Навыки vertical-industry-data-team
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vertical-industry-data-team

Ревизия содержимого r1 Безопасно 📁 Доступ к файловой системе⚙️ Внешние команды🌐 Доступ к сети

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.

Поддерживает: Claude Codex Code(CC)
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Запрос агента
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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Ресурсы для AI-агентов

Используйте эти ссылки, когда AI-агенту, crawler или script нужен чистый контекст вместо полной страницы.

Протестировать

Использование «vertical-industry-data-team». Can charging-network usage data support an assetization project?

Ожидаемый результат:

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

Использование «vertical-industry-data-team». How should a city plan a five-year public-data authorization?

Ожидаемый результат:

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

Использование «vertical-industry-data-team». Can verified emissions reductions be marketed as official carbon credits?

Ожидаемый результат:

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.

Аудит безопасности

Безопасно

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.

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Пункты проверки
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Ложные срабатывания проигнорированы

Факторы риска

📁 Доступ к файловой системе (2)
⚙️ Внешние команды (2)
🌐 Доступ к сети (2)
Последний завершенный статический и семантический аудит не обнаружил подтвержденных проблем безопасности. Это не доказывает отсутствие побочных эффектов у навыка.
Аудитор:: codex
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Оценка безопасности

Копировать ссылку на отчёт

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

Значок Markdown

[![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)

Значок HTML

<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>

Встраиваемая карточка

<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>
Академические ссылки (APA · BibTeX · CFF)

Цитата APA

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

Цитата BibTeX

@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"

Оценка Skillstore

Почему такая оценка Достоверность доказательств: Средний
55
Архитектура
85
Сопровождаемость
87
Контент
65
Сообщество
83
Соответствие спецификации

Что вы можете построить

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.

Попробуйте эти промпты

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.

Лучшие практики

  • 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.

Избегать

  • 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.

Часто задаваемые вопросы

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.

Сведения для разработчиков

Автор

图灵数盟

Лицензия

MIT

Ревизия Skillstore

r1

Примечание о версии

Автор не указал версию.

Ссылка

50b73f83991cf073cbdf3f5afad2bbac71dd3bae

Актуальность поддержки

30.09.2026

Использование

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