Compétences data-scenario-mining-expert

📦

data-scenario-mining-expert

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

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

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.

Prend en charge: Claude Codex Code(CC)

🥉 78 Bronze

Maintain this Skill? Claim it 🥉 78 Bronze

Installer avec mon Agent

Copiez cette demande dans votre Agent. Elle contient la page Skill de référence et le manifeste.

Demande d’agent

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.

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-data-scenario-mining-expert.md manifest signé GET /api/skills/dhturing-data-scenario-mining-expert/manifest lockfile signé GET /api/skills/dhturing-data-scenario-mining-expert/lockfile

Tester

Utilisation de « data-scenario-mining-expert ». We have regional weather, irrigation, and crop yield data. Find valuable scenarios beyond internal reporting.

Résultat attendu:

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.

Utilisation de « data-scenario-mining-expert ». Rank demand forecasting, insurance risk scoring, and academic research access for our mobility data.

Résultat attendu:

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.

Utilisation de « data-scenario-mining-expert ». Create a validation plan for an energy benchmarking data product.

Résultat attendu:

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

Audit de sécurité

Sûr

v1 • 27/09/2026 Ouvrir le rapport versionné

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.

Fichiers analysés

436

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 (3)

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

🌐 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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Partagez le rapport d’évaluation versionné, le badge neutre, la carte intégrable et les citations. Skillstore présente les preuves sans décider si ce Skill est sûr.

Ouvrir le rapport versionné

Copier le lien du rapport

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>

Carte intégrable

<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>

Citations académiques (APA · BibTeX · CFF)

Citation 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

Citation 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"

Score Skillstore

Pourquoi ce score Niveau de confiance des preuves: Moyen

Architecture

Maintenabilité

Contenu

Communauté

Conformité aux spécifications

Ce que vous pouvez créer

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.

Essayez ces 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.

Bonnes pratiques

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.

Éviter

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.