Habilidades data-scenario-mining-expert

📦

data-scenario-mining-expert

Name: data-scenario-mining-expert
Author: 图灵数盟

Revisão do conteúdo r1 Seguro 📁 Acesso ao sistema de arquivos⚙️ Comandos externos🌐 Acesso à rede

Discover and Prioritize Data Use Scenarios

Organizations often overlook valuable uses for data they already hold. This skill scans industries, ranks opportunities, and defines evidence needed for validation.

Suporta: Claude Codex Code(CC)

🥉 78 Bronze

Instalar com meu Agente

Copie esta solicitação para o seu Agente. Ela inclui a página canônica do Skill e o manifesto.

Solicitação do agente

Review the Skillstore skill "data-scenario-mining-expert" from https://skillstore.io/skills/dhturing-data-scenario-mining-expert.md and its manifest at https://skillstore.io/api/skills/dhturing-data-scenario-mining-expert/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Seu Agente ainda deve apresentar o plano e solicitar qualquer confirmação exigida pela política de segurança.

Recursos legíveis por agentes

Use estes links quando um agente de IA, crawler ou script precisar de contexto limpo em vez de ler a página completa.

Detalhe em Markdown GET /skills/dhturing-data-scenario-mining-expert.md manifest assinado GET /api/skills/dhturing-data-scenario-mining-expert/manifest lockfile assinado GET /api/skills/dhturing-data-scenario-mining-expert/lockfile

Testar

Usando "data-scenario-mining-expert". We have regional weather, irrigation, and crop yield data. Find valuable scenarios beyond internal reporting.

Resultado esperado:

Priority scenario: farm irrigation recommendations for agricultural cooperatives.
Buyer evidence needed: named cooperative users, seasonal budget, and an agreed recommendation report.
Feasibility checks: field coverage, update frequency, farm identifiers, and permission for advisory use.

Usando "data-scenario-mining-expert". Rank demand forecasting, insurance risk scoring, and academic research access for our mobility data.

Resultado esperado:

Prioritize demand forecasting when buyer access and aggregated data are already available.
Treat insurance scoring as a high-risk scenario until authorization, fairness, and regulated-use requirements are resolved.
Keep research access as a reserve scenario with de-identification and institutional review conditions.

Usando "data-scenario-mining-expert". Create a validation plan for an energy benchmarking data product.

Resultado esperado:

Interview five facility managers, confirm two budget owners, test a sample benchmark report, verify usage rights, and stop if no paid pilot emerges.

Auditoria de segurança

Seguro

v1 • 27/09/2026 Abrir o relatório versionado

All 12 static alerts are false positives supported by direct file review. The flagged commands are Markdown formatting, the hidden path is installation documentation, the URLs are metadata links, and the entropy alerts are readable Chinese Markdown.

Arquivos analisados

436

Linhas analisadas

Itens de revisão

Falsos positivos ignorados

Fatores de risco

📁 Acesso ao sistema de arquivos (2)

profile.md:38 profile.md:38

⚙️ Comandos externos (3)

SKILL.md:20 SKILL.md:26 SKILL.md:33

🌐 Acesso à rede (2)

SKILL.md:6 SKILL.md:12

A auditoria estática e semântica concluída mais recente não detectou achados de segurança confirmados. Isso não comprova que a skill não tenha efeitos colaterais.

Auditado por: codex

Compartilhar e citar este relatório

Compartilhe o relatório de avaliação versionado, o badge neutro, o cartão incorporável e as citações. A Skillstore apresenta evidências sem decidir se este Skill é seguro.

Abrir o relatório versionado

Copiar link do relatório

https://skillstore.io/skills/dhturing-data-scenario-mining-expert/audits/1?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

Badge Markdown

[![Skillstore security assessment](https://skillstore.io/badges/skills/dhturing-data-scenario-mining-expert/security.svg)](https://skillstore.io/skills/dhturing-data-scenario-mining-expert?utm_source=security_passport_badge)

Badge HTML

<a href="https://skillstore.io/skills/dhturing-data-scenario-mining-expert?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/dhturing-data-scenario-mining-expert/security.svg" alt="Skillstore security assessment" loading="lazy"></a>

Cartão incorporável

<iframe src="https://skillstore.io/embed/skills/dhturing-data-scenario-mining-expert.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>

Citações acadêmicas (APA · BibTeX · CFF)

Citação APA

图灵数盟. (2026). data-scenario-mining-expert security audit report (audit version 1) [Author version unspecified]. Skillstore. https://skillstore.io/skills/dhturing-data-scenario-mining-expert/audits/1

Citação BibTeX

@techreport{dhturing-data-scenario-mining-expert-2026,
  author = {图灵数盟},
  title = {data-scenario-mining-expert security audit report (audit version 1)},
  institution = {Skillstore},
  year = {2026},
  number = {1},
  url = {https://skillstore.io/skills/dhturing-data-scenario-mining-expert/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: "data-scenario-mining-expert 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-data-scenario-mining-expert/audits/1"
identifiers:
  - type: other
    value: "skillstore:dhturing-data-scenario-mining-expert:audit:1"
    description: "Skillstore immutable audit report identifier"

Pontuação do Skillstore

Por que esta pontuação Confiança das evidências: Médio

Arquitetura

Manutenibilidade

Conteúdo

Comunidade

Conformidade com a especificação

O que você pode criar

Expand an enterprise data portfolio

Identify credible internal and external uses for existing operational, customer, or sensor data.

Select data products for validation

Rank candidate scenarios and choose up to three opportunities for focused market testing.

Explore cross-industry demand

Map a dataset to unfamiliar industries while recording likely buyers, value, feasibility, and authorization concerns.

Teste estes prompts

Generate an initial scenario list

Review this data inventory: [inventory]. Suggest practical use scenarios, likely users, required data fields, and assumptions that need validation.

Scan industries with ISIC

Scan these datasets across relevant ISIC sectors: [datasets]. For each opportunity, explain the problem, buyer, data contribution, and expected deliverable.

Prioritize candidate scenarios

Evaluate these scenarios: [scenarios]. Score value and feasibility using stated evidence, place each in a quadrant, and recommend three validation priorities.

Design a validation and compliance plan

For these priority scenarios: [scenarios], define buyer interviews, payment evidence, deliverable tests, data gaps, authorization checks, decision gates, and a ninety-day plan.

Boas práticas

Provide a precise data inventory including fields, time range, quality, granularity, ownership, authorization, and current uses.
Require sources for comparable cases and label every unsupported statement as an assumption.
Validate no more than three priority scenarios and include one option testable within ninety days.

Evitar

Do not equate a long scenario list with verified market demand.
Do not assign revenue estimates without a stated basis, buyer evidence, and uncertainty range.
Do not reuse data across industries before checking consent, contracts, privacy, and sector rules.