技能 vertical-industry-data-team

📦

vertical-industry-data-team

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

内容修订 r1 安全 📁 文件系统访问⚙️ 外部命令🌐 网络访问

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.

支持: Claude Codex Code(CC)

🥉 77 青铜

使用我的智能体安装

将此请求复制给你的智能体，其中包含权威 Skill 页面和清单。

智能体请求

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.

你的智能体仍应展示其计划，并按安全策略请求必要确认。

Agent 可读资源

当 AI Agent、爬虫或脚本需要干净上下文，而不是读取完整页面时，请使用这些链接。

Markdown 详情 GET /skills/dhturing-vertical-industry-data-team.md 已签名 manifest GET /api/skills/dhturing-vertical-industry-data-team/manifest 已签名 lockfile GET /api/skills/dhturing-vertical-industry-data-team/lockfile

测试它

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

预期结果:

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?

预期结果:

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?

预期结果:

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.

安全审计

安全

v1 • 2026/9/27 打开版本化报告

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.

已扫描文件

275

分析行数

复核项

已忽略误报

风险因素

📁 文件系统访问 (2)

profile.md:38 profile.md:38

⚙️ 外部命令 (2)

SKILL.md:20 SKILL.md:31

🌐 网络访问 (2)

SKILL.md:6 SKILL.md:12

最新一次完成的静态与语义审计未检测到已确认的安全发现。这并不能证明该 Skill 没有副作用。

审计者: codex

分享与引用此报告

分享版本化评估报告、中性徽章、嵌入卡片和引用。Skillstore 只报告证据，不判断该 Skill 是否安全。

打开版本化报告

复制报告链接

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>

学术引用 (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 评分

评分依据证据置信度: 中

架构

可维护性

内容

社区

规范符合性

你能构建什么

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.

试试这些提示

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.

最佳实践

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.

常见问题

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.