 依赖项重复 | 去重（Dedupe）、更新版本 |
| 主包代码包含路由逻辑 | 实施代码分割（Code split） |
| 存在未引用的导出 | 启用 Tree shake（摇树优化） |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 150 lines · 17 tokens per session scan A f675493c20eb
performance-profiling is a skill published in the GitHub repository MisonL/Ling (9 stars, last pushed 5mo ago), licensed MIT. It adds 17 tokens to every session and 1,420 once invoked, about $0.0001 per session on Opus 5. 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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