Borrowing it
Nothing to install: this file belongs to kanazawazawa/copilot-agent-workspace. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/kanazawazawa/copilot-agent-workspace/master/.github/skills/ms-learn-research/SKILL.mdgit clone --depth 1 https://github.com/kanazawazawa/copilot-agent-workspaceWrote 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/kanazawazawa/copilot-agent-workspace/ms-learn-research)<a href="https://agentmods.dev/skills/kanazawazawa/copilot-agent-workspace/ms-learn-research"><img src="https://agentmods.dev/badge/skills/kanazawazawa/copilot-agent-workspace/ms-learn-research/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/kanazawazawa/copilot-agent-workspace/ms-learn-research"><img src="https://agentmods.dev/badge/skills/kanazawazawa/copilot-agent-workspace/ms-learn-research.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 | $0.00060 | $0.00973 |
| Opus 5 | $0.00030 | $0.00487 |
| Sonnet 5 | $0.00012 | $0.00195 |
| Haiku 4.5 | $0.00006 | $0.00097 |
Grade A, and why
ms-learn-research 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.
What it actually says
MS Learn 調査スキル
概要
Microsoft Learn（MS Learn）の公式ドキュメントを効率的に調査し、 正確で最新の Azure / Microsoft 技術情報を提供するためのスキルです。
トリガーワード
以下のキーワードが含まれる場合にこのスキルを使用する:
- 「MS Learn」「Microsoft Learn」「公式ドキュメント」
- 「Azure の仕様」「Azure の機能」
- 「サービス比較」「料金」「SLA」
- 「ベストプラクティス」「推奨構成」
使用しない場合
- 一般的なプログラミングの質問（Azure 固有でない場合）
- コードの書き方だけが求められている場合
調査ワークフロー
Step 1: 質問の明確化
調査を始める前に、以下を確認する:
- 何を調べるのか: サービス名、機能名、概念
- なぜ調べるのか: 提案用？設計判断？トラブルシュート？
- どの深さで: 概要レベル？詳細仕様？
Step 2: 情報収集
-
microsoft-docs MCP を最初に使う
microsoft_docs_searchで関連ドキュメントを検索- 検索クエリは英語で行うと精度が高い
- 例:
"Azure Functions pricing tiers comparison"
-
必要に応じて詳細取得
microsoft_docs_fetchで特定ページの全文を取得- チュートリアル、前提条件、詳細な手順が必要な場合
-
コードサンプルが必要な場合
microsoft_code_sample_searchで公式サンプルを検索
Step 3: 情報の整理
以下のフォーマットで回答を構造化する:
## [調査テーマ]
### 概要
- 1-2 文で要約
### 詳細
- 箇条書きで主要ポイント
- 表で比較情報
### 参考情報
- [ドキュメントタイトル](URL) — 要約
出力ルール
保存先
- 調査結果を保存する場合:
output/research/YYYY-MM-DD_HHmmss_テーマ名/report.md - 例:
output/research/2026-03-03_143052_cosmos-db-比較/report.md
品質基準
- 日本語で回答する
- 技術用語は英語のまま使用してよい
- 情報源 URL を必ず添える
- 確認できなかった情報は「※未確認」と明記する
- 料金情報は変動するため、参照日と「最新情報は公式サイトで確認」の注記を添える
サービス比較テンプレート
サービス比較を依頼された場合は以下の形式を使う:
| 項目 | サービスA | サービスB |
|---|---|---|
| 概要 | ||
| 主な用途 | ||
| 料金体系 | ||
| SLA | ||
| リージョン | ||
| 制限事項 |
よくある調査パターン
パターン1: サービスの概要調査
→ 検索 → 概要ページ取得 → 要約
パターン2: 2つのサービスの比較
→ 両方を検索 → 比較表作成 → 使い分け指針を提示
パターン3: 特定機能の詳細確認
→ 検索 → 該当ページ全文取得 → 手順・制約・注意点を抽出
パターン4: ベストプラクティス・推奨構成
→ "best practices" で検索 → Well-Architected Framework 参照 → まとめ
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 · 109 lines · 60 tokens per session scan A e18578972743
ms-learn-research is a skill published in the GitHub repository kanazawazawa/copilot-agent-workspace (5 stars, last pushed 3mo ago), licensed MIT. It adds 60 tokens to every session and 973 once invoked, about $0.0003 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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