# 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.

## Install

```bash
npx skillstore add 图灵数盟/dhturing-data-asset-valuation-expert
```

## Metadata

- Status: approved
- Slug: dhturing-data-asset-valuation-expert
- Skillstore revision: r1
- Version status: missing
- Tree hash: c0b3bc2e52c2b9a9803bfff7ae4d5ded8b6aa643c3f387c107118563e50440db
- Author: 图灵数盟
- GitHub username: dhturing
- License: MIT
- Repository: https://github.com/dhturing/turing-data-experts/tree/9c58ffa97ec5b760befef6f910eb23107cdcdbf4/skills/data-asset-valuation-expert
- Ref: 50b73f83991cf073cbdf3f5afad2bbac71dd3bae
- Supported tools: Claude, Codex, Claude Code
- Audit status: complete
- Agent install advisory: allowed
- Manual install advisory: allowed
- Artifact signature: available
- Audit attestation: unavailable
- Human verification: not\_verified
- Risk factors: filesystem, external\_commands, network
- Quality score: 77
- Quality tier: bronze
- Public page: https://skillstore.pages.dev/skills/dhturing-data-asset-valuation-expert
- Manifest: https://skillstore.pages.dev/api/skills/dhturing-data-asset-valuation-expert/manifest

## Capabilities

- Identifies valuation purposes, asset boundaries, value types, and valuation dates.
- Selects income, cost, and market approaches using a documented decision process.
- Structures assumptions for attributable income, useful life, discount rates, replacement cost, and adjustment factors.
- Compares results from multiple approaches and explains material differences.
- Drafts valuation report sections, evidence lists, review checklists, and stakeholder responses.
- Flags ownership, compliance, qualification, market, and parameter risks before conclusions.

## Use Cases

- Prepare an accounting valuation: Define the asset boundary, select methods, request evidence, and prepare a review-ready valuation analysis.
- Assess collateral support: Explain valuation assumptions, financing discounts, report validity, and evidence needed for lender review.
- Review appraisal workpapers: Check parameter support, method selection, cross-validation, disclosures, and required signatures before report issuance.

## Prompt Templates

### Choose a valuation method

```
Assess this data asset: [description]. Ask for missing facts, then recommend suitable valuation methods and explain the selection.
```

### Build an evidence request

```
Create a document request for valuing [asset] for [purpose] on [date]. Cover ownership, costs, income, quality, markets, and compliance.
```

### Analyze valuation parameters

```
Evaluate these assumptions: [assumptions]. Test income attribution, useful life, discount rate, replacement cost, adjustment factors, and comparable transactions.
```

### Draft a cross-validated conclusion

```
Compare these valuation results: [results]. Reconcile differences, perform sensitivity analysis, state limitations, and draft a defensible conclusion for [audience].
```

## Limitations

- It does not replace a licensed appraisal institution or authorized appraiser.
- It cannot verify source records, legal ownership, market transactions, or management forecasts independently.
- Illustrative parameter ranges require current evidence and professional judgment before use.
- Its guidance focuses on Chinese data asset appraisal practices and may not fit other jurisdictions.

## Best Practices

- Define the valuation purpose, asset boundary, value type, and date before selecting a method.
- Document every material assumption with traceable evidence and run sensitivity tests for uncertain parameters.
- Use at least two approaches when feasible and explain differences before forming a conclusion.

## Anti Patterns

- Do not target a desired value or adjust unsupported parameters to meet financing expectations.
- Do not treat replacement cost as value without evaluating quality, utility, market demand, and legal constraints.
- Do not present an AI-generated analysis as a signed appraisal report from a licensed institution.

## Security Audit

- Audited at: 2026-09-27T03:10:01.315\+00:00
- Summary: 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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