Fähigkeiten data-scenario-mining-expert

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data-scenario-mining-expert

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

Inhaltsrevision r1 Sicher 📁 Dateisystemzugriff⚙️ Externe Befehle🌐 Netzwerkzugriff

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.

Unterstützt: Claude Codex Code(CC)

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Mit meinem Agent installieren

Kopieren Sie diese Anfrage in Ihren Agent. Sie enthält die maßgebliche Skill-Seite und das Manifest.

Agent-Anfrage

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.

Ihr Agent sollte weiterhin seinen Plan anzeigen und alle von der Sicherheitsrichtlinie verlangten Bestätigungen anfordern.

Agent-lesbare Ressourcen

Verwenden Sie diese Links, wenn ein KI-Agent, Crawler oder Skript sauberen Kontext benötigt, statt die vollständige Seite zu lesen.

Markdown-Detailseite GET /skills/dhturing-data-scenario-mining-expert.md Signiertes manifest GET /api/skills/dhturing-data-scenario-mining-expert/manifest Signiertes lockfile GET /api/skills/dhturing-data-scenario-mining-expert/lockfile

Testen

„data-scenario-mining-expert“ wird verwendet. We have regional weather, irrigation, and crop yield data. Find valuable scenarios beyond internal reporting.

Erwartetes Ergebnis:

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.

„data-scenario-mining-expert“ wird verwendet. Rank demand forecasting, insurance risk scoring, and academic research access for our mobility data.

Erwartetes Ergebnis:

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.

„data-scenario-mining-expert“ wird verwendet. Create a validation plan for an energy benchmarking data product.

Erwartetes Ergebnis:

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

Sicherheitsaudit

Sicher

v1 • 27.9.2026 Versionsbericht öffnen

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.

Gescannte Dateien

436

Analysierte Zeilen

Prüfelemente

Falschmeldungen ignoriert

Risikofaktoren

📁 Dateisystemzugriff (2)

profile.md:38 profile.md:38

⚙️ Externe Befehle (3)

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

🌐 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

Diesen Bericht teilen & zitieren

Teile den versionierten Bewertungsbericht, das neutrale Badge, die Einbettungskarte und Zitate. Skillstore berichtet Nachweise, ohne zu entscheiden, ob dieser Skill sicher ist.

Versionsbericht öffnen

Berichtslink kopieren

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

Markdown-Badge

[![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)

HTML-Badge

<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>

Einbettungskarte

<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>

Wissenschaftliche Zitate (APA · BibTeX · CFF)

APA-Zitat

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

BibTeX-Zitat

@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"

Skillstore-Score

Warum dieser Score Evidenzvertrauen: Mittel

Architektur

Wartbarkeit

Inhalt

Gemeinschaft

Spezifikationskonformität

Was Sie erstellen können

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.

Diese Prompts ausprobieren

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.

Bewährte Praktiken

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.

Vermeiden

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.