Benchmark

CommunityPopular

affaan-m

benchmark

このスキルを使用して、パフォーマンスベースラインを測定し、PR前後の回帰を検出し、スタック代替案を比較します。

Overview

Publisher	affaan-m
Repository	`ECC`
Skill name	`benchmark`
Stars	261.1K
Forks	39.1K
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

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 affaan-m on GitHub. Read the source before you install it.

Installation

Install the Benchmark 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/affaan-m/ECC.git /tmp/ECC
mkdir -p .claude/skills
cp -r /tmp/ECC/docs/ja-JP/skills/benchmark .claude/skills/benchmark

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

Use it in TypingMind

Enable Benchmark 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 Benchmark 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 Benchmark 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.

ベンチマーク — パフォーマンスベースラインと回帰検出

使用時期

PR前後にパフォーマンスへの影響を測定
プロジェクトのパフォーマンスベースラインを設定
ユーザーが「遅く感じる」と報告したとき
ローンチ前 — パフォーマンスターゲットを満たしていることを確認
スタックを代替案と比較

動作方法

モード1：ページパフォーマンス

ブラウザMCPを介してリアルブラウザメトリクスを測定：


1. 各ターゲットURLに移動
2. Core Web Vitalsを測定：
   - LCP (Largest Contentful Paint) — ターゲット < 2.5s
   - CLS (Cumulative Layout Shift) — ターゲット < 0.1
   - INP (Interaction to Next Paint) — ターゲット < 200ms
   - FCP (First Contentful Paint) — ターゲット < 1.8s
   - TTFB (Time to First Byte) — ターゲット < 800ms
3. リソースサイズを測定：
   - 合計ページウェイト（ターゲット < 1MB）
   - JSバンドルサイズ（ターゲット < 200KBgzipped）
   - CSSサイズ
   - 画像ウェイト
   - サードパーティスクリプトウェイト
4. ネットワークリクエストをカウント
5. レンダリングブロッキングリソースをチェック

モード2：APIパフォーマンス

APIエンドポイントをベンチマーク：


1. 各エンドポイントに100回ヒット
2. 測定：p50、p95、p99レイテンシ
3. トラック：レスポンスサイズ、ステータスコード
4. ロード下でテスト：10個の同時リクエスト
5. SLAターゲットと比較

モード3：ビルドパフォーマンス

開発フィードバックループを測定：


1. コールドビルド時間
2. ホットリロード時間（HMR）
3. テストスイート期間
4. TypeScriptチェック時間
5. Lint時間
6. Dockerビルド時間

モード4：前後の比較

変更前後に実行して影響を測定：


/benchmark baseline    # 現在のメトリクスを保存
# ... 変更を加える ...
/benchmark compare     # ベースラインと比較

出力：


| Metric | Before | After | Delta | Verdict |
|--------|--------|-------|-------|---------|
| LCP | 1.2s | 1.4s | +200ms | WARNING: WARN |
| Bundle | 180KB | 175KB | -5KB | ✓ BETTER |
| Build | 12s | 14s | +2s | WARNING: WARN |

出力

.ecc/benchmarks/にJSONとしてベースラインを保存。Gitで追跡されるため、チームはベースラインを共有します。

統合

CI：すべてのPRで/benchmark compareを実行
/canary-watchとペアリングしてデプロイ後の監視
/browser-qaとペアリングして完全な出荷前チェックリスト

Frequently asked questions

What does the Benchmark AI skill do?

このスキルを使用して、パフォーマンスベースラインを測定し、PR前後の回帰を検出し、スタック代替案を比較します。

Why use Benchmark on TypingMind?

Because you install it once and use it with any model. Benchmark 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 Benchmark in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/affaan-m/ECC/tree/main/docs/ja-JP/skills/benchmark. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Benchmark?

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 Benchmark?

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

Is the Benchmark AI skill free?

Yes. It is published on GitHub by affaan-m under the MIT license. 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

ベンチマーク — パフォーマンスベースラインと回帰検出

使用時期

動作方法

モード1：ページパフォーマンス

モード2：APIパフォーマンス

モード3：ビルドパフォーマンス

モード4：前後の比較

出力

統合

Frequently asked questions

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