Skills data-value-assessment-expert

📦

data-value-assessment-expert

Name: data-value-assessment-expert
Author: 图灵数盟

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

Assess Data Value and Prioritize Opportunities

Organizations often overlook valuable uses for their data or cannot explain where value originates. This skill provides structured diagnosis, measurement guidance, and prioritized improvement actions.

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 "data-value-assessment-expert" from https://skillstore.io/skills/dhturing-data-value-assessment-expert.md and its manifest at https://skillstore.io/api/skills/dhturing-data-value-assessment-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-value-assessment-expert.md Signed manifest GET /api/skills/dhturing-data-value-assessment-expert/manifest Signed lockfile GET /api/skills/dhturing-data-value-assessment-expert/lockfile

Test it

Using "data-value-assessment-expert". Assess customer service transcripts for overlooked value.

Expected outcome:

Strongest value: cost reduction through automated issue classification.
Secondary value: product improvement from recurring complaint patterns.
Main gap: unclear consent for uses beyond service delivery.
Next action: validate permissions before testing external scenarios.

Using "data-value-assessment-expert". Diagnose the value of weekly logistics data.

Expected outcome:

The weakest factor is buyer action because interest lacks paid validation. Run two buyer interviews and a limited pilot before forecasting revenue.

Using "data-value-assessment-expert". Prioritize three uses for equipment sensor data.

Expected outcome:

First: predictive maintenance, supported by downtime records and an internal owner.
Second: warranty risk analysis, pending quality checks.
Third: external benchmarking, pending authorization and buyer validation.

Security Audit

Safe

v1 • 9/27/2026 Open versioned report

All twelve static findings are false positives. The files contain normal Markdown documentation, Chinese UTF-8 prose, installation paths, inline code formatting, and author links without executable behavior, concealed payloads, or prompt injection.

Files scanned

424

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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Open versioned report

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Academic citations (APA · BibTeX · CFF)

APA citation

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

BibTeX citation

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

Map overlooked value

Review a data portfolio across nine value types and identify evidence gaps.

Prioritize product scenarios

Compare candidate data products and rank their most credible use scenarios.

Prepare investment decisions

Separate realized benefits from potential value and identify improvement priorities.

Try These Prompts

Identify value types

Review this dataset across all nine value types. For each type, state relevance, evidence, and missing information.

Diagnose value factors

Score the five value factors from one to five. Explain each score, identify the weakest factor, and recommend improvements.

Rank value scenarios

Compare these internal and external scenarios by market scale, buyer value, competition, compliance, and evidence. Rank them with stated assumptions.

Build an assessment report

Create a complete data value assessment. Include value types, five-factor diagnosis, data map, scenario ranking, measurement ranges, decay risks, and actions.

Best Practices

Provide evidence for each score and label every assumption.
Separate realized benefits, validated opportunities, and speculative potential.
Complete rights and compliance checks before recommending external use.

Avoid

Do not treat stated buyer interest as proven willingness to pay.
Do not present potential value as current revenue or asset value.
Do not rank scenarios without considering permissions, quality, and alternatives.

Frequently Asked Questions

Does this skill produce a formal asset valuation?

No. It analyzes value sources and opportunities but does not replace a licensed valuation report.

What information should I provide?

Provide data sources, fields, quality, permissions, current uses, candidate buyers, operating costs, and available performance evidence.

How are value opportunities classified?

The framework reviews nine types, including revenue, savings, efficiency, risk, decisions, financing, ecosystem, compliance, and strategic options.

Can it estimate monetary value?

It can create evidence-based ranges when sufficient inputs exist. Every estimate should state its method, assumptions, and uncertainty.

How does it find cross-industry opportunities?

It examines other industries, buyer problems, comparable uses, and complementary datasets. These opportunities still require market and compliance validation.

Can it handle confidential data?

Use summaries or approved samples. Follow organizational controls and avoid sharing personal, restricted, or proprietary records without authorization.