Fähigkeiten vertical-industry-data-team

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vertical-industry-data-team

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

Inhaltsrevision r1 Sicher 📁 Dateisystemzugriff⚙️ Externe Befehle🌐 Netzwerkzugriff

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.

Unterstützt: Claude Codex Code(CC)

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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.

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Testen

„vertical-industry-data-team“ wird verwendet. Can charging-network usage data support an assetization project?

Erwartetes Ergebnis:

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

„vertical-industry-data-team“ wird verwendet. How should a city plan a five-year public-data authorization?

Erwartetes Ergebnis:

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

„vertical-industry-data-team“ wird verwendet. Can verified emissions reductions be marketed as official carbon credits?

Erwartetes Ergebnis:

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.

Sicherheitsaudit

Sicher

v1 • 27.9.2026 Versionsbericht öffnen

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.

Gescannte Dateien

275

Analysierte Zeilen

Prüfelemente

Falschmeldungen ignoriert

Risikofaktoren

📁 Dateisystemzugriff (2)

profile.md:38 profile.md:38

⚙️ Externe Befehle (2)

SKILL.md:20 SKILL.md:31

🌐 Netzwerkzugriff (2)

SKILL.md:6 SKILL.md:12

Das letzte abgeschlossene statische und semantische Audit erkannte keine bestätigten Sicherheitsbefunde. Dies beweist nicht, dass der Skill keine Nebenwirkungen hat.

Geprüft von: codex

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Berichtslink kopieren

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

Markdown-Badge

[![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-Badge

<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>

Einbettungskarte

<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>

Wissenschaftliche Zitate (APA · BibTeX · CFF)

APA-Zitat

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-Zitat

@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-Score

Warum dieser Score Evidenzvertrauen: Mittel

Architektur

Wartbarkeit

Inhalt

Gemeinschaft

Spezifikationskonformität

Was Sie erstellen können

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.

Diese Prompts ausprobieren

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.

Bewährte Praktiken

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.

Vermeiden

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.

Häufig gestellte Fragen

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.