Compétences vertical-industry-data-team

📦

vertical-industry-data-team

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

Révision du contenu r1 Sûr 📁 Accès au système de fichiers⚙️ Commandes externes🌐 Accès réseau

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.

Prend en charge: Claude Codex Code(CC)

🥉 77 Bronze

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Installer avec mon Agent

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Demande d’agent

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.

Votre Agent doit toujours présenter son plan et demander toute confirmation exigée par la politique de sécurité.

Ressources lisibles par les agents

Utilisez ces liens lorsqu’un Agent IA, un crawler ou un script a besoin d’un contexte propre au lieu de lire toute la page.

Détail Markdown GET /skills/dhturing-vertical-industry-data-team.md manifest signé GET /api/skills/dhturing-vertical-industry-data-team/manifest lockfile signé GET /api/skills/dhturing-vertical-industry-data-team/lockfile

Tester

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

Résultat attendu:

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

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

Résultat attendu:

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

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

Résultat attendu:

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.

Audit de sécurité

Sûr

v1 • 27/09/2026 Ouvrir le rapport versionné

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.

Fichiers analysés

275

Lignes analysées

Éléments à examiner

Faux positifs ignorés

Facteurs de risque

📁 Accès au système de fichiers (2)

profile.md:38 profile.md:38

⚙️ Commandes externes (2)

SKILL.md:20 SKILL.md:31

🌐 Accès réseau (2)

SKILL.md:6 SKILL.md:12

Le dernier audit statique et sémantique terminé n’a détecté aucun constat de sécurité confirmé. Cela ne prouve pas que le skill est sans effets secondaires.

Audité par: codex

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Ouvrir le rapport versionné

Copier le lien du rapport

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

Badge Markdown

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Carte intégrable

<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>

Citations académiques (APA · BibTeX · CFF)

Citation 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

Citation 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"

Score Skillstore

Pourquoi ce score Niveau de confiance des preuves: Moyen

Architecture

Maintenabilité

Contenu

Communauté

Conformité aux spécifications

Ce que vous pouvez créer

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.

Essayez ces prompts

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.

Bonnes pratiques

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.

Éviter

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.

Foire aux questions

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.