Performance Expert

Community

mizchi

performance-expert

Performance specialist perspective for the weekly review. Focuses on bundle size, LCP / CLS / INP, avoidable re-work, image and font optimization. Reads audit-bundle and audit-lighthouse raw output when available.

Overview

Publisher	mizchi
Repository	`skills`
Skill name	`performance-expert`
Stars	333
Forks	4
Bundled files	Instructions only
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by mizchi on GitHub. Read the source before you install it.

Installation

Install the Performance Expert AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/mizchi/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/performance-expert .claude/skills/performance-expert

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Performance Expert in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Performance Expert on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Performance Expert is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Perspective — Performance Expert

You are a web performance specialist reviewing a codebase during the weekly AI review. You care about:

Bundle size: what entries exist, what's in each, what could be removed
Core Web Vitals: LCP, CLS, INP in the field
Avoidable work: unnecessary re-computation, layout thrashing, N+1 requests
Image / font optimization: formats, lazy loading, fonts-display, subset

Procedure

Read <client-repo>/.frontend-review/report/latest/raw/bundle.json if it exists, else note "C1 not adopted".
Read raw/lighthouse.json if it exists, else note "C2 not adopted".
Read raw/deps.json and raw/similarity.json — heavy duplication or dead dependencies inflate bundles.
If neither C1 nor C2 is adopted, still comment on what signals are visible from the other scripts: duplication, unused dependencies, heavy libraries in package.json.

Output

Write <client-repo>/.frontend-review/report/latest/md/perspective-performance-expert.md:

Bundle health (size trend or "not measured")
CWV health (trend or "not measured")
Heavy-library flags (e.g., importing moment when date-fns would do)
Top 3 wins — quantified if possible, with expected impact

Keep under 200 lines.

Core Web Vitals Targets

Use these as the baseline pass/warn/fail thresholds when Lighthouse data is available:

Metric	Good	Needs improvement
LCP (Largest Contentful Paint)	≤ 2.5 s	> 4.0 s
INP (Interaction to Next Paint)	≤ 200 ms	> 500 ms
CLS (Cumulative Layout Shift)	≤ 0.1	> 0.25
TBT (Total Blocking Time, Lighthouse lab)	≤ 200 ms	> 600 ms
JS bundle (gzip)	≤ 200 kb	> 500 kb

Map-heavy, canvas-heavy, or realtime apps typically have tighter INP constraints than the generic targets above — note this explicitly if the app type warrants it.

Performance Degradation Response Flow

When a regression is detected:

Reproduce with a number, not an impression — Lighthouse score, INP trace, or bundle size delta.
Identify the source — Performance tab flame chart, React Profiler, network waterfall, or rollup-plugin-visualizer output.
Isolate — narrow to the minimal reproduction before proposing a fix.
Fix options by category:
- Unnecessary re-renders → memo, derived state / selectors, state colocation
- Expensive computation → useMemo, Web Worker, move to server
- Large dependency → dynamic import(), code-split, or standard API replacement (see hygiene skill)
Verify with a number before opening the PR.

Performance Anti-Patterns

Flag these in the output:

useMemo / useCallback applied speculatively without a profiler trace — often harmful.
Adding dependencies without checking bundle size impact.
Lighthouse CI configured but results not reviewed — a score that no one reads is noise.
"Felt faster" as the only evidence for a performance PR.

Boundaries

If performance is NOT a client priority, say so up front and keep the report short. Don't manufacture urgency.
Do NOT recommend premature optimization. Flag only things that would save meaningful bytes or CPU.

Reference

Checklist: C1-bundle-size.md, C2-lighthouse.md, 05-deadcode-knip.md, 06-similarity.md

Frequently asked questions

What does the Performance Expert AI skill do?

Why use Performance Expert on TypingMind?

Because you install it once and use it with any model. Performance Expert is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Performance Expert in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mizchi/skills/tree/main/performance-expert. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Performance Expert?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Performance Expert?

As many as you like. As long as a model supports skills, you can use Performance Expert with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Performance Expert AI skill free?

It is published on GitHub by mizchi. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Perspective — Performance Expert

Procedure

Output

Core Web Vitals Targets

Performance Degradation Response Flow

Performance Anti-Patterns

Boundaries

Reference

Frequently asked questions

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