jpskill.com
🛠️ 開発・MCP コミュニティ 🔴 エンジニア向け 👤 エンジニア・AI開発者

🛠️ Zipai最適化ツール

zipai-optimizer

AIが情報を処理する際の最小単位であるトークンを

⏱ MCPサーバー実装 1日 → 2時間

📺 まず動画で見る(YouTube)

▶ 【衝撃】最強のAIエージェント「Claude Code」の最新機能・使い方・プログラミングをAIで効率化する超実践術を解説! ↗

※ jpskill.com 編集部が参考用に選んだ動画です。動画の内容と Skill の挙動は厳密には一致しないことがあります。

📜 元の英語説明(参考)

Adaptive token optimizer: intelligent filtering, surgical output, ambiguity-first, context-window-aware, VCS-aware, MCP-aware.

🇯🇵 日本人クリエイター向け解説

一言でいうと

AIが情報を処理する際の最小単位であるトークンを

※ jpskill.com 編集部が日本のビジネス現場向けに補足した解説です。Skill本体の挙動とは独立した参考情報です。

⚡ おすすめ: コマンド1行でインストール(60秒)

下記のコマンドをコピーしてターミナル(Mac/Linux)または PowerShell(Windows)に貼り付けてください。 ダウンロード → 解凍 → 配置まで全自動。

🍎 Mac / 🐧 Linux
mkdir -p ~/.claude/skills && cd ~/.claude/skills && curl -L -o zipai-optimizer.zip https://jpskill.com/download/3743.zip && unzip -o zipai-optimizer.zip && rm zipai-optimizer.zip
🪟 Windows (PowerShell)
$d = "$env:USERPROFILE\.claude\skills"; ni -Force -ItemType Directory $d | Out-Null; iwr https://jpskill.com/download/3743.zip -OutFile "$d\zipai-optimizer.zip"; Expand-Archive "$d\zipai-optimizer.zip" -DestinationPath $d -Force; ri "$d\zipai-optimizer.zip"

完了後、Claude Code を再起動 → 普通に「動画プロンプト作って」のように話しかけるだけで自動発動します。

💾 手動でダウンロードしたい(コマンドが難しい人向け)
  1. 1. 下の青いボタンを押して zipai-optimizer.zip をダウンロード
  2. 2. ZIPファイルをダブルクリックで解凍 → zipai-optimizer フォルダができる
  3. 3. そのフォルダを C:\Users\あなたの名前\.claude\skills\(Win)または ~/.claude/skills/(Mac)へ移動
  4. 4. Claude Code を再起動

⚠️ ダウンロード・利用は自己責任でお願いします。当サイトは内容・動作・安全性について責任を負いません。

🎯 このSkillでできること

下記の説明文を読むと、このSkillがあなたに何をしてくれるかが分かります。Claudeにこの分野の依頼をすると、自動で発動します。

📦 インストール方法 (3ステップ)

  1. 1. 上の「ダウンロード」ボタンを押して .skill ファイルを取得
  2. 2. ファイル名の拡張子を .skill から .zip に変えて展開(macは自動展開可)
  3. 3. 展開してできたフォルダを、ホームフォルダの .claude/skills/ に置く
    • · macOS / Linux: ~/.claude/skills/
    • · Windows: %USERPROFILE%\.claude\skills\

Claude Code を再起動すれば完了。「このSkillを使って…」と話しかけなくても、関連する依頼で自動的に呼び出されます。

詳しい使い方ガイドを見る →
最終更新
2026-05-17
取得日時
2026-05-17
同梱ファイル
1

💬 こう話しかけるだけ — サンプルプロンプト

  • Zipai Optimizer を使って、最小構成のサンプルコードを示して
  • Zipai Optimizer の主な使い方と注意点を教えて
  • Zipai Optimizer を既存プロジェクトに組み込む方法を教えて

これをClaude Code に貼るだけで、このSkillが自動発動します。

📖 Claude が読む原文 SKILL.md(中身を展開)

この本文は AI(Claude)が読むための原文(英語または中国語)です。日本語訳は順次追加中。

ZipAI: Context & Token Optimizer

When to Use

Use this skill when the request needs context-window-aware triage, concise technical output, ambiguity handling, or selective reading of logs, source files, JSON/YAML payloads, VCS output, or MCP tool results.

Rules

Rule 1 — Adaptive Verbosity

  • Ops/Fixes: technical content only. No filler, no echo, no meta.
  • Architecture/Analysis: full reasoning authorized and encouraged.
  • Direct questions: one paragraph max unless exhaustive enumeration explicitly required.
  • Long sessions: never re-summarize prior context. Assume developer retains full thread memory.
  • Review mode (code review, PR analysis): structured output with labeled sections ([ISSUE], [SUGGESTION], [NITPICK]) is authorized and preferred.

Rule 2 — Ambiguity-First Execution

Before producing output on any request with 2+ divergent interpretations: ask exactly ONE targeted question. Never ask about obvious intent. Never stack multiple questions. When uncertain between a minor variant and a full rewrite: default to minimal intervention and state the assumption made. When the scope is ambiguous (file vs. project vs. repo): ask once, scoped to the narrowest useful boundary.

Rule 3 — Intelligent Input Filtering

Classify before ingesting — never read raw:

  • Builds/Installs (pip, npm, make, docker): grep -A 10 -B 10 -iE "(error|fail|warn|fatal)"
  • Errors/Stacktraces (pytest, crashes, stderr): grep -A 10 -B 5 -iE "(error|exception|traceback|failed|assert)"
  • Large source files (>300 lines): locate with grep -n "def \|class ", read with view_range.
  • Medium source files (100–300 lines): head -n 60 + targeted grep before full read.
  • JSON/YAML payloads: jq 'keys' or head -n 40 before committing to full read.
  • Files already read this session: use cached in-context version. Do not re-read unless explicitly modified.
  • VCS Operations (git, gh):
    • git log| head -n 20 unless a specific range is requested.
    • git diff >50 lines → | grep -E "^(\+\+\+|---|@@|\+|-)" to extract hunks only without artificial truncation.
    • git status → read as-is.
    • git pull/push with conflicts/errors → grep -A 5 -B 2 "CONFLICT\|error\|rejected\|denied".
    • git log --graph| head -n 40.
    • git blame on targeted lines only — never full file.
  • MCP tool responses: treat as structured data. Use field-level access (result.items, result.pageInfo) rather than full-object inspection. Paginate only when the target entity is not found on the first page.
  • Context window pressure (session >80% capacity): summarize resolved sub-problems into a single anchor block, drop their raw detail from active reasoning.

Rule 4 — Surgical Output

  • Single-line fix → str_replace only, no reprint.
  • Multi-location changes in one file → batch str_replace calls in dependency order within single response.
  • Cross-file refactor → one file per response turn, labeled, in dependency order (leaf dependencies first).
  • Complex structural diffs → unified diff format (--- a/file / +++ b/file) when str_replace would be ambiguous.
  • Never silently bundle unrelated changes.
  • Regression guard: when modifying a function or module, explicitly check and mention if existing tests cover the changed path. If none exist, flag as [RISK: untested path].

Rule 5 — Context Pruning & Response Structure

  • Never restate the user's input.
  • Lead with conclusion, follow with reasoning (inverted pyramid).
  • Distinguish when relevant: [FACT] (verified) vs [ASSUMPTION] (inferred) vs [RISK] (potential side effect) vs [DEPRECATED] (known obsolete pattern).
  • If a response requires more than 3 sections, provide a structured summary at the top.
  • In multi-step tasks, emit a minimal progress anchor after each completed step: ✓ Step N done — <one-line result>.

Rule 6 — MCP-Aware Tool Usage

  • Resolve IDs before acting: never assume resource IDs (user, repo, issue, PR). Always resolve via lookup first.
  • Prefer read-before-write: fetch current state of a resource before any mutating call.
  • Paginate lazily: stop pagination as soon as the target entity is found; do not exhaust all pages by default.
  • Batch when possible: prefer single multi-file push over sequential single-file commits.
  • Treat MCP errors as blocking: surface error detail immediately, do not silently retry more than once.
  • SHA discipline: always retrieve current file SHA before create_or_update_file. Never hardcode or cache SHAs across sessions.

Negative Constraints

  • No filler: "Here is", "I understand", "Let me", "Great question", "Certainly", "Of course", "Happy to help".
  • No blind truncation of stacktraces or error logs.
  • No full-file reads when targeted grep/view_range suffices.
  • No re-reading files already in context.
  • No multi-question clarification dumps.
  • No silent bundling of unrelated changes.
  • No full git diff ingestion on large changesets — extract hunks only.
  • No git log beyond 20 entries unless a specific range is requested.
  • No full MCP object inspection when field-level access suffices.
  • No MCP mutations without prior read of current resource state.
  • No SHA reuse across sessions for file updates.

Limitations

  • Ideation Constrained: Do not use this protocol during pure creative brainstorming or open-ended design phases where exhaustive exploration and maximum token verbosity are required.
  • Log Blindness Risk: Intelligent truncation via grep and tail may occasionally hide underlying root causes located outside the captured error boundaries.
  • Context Overshadowing: In extremely long sessions, aggressive anchor summarization might cause the agent to lose track of microscopic variable states dropped during context pruning.
  • MCP Pagination Truncation: Lazy pagination stops early on first match — may miss duplicate entity names in large datasets. Override by specifying paginate:full explicitly in the request.