技能 vertical-industry-data-team

📦

vertical-industry-data-team

Name: vertical-industry-data-team
Author: 图灵数盟

內容修訂 r1 安全 📁 檔案系統存取⚙️ 外部命令🌐 網路存取

Assess Industry Data Assetization Paths

Regulated data projects often fail when generic assetization methods overlook industry rules. This skill structures compliance-first assessments for carbon, seed, energy, and public data.

支援: Claude Codex Code(CC)

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智能體請求

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

你的智慧代理仍應顯示其計畫，並依安全政策請求必要確認。

Agent 可讀資源

當 AI Agent、爬蟲或腳本需要乾淨脈絡、而不是讀取完整頁面時，請使用這些連結。

Markdown 詳情 GET /skills/dhturing-vertical-industry-data-team.md 已簽名 manifest GET /api/skills/dhturing-vertical-industry-data-team/manifest 已簽名 lockfile GET /api/skills/dhturing-vertical-industry-data-team/lockfile

測試它

正在使用「vertical-industry-data-team」。 Can charging-network usage data support an assetization project?

預期結果:

Conditional feasibility. First classify personal, operational, and grid-related data. Confirm de-identification, CII exposure, ownership, auditability, and measurable revenue before selecting a structure.

正在使用「vertical-industry-data-team」。 How should a city plan a five-year public-data authorization?

預期結果:

Use the five-step authorization process and document scope, operator selection, security duties, product boundaries, and revenue allocation. Track the shorter registration renewal separately.

正在使用「vertical-industry-data-team」。 Can verified emissions reductions be marketed as official carbon credits?

預期結果:

Not automatically. Verify the approved methodology, MRV evidence, validation path, crediting period, registration status, and issuance estimate. Keep commercial carbon claims distinct from official CCER.

安全審計

安全

v1 • 2026/9/27 開啟版本化報告

All twelve static findings are false positives caused by documentation examples, Markdown formatting, public website links, and UTF-8 Chinese prose. No executable payload, hidden file operation, unauthorized network request, prompt injection, or data-exfiltration intent was found.

已掃描檔案

275

分析行數

複核項目

已忽略的誤報

風險因素

📁 檔案系統存取 (2)

profile.md:38 profile.md:38

⚙️ 外部命令 (2)

SKILL.md:20 SKILL.md:31

🌐 網路存取 (2)

SKILL.md:6 SKILL.md:12

最近一次完成的靜態與語意稽核未偵測到已確認的安全發現。這並不能證明該 Skill 沒有副作用。

審計單位: codex

分享與引用此報告

分享版本化評估報告、中性徽章、嵌入卡片與引用。Skillstore 只報告證據，不判斷該 Skill 是否安全。

開啟版本化報告

複製報告連結

https://skillstore.io/skills/dhturing-vertical-industry-data-team/audits/1?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

Markdown 徽章

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

HTML 徽章

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

嵌入卡片

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

學術引用 (APA · BibTeX · CFF)

APA 引用

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

BibTeX 引用

@techreport{dhturing-vertical-industry-data-team-2026,
  author = {图灵数盟},
  title = {vertical-industry-data-team security audit report (audit version 1)},
  institution = {Skillstore},
  year = {2026},
  number = {1},
  url = {https://skillstore.io/skills/dhturing-vertical-industry-data-team/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: "vertical-industry-data-team 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-vertical-industry-data-team/audits/1"
identifiers:
  - type: other
    value: "skillstore:dhturing-vertical-industry-data-team:audit:1"
    description: "Skillstore immutable audit report identifier"

Skillstore 評分

評分依據證據置信度: 中

架構

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你可以打造什麼

Screen a Carbon Data Project

Assess MRV readiness, CCER boundaries, evidence gaps, and possible assetization paths before commissioning specialist work.

Evaluate Energy Data Commercialization

Classify storage or charging data, identify CII and important-data concerns, and test whether cash flows are measurable.

Plan Public Data Authorization

Structure the authorization process, required deliverables, term controls, operating boundaries, and renewal reminders for a regional project.

試試這些提示

Identify the Industry Gates

Review this [industry] data project. List its data types, main compliance gates, missing evidence, and the next three actions.

Compare Assetization Routes

Compare accounting recognition, data licensing, authorized operation, and structured revenue rights for [project]. Explain eligibility, blockers, evidence, and tradeoffs.

Build a Cross-Role Delivery Plan

Create a phased delivery plan for [project]. Assign specialist responsibilities, required inputs, deliverables, quality checks, decision gates, and escalation points.

Challenge an Investment Proposal

Stress-test this [industry] data investment proposal. Separate verified facts from assumptions, test regulatory claims, assess measurable cash flows, and recommend proceed, pause, or reject.

最佳實務

Provide the jurisdiction, project stage, data inventory, participants, intended buyers, and available evidence.
Confirm current policies with official sources before relying on dates, limits, or approval requirements.
Keep regulatory rights, data rights, intellectual property, and revenue rights separate throughout the assessment.

避免

Do not treat a generic data assetization process as sufficient for a regulated industry.
Do not present commercial tokens, revenue rights, or private certificates as official regulatory instruments.
Do not infer ownership, approval, or financial value when supporting evidence is missing.

常見問題

Which industries does this skill cover?

It covers carbon, seed, power and energy storage, and public data authorization scenarios.

Does it provide legal or regulatory approval?

No. It organizes issues and evidence but does not replace qualified advisers or competent authorities.

Can it calculate a final data asset valuation?

No. It can identify valuation inputs and cash-flow questions, but specialists must validate assumptions and issue formal opinions.

What information should I provide?

Provide the jurisdiction, data inventory, participants, project stage, proposed transaction, available approvals, and supporting evidence.

Can it compare several industries?

Yes. It can compare compliance gates, ownership complexity, buyers, commercialization routes, and required deliverables across the four covered sectors.

How current is the regulatory guidance?

The source states a review date of September 23, 2026. Verify every policy and deadline against current official publications.