Value Data Assets with Evidence
Data assets often lack active markets, making valuation methods and assumptions difficult to defend. This skill structures evidence-based valuations under Chinese appraisal guidance.
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Review the Skillstore skill "data-asset-valuation-expert" from https://skillstore.io/skills/dhturing-data-asset-valuation-expert.md and its manifest at https://skillstore.io/api/skills/dhturing-data-asset-valuation-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.
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Using "data-asset-valuation-expert". A retailer wants to value customer behavior data for accounting recognition but has no direct data sales.
Expected outcome:
Recommended approach: use the cost method as the primary method and test attributable efficiency gains through the income method. Verify ownership and compliance first.
Using "data-asset-valuation-expert". A lender asks why financing is lower than the appraised value.
Expected outcome:
The appraisal estimates asset value under stated assumptions. Credit limits may apply discounts for liquidity, enforceability, value volatility, and disposal uncertainty.
Using "data-asset-valuation-expert". Review a valuation using a five-year income period and a high data contribution rate.
Expected outcome:
Request evidence for data refresh cycles, customer retention, income attribution, and the contribution benchmark. Test shorter periods and lower contribution rates.
All twelve static alerts are false positives caused by Markdown syntax, documented installation paths, public attribution links, and Chinese plaintext entropy. The skill contains no executable code, active network requests, obfuscated payloads, or prompt-injection instructions.
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https://skillstore.io/skills/dhturing-data-asset-valuation-expert/audits/1?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report[](https://skillstore.io/skills/dhturing-data-asset-valuation-expert?utm_source=security_passport_badge)<a href="https://skillstore.io/skills/dhturing-data-asset-valuation-expert?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/dhturing-data-asset-valuation-expert/security.svg" alt="Skillstore security assessment" loading="lazy"></a><iframe src="https://skillstore.io/embed/skills/dhturing-data-asset-valuation-expert.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>图灵数盟. (2026). data-asset-valuation-expert security audit report (audit version 1) [Author version unspecified]. Skillstore. https://skillstore.io/skills/dhturing-data-asset-valuation-expert/audits/1@techreport{dhturing-data-asset-valuation-expert-2026,
author = {图灵数盟},
title = {data-asset-valuation-expert security audit report (audit version 1)},
institution = {Skillstore},
year = {2026},
number = {1},
url = {https://skillstore.io/skills/dhturing-data-asset-valuation-expert/audits/1},
note = {Author version unspecified}
}cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "data-asset-valuation-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-asset-valuation-expert/audits/1"
identifiers:
- type: other
value: "skillstore:dhturing-data-asset-valuation-expert:audit:1"
description: "Skillstore immutable audit report identifier"
Define the asset boundary, select methods, request evidence, and prepare a review-ready valuation analysis.
Explain valuation assumptions, financing discounts, report validity, and evidence needed for lender review.
Check parameter support, method selection, cross-validation, disclosures, and required signatures before report issuance.
Assess this data asset: [description]. Ask for missing facts, then recommend suitable valuation methods and explain the selection.
Create a document request for valuing [asset] for [purpose] on [date]. Cover ownership, costs, income, quality, markets, and compliance.
Evaluate these assumptions: [assumptions]. Test income attribution, useful life, discount rate, replacement cost, adjustment factors, and comparable transactions.
Compare these valuation results: [results]. Reconcile differences, perform sensitivity analysis, state limitations, and draft a defensible conclusion for [audience].
Author
图灵数盟License
MIT
Skillstore revision
r1
Version notice
The author did not declare a version.
Ref
50b73f83991cf073cbdf3f5afad2bbac71dd3bae
Maintenance freshness
9/30/2026
Usage
0 downloads · 0 views
File structure
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