Skills data-scenario-mining-expert

📦

data-scenario-mining-expert

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

Content revision r1 Safe 📁 Filesystem access⚙️ External commands🌐 Network access

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.

Supports: Claude Codex Code(CC)

🥉 78 Bronze

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request

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.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/dhturing-data-scenario-mining-expert.md Signed manifest GET /api/skills/dhturing-data-scenario-mining-expert/manifest Signed lockfile GET /api/skills/dhturing-data-scenario-mining-expert/lockfile

Test it

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

Expected outcome:

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.

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

Expected outcome:

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.

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

Expected outcome:

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

Security Audit

Safe

v1 • 9/27/2026 Open versioned report

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.

Files scanned

436

Lines analyzed

Review items

False positives ignored

Risk Factors

📁 Filesystem access (2)

profile.md:38 profile.md:38

⚙️ External commands (3)

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

🌐 Network access (2)

SKILL.md:6 SKILL.md:12

No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.

Audited by: codex

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Academic citations (APA · BibTeX · CFF)

APA citation

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 citation

@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

Why this score Evidence Confidence: Medium

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

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.

Try These 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.

Best Practices

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.

Avoid

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.