Skills data-value-monetization-team

📦

data-value-monetization-team

Name: data-value-monetization-team
Author: 图灵数盟

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

Create a Data Monetization Plan

Data teams often struggle to connect data assets to validated revenue opportunities. This skill provides a structured, auditable path from value discovery to monetization operations.

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

Test it

Using "data-value-monetization-team". Assess customer service interaction data for monetization.

Expected outcome:

Value hypothesis: operational benchmarks and service-quality insights.
Priority pilot: anonymized benchmark report for a defined industry segment.
Evidence needed: data rights, consent scope, buyer interviews, and quality measurements.

Using "data-value-monetization-team". Create a plan for a data subscription product.

Expected outcome:

Recommended model: annual subscription with a limited pilot tier.
First milestone: validate three buyers, a pricing range, and a repeatable update process.
Operating metrics: active subscribers, renewal rate, recurring revenue share, and data freshness.

Security Audit

Safe

v1 • 9/27/2026 Open versioned report

All 12 static findings are false positives. The apparent command-execution findings are Markdown file references, the filesystem path is installation documentation, and the URLs are author links. The high-entropy alerts arise from readable multilingual Markdown; no prompt injection, data exfiltration, or executable payload was found.

Files scanned

274

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:31

🌐 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-data-value-monetization-team/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). data-value-monetization-team security audit report (audit version 1) [Author version unspecified]. Skillstore. https://skillstore.io/skills/dhturing-data-value-monetization-team/audits/1

BibTeX citation

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

Assess an internal data portfolio

Inventory enterprise data assets and identify the most promising value and use-case opportunities.

Design a recurring revenue model

Compare service, subscription, transaction, and revenue-sharing models for a data product.

Build an operating roadmap

Create a phased plan for launch, measurement, renewal, and ongoing data asset operations.

Try These Prompts

Identify data value

List the data assets we have: [assets]. Identify likely value types, missing evidence, and the first three discovery questions.

Prioritize use cases

For this data asset: [description], propose up to five use cases. Rank them by buyer need, feasibility, compliance readiness, and expected validation effort.

Compare monetization options

Compare direct sale, data service, subscription, transaction commission, and revenue sharing for [data product]. State assumptions, risks, metrics, and a recommended pilot.

Produce a monetization roadmap

Create a 90-day monetization roadmap for [organization and data assets]. Include ownership, milestones, evidence gates, buyer validation, compliance review, pricing tests, and operating metrics.

Best Practices

Document data ownership, consent, contract limits, and permitted uses before designing a monetization offer.
Validate buyer demand with named users, budget evidence, and a testable deliverable before scaling.
State assumptions, evidence gaps, pricing logic, and professional-review needs in every recommendation.

Avoid

Do not treat estimated asset value as guaranteed revenue or a guaranteed financing outcome.
Do not commercialize data before confirming rights, privacy obligations, and contractual restrictions.
Do not select a business model without testing buyer demand, delivery capacity, and ongoing data quality.