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/customer-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/customer-research)<a href="https://agentmods.dev/skills/kanazawazawa/copilot-agent-workspace/customer-research"><img src="https://agentmods.dev/badge/skills/kanazawazawa/copilot-agent-workspace/customer-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/customer-research"><img src="https://agentmods.dev/badge/skills/kanazawazawa/copilot-agent-workspace/customer-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 | $0.00050 | $0.01029 |
| Opus 5 | $0.00025 | $0.00515 |
| Sonnet 5 | $0.00010 | $0.00206 |
| Haiku 4.5 | $0.00005 | $0.00103 |
Grade A, and why
customer-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
顧客情報調査スキル
概要
顧客企業や業界に関する情報を効率的に調査し、 提案活動やプリセールス資料の作成に役立つインサイトを提供するスキルです。
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:
- 「顧客情報」「顧客調査」「企業調査」
- 「業界動向」「業界調査」「市場調査」
- 「導入事例」「ケーススタディ」「事例」
- 「競合分析」「提案準備」
使用しない場合
- 技術仕様の確認のみ(→ ms-learn-research を使う)
- 社内プロジェクトの情報整理
調査ワークフロー
Step 1: 調査目的の確認
- 対象顧客: 企業名、業界
- 調査の目的: 初回提案?深掘り?更新?
- 必要な情報: 企業概要?IT戦略?競合?事例?
Step 2: 情報収集
-
企業の基本情報
- Web 検索で企業概要、事業内容、最新ニュースを取得
- IR 情報(上場企業の場合)
-
業界動向
- 業界レポート、トレンド記事を検索
- DX 動向、クラウド採用状況
-
Microsoft / Azure 関連
microsoft_docs_searchで導入事例を検索- クエリ例:
"case study [業界名] Azure" microsoft_docs_searchで業界ソリューションを検索
-
技術的なコンテキスト
- 競合クラウド(AWS, GCP)との比較ポイント
- 導入済みの主要システム情報(公開情報ベース)
Step 3: 整理とアウトプット
出力テンプレート
企業概要テンプレート
## [企業名] 調査サマリー
### 基本情報
| 項目 | 内容 |
|------|------|
| 企業名 | |
| 業界 | |
| 従業員数 | |
| 売上規模 | |
| 主な事業 | |
### IT / DX の取り組み
- [公開情報ベースで記載]
### Microsoft との関係
- 既存導入製品(公開情報ベース)
- 類似業界の Azure 導入事例
### 提案の方向性(仮説)
- ビジネス課題 → 技術ソリューション候補
業界概要テンプレート
## [業界名] 概要
### 業界トレンド
- 直近のキートレンド 3-5 点
### DX / クラウド動向
- クラウド採用率、主要な利用領域
### よくある課題
- 業界共通のIT課題
### Azure ソリューション候補
| 課題 | Azure サービス | 概要 |
|------|---------------|------|
出力ルール
保存先
- 企業調査:
output/customers/企業名/profile.md(最新の企業概要) - テーマ別調査:
output/customers/企業名/YYYY-MM-DD_HHmmss_調査テーマ.md - 業界調査:
output/research/YYYY-MM-DD_HHmmss_業界名_調査/report.md
品質基準
- 公開情報のみを使用する(機密情報は扱わない)
- 情報源を明記する
- 推測・仮説は「仮説:」「推定:」と明記する
- 情報の鮮度(いつの情報か)を意識する
- 数値データは元ソースを確認できるようにする
注意事項
- 個人情報は扱わない
- 非公開の顧客内部情報は記載しない
- 競合の批判ではなく、差別化ポイントとして記載する
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 · 122 lines · 50 tokens per session scan A 094ba6170fcb
customer-research is a skill published in the GitHub repository kanazawazawa/copilot-agent-workspace (5 stars, last pushed 3mo ago), licensed MIT. It adds 50 tokens to every session and 1,029 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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