Skills vertical-industry-data-expert

📦

vertical-industry-data-expert

Name: vertical-industry-data-expert
Author: 图灵数盟

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

Plan Industry-Specific Data Asset Strategies

Generic data asset plans can miss sector rules, data ownership, buyer needs, and approval steps. This skill organizes industry selection, data assessment, compliance screening, commercialization scenarios, and repeatable delivery plans.

Supports: Claude Codex Code(CC)

🥉 77 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 "vertical-industry-data-expert" from https://skillstore.io/skills/dhturing-vertical-industry-data-expert.md and its manifest at https://skillstore.io/api/skills/dhturing-vertical-industry-data-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-vertical-industry-data-expert.md Signed manifest GET /api/skills/dhturing-vertical-industry-data-expert/manifest Signed lockfile GET /api/skills/dhturing-vertical-industry-data-expert/lockfile

Test it

Using "vertical-industry-data-expert". Assess whether a hospital imaging dataset is ready for commercialization.

Expected outcome:

Readiness: conditional, pending ownership, patient authorization, de-identification, and sector approval review.
Priority evidence: data inventory, notices, consent records, rights agreements, security controls, and permitted-use records.
Next step: obtain specialist review before selecting a buyer or valuation approach.

Using "vertical-industry-data-expert". Turn a charging-network data project into a replication plan.

Expected outcome:

Standardize the data inventory, metrics, compliance checks, buyer profile, and acceptance criteria.
Separate reusable methods from client-specific contracts, infrastructure, permissions, and financial assumptions.
Validate the package with a second customer before wider rollout.

Security Audit

Safe

v1 • 9/27/2026 Open versioned report

All 12 static findings are false positives. The flagged paths, backticks, and URLs are documentation syntax or passive metadata, while the entropy alerts reflect readable multilingual Markdown. No prompt injection, data-exfiltration intent, or executable behavior was found.

Files scanned

505

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: claude

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Open versioned report

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

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Academic citations (APA · BibTeX · CFF)

APA citation

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

BibTeX citation

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

Prioritize a Data-Rich Sector

Compare candidate industries and select one or two sectors for focused data asset development.

Screen a Sector Data Project

Identify data traits, ownership questions, sensitive data, regulations, approvals, and evidence gaps before design.

Replicate a Benchmark Project

Turn one delivery into reusable materials, validation criteria, buyer messaging, and a rollout sequence.

Try These Prompts

Describe an Industry Data Opportunity

Analyze the data opportunity in [industry]. List data types, producers, update frequency, quality concerns, and value scenarios. State your assumptions.

Create a Compliance Screening Checklist

Review this [industry] data project in [jurisdiction]. Identify sensitive data, ownership questions, sector rules, approvals, evidence gaps, and required specialist reviews.

Map Scenarios and Buyers

For [data assets], rank commercial scenarios by value and feasibility. For each scenario, describe buyers, budget owners, decision makers, procurement routes, and proof requirements.

Design a Benchmark and Replication Plan

Design a benchmark project for [industry and client]. Include phases, inputs, controls, deliverables, acceptance criteria, dependencies, risks, and a replication plan.

Best Practices

Provide the industry, jurisdiction, data sources, rights, intended use, and available evidence.
Treat compliance and ownership as decision gates before valuation or commercialization.
Validate recommendations with current regulations, primary evidence, and qualified sector specialists.

Avoid

Do not reuse one generic data asset plan across industries with different regulatory obligations.
Do not present scenario assumptions, estimated values, or cited internal cases as verified facts.
Do not start commercialization while ownership, consent, approval, or data-quality questions remain unresolved.

Frequently Asked Questions

Which industries does this skill cover?

It provides guidance for healthcare, agriculture, energy, education, carbon, trade finance, commodities, entertainment, consumer data, and related sectors.

Can it choose the best industry for my organization?

It can structure and score the decision. Final selection needs verified resources, market evidence, budgets, competition, and organizational priorities.

Does it provide legal or regulatory approval?

No. It identifies review areas and evidence gaps. Qualified specialists and authorities must confirm requirements.

Can it value a dataset?

It can prepare industry context and value scenarios. Defensible valuation requires verified financial evidence, suitable methods, and qualified professionals.

Does the package include the cited internal cases?

No. The knowledge base refers to internal materials, but the underlying documents and datasets are not included.

What information produces the best result?

Provide the industry, jurisdiction, data inventory, ownership evidence, collection basis, intended uses, buyer hypotheses, and known regulatory constraints.