スキル data-value-assessment-expert

📦

data-value-assessment-expert

Name: data-value-assessment-expert
Author: 图灵数盟

コンテンツリビジョン r1 安全 📁 ファイルシステムへのアクセス⚙️ 外部コマンド🌐 ネットワークアクセス

Assess Data Value and Prioritize Opportunities

Organizations often overlook valuable uses for their data or cannot explain where value originates. This skill provides structured diagnosis, measurement guidance, and prioritized improvement actions.

対応: Claude Codex Code(CC)

🥉 77 ブロンズ

Maintain this Skill? Claim it 🥉 77 ブロンズ

自分のエージェントでインストール

このリクエストをエージェントにコピーしてください。正規の Skill ページとマニフェストが含まれています。

エージェントリクエスト

Review the Skillstore skill "data-value-assessment-expert" from https://skillstore.io/skills/dhturing-data-value-assessment-expert.md and its manifest at https://skillstore.io/api/skills/dhturing-data-value-assessment-expert/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

エージェントは引き続き計画を提示し、セキュリティポリシーで必要な確認を求める必要があります。

エージェントが読めるリソース

AI エージェント、クローラー、スクリプトがページ全体ではなく整理されたコンテキストを必要とする場合は、これらのリンクを使ってください。

Markdown詳細 GET /skills/dhturing-data-value-assessment-expert.md 署名済み manifest GET /api/skills/dhturing-data-value-assessment-expert/manifest 署名済み lockfile GET /api/skills/dhturing-data-value-assessment-expert/lockfile

テストする

「data-value-assessment-expert」を使用しています。 Assess customer service transcripts for overlooked value.

期待される結果:

Strongest value: cost reduction through automated issue classification.
Secondary value: product improvement from recurring complaint patterns.
Main gap: unclear consent for uses beyond service delivery.
Next action: validate permissions before testing external scenarios.

「data-value-assessment-expert」を使用しています。 Diagnose the value of weekly logistics data.

期待される結果:

The weakest factor is buyer action because interest lacks paid validation. Run two buyer interviews and a limited pilot before forecasting revenue.

「data-value-assessment-expert」を使用しています。 Prioritize three uses for equipment sensor data.

期待される結果:

First: predictive maintenance, supported by downtime records and an internal owner.
Second: warranty risk analysis, pending quality checks.
Third: external benchmarking, pending authorization and buyer validation.

セキュリティ監査

安全

v1 • 2026/9/27 バージョン別レポートを開く

All twelve static findings are false positives. The files contain normal Markdown documentation, Chinese UTF-8 prose, installation paths, inline code formatting, and author links without executable behavior, concealed payloads, or prompt injection.

スキャンされたファイル

424

解析済み行数

レビュー項目

誤検知を無視

リスク要因

📁 ファイルシステムへのアクセス (2)

profile.md:38 profile.md:38

⚙️ 外部コマンド (3)

SKILL.md:20 SKILL.md:26 SKILL.md:33

🌐 ネットワークアクセス (2)

SKILL.md:6 SKILL.md:12

最新の完了済み静的・セマンティック監査では、確認済みのセキュリティ検出事項は見つかりませんでした。これは、スキルに副作用がないことを証明するものではありません。

監査者: codex

このレポートを共有・引用

バージョン付き評価レポート、中立的なバッジ、埋め込みカード、引用を共有できます。Skillstore は証拠を報告しますが、この Skill が安全かどうかは判断しません。

バージョン別レポートを開く

レポートリンクをコピー

https://skillstore.io/skills/dhturing-data-value-assessment-expert/audits/1?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

Markdownバッジ

[![Skillstore security assessment](https://skillstore.io/badges/skills/dhturing-data-value-assessment-expert/security.svg)](https://skillstore.io/skills/dhturing-data-value-assessment-expert?utm_source=security_passport_badge)

HTMLバッジ

<a href="https://skillstore.io/skills/dhturing-data-value-assessment-expert?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/dhturing-data-value-assessment-expert/security.svg" alt="Skillstore security assessment" loading="lazy"></a>

埋め込みカード

<iframe src="https://skillstore.io/embed/skills/dhturing-data-value-assessment-expert.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>

学術引用 (APA · BibTeX · CFF)

APA形式の引用

图灵数盟. (2026). data-value-assessment-expert security audit report (audit version 1) [Author version unspecified]. Skillstore. https://skillstore.io/skills/dhturing-data-value-assessment-expert/audits/1

BibTeX形式の引用

@techreport{dhturing-data-value-assessment-expert-2026,
  author = {图灵数盟},
  title = {data-value-assessment-expert security audit report (audit version 1)},
  institution = {Skillstore},
  year = {2026},
  number = {1},
  url = {https://skillstore.io/skills/dhturing-data-value-assessment-expert/audits/1},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "data-value-assessment-expert security audit report (audit version 1)"
version: "unspecified"
type: report
authors:
  - name: "图灵数盟"
date-released: "2026-09-27"
url: "https://skillstore.io/skills/dhturing-data-value-assessment-expert/audits/1"
identifiers:
  - type: other
    value: "skillstore:dhturing-data-value-assessment-expert:audit:1"
    description: "Skillstore immutable audit report identifier"

Skillstore スコア

このスコアの理由証拠の信頼度: 中

アーキテクチャ

保守性

コンテンツ

コミュニティ

仕様準拠

作成できるもの

Map overlooked value

Review a data portfolio across nine value types and identify evidence gaps.

Prioritize product scenarios

Compare candidate data products and rank their most credible use scenarios.

Prepare investment decisions

Separate realized benefits from potential value and identify improvement priorities.

これらのプロンプトを試す

Identify value types

Review this dataset across all nine value types. For each type, state relevance, evidence, and missing information.

Diagnose value factors

Score the five value factors from one to five. Explain each score, identify the weakest factor, and recommend improvements.

Rank value scenarios

Compare these internal and external scenarios by market scale, buyer value, competition, compliance, and evidence. Rank them with stated assumptions.

Build an assessment report

Create a complete data value assessment. Include value types, five-factor diagnosis, data map, scenario ranking, measurement ranges, decay risks, and actions.

ベストプラクティス

Provide evidence for each score and label every assumption.
Separate realized benefits, validated opportunities, and speculative potential.
Complete rights and compliance checks before recommending external use.

回避

Do not treat stated buyer interest as proven willingness to pay.
Do not present potential value as current revenue or asset value.
Do not rank scenarios without considering permissions, quality, and alternatives.

よくある質問

Does this skill produce a formal asset valuation?

No. It analyzes value sources and opportunities but does not replace a licensed valuation report.

What information should I provide?

Provide data sources, fields, quality, permissions, current uses, candidate buyers, operating costs, and available performance evidence.

How are value opportunities classified?

The framework reviews nine types, including revenue, savings, efficiency, risk, decisions, financing, ecosystem, compliance, and strategic options.

Can it estimate monetary value?

It can create evidence-based ranges when sufficient inputs exist. Every estimate should state its method, assumptions, and uncertainty.

How does it find cross-industry opportunities?

It examines other industries, buyer problems, comparable uses, and complementary datasets. These opportunities still require market and compliance validation.

Can it handle confidential data?

Use summaries or approved samples. Follow organizational controls and avoid sharing personal, restricted, or proprietary records without authorization.