vertical-industry-data-team
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.
Instalar con mi Agente
Copia esta solicitud en tu Agente. Incluye la página canónica del Skill y el manifiesto.
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.Tu Agente debe seguir mostrando su plan y solicitar cualquier confirmación exigida por la política de seguridad.
Recursos legibles por agentes
Usa estos enlaces cuando un agente de IA, crawler o script necesite contexto limpio en vez de leer la página completa.
Pruébalo
Usando "vertical-industry-data-team". Can charging-network usage data support an assetization project?
Resultado esperado:
Conditional feasibility. First classify personal, operational, and grid-related data. Confirm de-identification, CII exposure, ownership, auditability, and measurable revenue before selecting a structure.
Usando "vertical-industry-data-team". How should a city plan a five-year public-data authorization?
Resultado esperado:
Use the five-step authorization process and document scope, operator selection, security duties, product boundaries, and revenue allocation. Track the shorter registration renewal separately.
Usando "vertical-industry-data-team". Can verified emissions reductions be marketed as official carbon credits?
Resultado esperado:
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.
Auditoría de seguridad
SeguroAll 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.
Factores de riesgo
📁 Acceso al sistema de archivos (2)
⚙️ Comandos externos (2)
🌐 Acceso a red (2)
Compartir y citar este informe
Comparte el informe de evaluación versionado, la insignia neutral, la tarjeta insertable y las citas. Skillstore presenta evidencias sin decidir si este Skill es seguro.
Copiar enlace del informe
https://skillstore.io/skills/dhturing-vertical-industry-data-team/audits/1?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportInsignia Markdown
[](https://skillstore.io/skills/dhturing-vertical-industry-data-team?utm_source=security_passport_badge)Insignia 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>Tarjeta para insertar
<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>Citas académicas (APA · BibTeX · CFF)
Cita 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/1Cita 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"
Puntuación de Skillstore
Por qué esta puntuación Confianza de la evidencia: MedioLo que puedes crear
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.
Prueba estos prompts
Review this [industry] data project. List its data types, main compliance gates, missing evidence, and the next three actions.
Compare accounting recognition, data licensing, authorized operation, and structured revenue rights for [project]. Explain eligibility, blockers, evidence, and tradeoffs.
Create a phased delivery plan for [project]. Assign specialist responsibilities, required inputs, deliverables, quality checks, decision gates, and escalation points.
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.
Mejores prácticas
- 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.
Evitar
- 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.
Preguntas frecuentes
Which industries does this skill cover?
Does it provide legal or regulatory approval?
Can it calculate a final data asset valuation?
What information should I provide?
Can it compare several industries?
How current is the regulatory guidance?
Detalles del desarrollador
Autor
图灵数盟Licencia
MIT
Revisión de Skillstore
r1
Aviso de versión
El autor no declaró una versión.
Ref.
50b73f83991cf073cbdf3f5afad2bbac71dd3bae
Actualidad del mantenimiento
30/9/2026
Uso
0 descargas · 0 vistas
Estructura de archivos