Навыки data-scenario-mining-expert

📦

data-scenario-mining-expert

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

Ревизия содержимого r1 Безопасно 📁 Доступ к файловой системе⚙️ Внешние команды🌐 Доступ к сети

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.

Поддерживает: Claude Codex Code(CC)

🥉 78 Бронза

Maintain this Skill? Claim it 🥉 78 Бронза

Установить с помощью моего Агента

Скопируйте этот запрос в своего Агента. Он содержит каноническую страницу Skill и манифест.

Запрос агента

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.

Ваш Агент по-прежнему должен показать план и запросить все подтверждения, требуемые политикой безопасности.

Ресурсы для AI-агентов

Используйте эти ссылки, когда AI-агенту, crawler или script нужен чистый контекст вместо полной страницы.

Markdown-детали GET /skills/dhturing-data-scenario-mining-expert.md Подписанный manifest GET /api/skills/dhturing-data-scenario-mining-expert/manifest Подписанный lockfile GET /api/skills/dhturing-data-scenario-mining-expert/lockfile

Протестировать

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

Ожидаемый результат:

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.

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

Ожидаемый результат:

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.

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

Ожидаемый результат:

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

Аудит безопасности

Безопасно

v1 • 27.09.2026 Открыть версионный отчет

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.

Просканировано файлов

436

Проанализировано строк

Пункты проверки

Ложные срабатывания проигнорированы

Факторы риска

📁 Доступ к файловой системе (2)

profile.md:38 profile.md:38

⚙️ Внешние команды (3)

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

🌐 Доступ к сети (2)

SKILL.md:6 SKILL.md:12

Последний завершенный статический и семантический аудит не обнаружил подтвержденных проблем безопасности. Это не доказывает отсутствие побочных эффектов у навыка.

Аудитор:: codex

Поделиться и цитировать этот отчет

Делитесь версионным отчетом об оценке, нейтральным значком, встраиваемой карточкой и цитатами. Skillstore публикует доказательства, не решая, безопасен ли этот Skill.

Открыть версионный отчет

Копировать ссылку на отчёт

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

Значок 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)

Значок 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>

Встраиваемая карточка

<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>

Академические ссылки (APA · BibTeX · CFF)

Цитата 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

Цитата 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"

Оценка Skillstore

Почему такая оценка Достоверность доказательств: Средний

Архитектура

Сопровождаемость

Контент

Сообщество

Соответствие спецификации

Что вы можете построить

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.

Попробуйте эти промпты

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.

Лучшие практики

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.

Избегать

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.