技能 data-product-incubation-expert

📦

data-product-incubation-expert

Name: data-product-incubation-expert
Author: 图灵数盟

內容修訂 r1 安全 📁 檔案系統存取⚙️ 外部命令🌐 網路存取

Incubate Market-Ready Data Products

Raw data often lacks a clear buyer, use case, specification, and price. This skill structures product discovery, MVP validation, pricing, and launch preparation.

支援: Claude Codex Code(CC)

🥉 77 銅級

Maintain this Skill? Claim it 🥉 77 銅級

使用我的智慧代理安裝

將此請求複製給你的智慧代理，其中包含權威 Skill 頁面和清單。

智能體請求

Review the Skillstore skill "data-product-incubation-expert" from https://skillstore.io/skills/dhturing-data-product-incubation-expert.md and its manifest at https://skillstore.io/api/skills/dhturing-data-product-incubation-expert/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

你的智慧代理仍應顯示其計畫，並依安全政策請求必要確認。

Agent 可讀資源

當 AI Agent、爬蟲或腳本需要乾淨脈絡、而不是讀取完整頁面時，請使用這些連結。

Markdown 詳情 GET /skills/dhturing-data-product-incubation-expert.md 已簽名 manifest GET /api/skills/dhturing-data-product-incubation-expert/manifest 已簽名 lockfile GET /api/skills/dhturing-data-product-incubation-expert/lockfile

測試它

正在使用「data-product-incubation-expert」。 Assess our anonymized machine maintenance records for product potential.

預期結果:

Strong describability and repeatability, but buyer requirements and delivery frequency need evidence.
Test a predictive maintenance benchmark with two equipment operators.
Proceed only after validating measurable downtime reduction and permitted usage rights.

正在使用「data-product-incubation-expert」。 Create an MVP plan for a retail demand benchmark.

預期結果:

Package regional category indices with weekly updates, documented coverage, and quality thresholds.
Recruit three retailers and compare forecast accuracy against their current baseline.
Advance when repeated usage and credible payment interest meet agreed thresholds.

正在使用「data-product-incubation-expert」。 Suggest pricing evidence for a logistics performance dataset.

預期結果:

Estimate production and support costs for the minimum sustainable price.
Quantify buyer savings from fewer delays and better carrier selection.
Compare equivalent market offers before testing a limited pilot price.

安全審計

安全

v1 • 2026/9/27 開啟版本化報告

All twelve static findings are false positives caused by documentation examples, Markdown formatting, public website references, or Chinese-language text. No executable payload, network request, hidden-file access, prompt injection, or malicious intent was found.

已掃描檔案

429

分析行數

複核項目

已忽略的誤報

風險因素

📁 檔案系統存取 (2)

profile.md:38 profile.md:38

⚙️ 外部命令 (3)

SKILL.md:20 SKILL.md:26 SKILL.md:33

🌐 網路存取 (2)

SKILL.md:6 SKILL.md:12

最近一次完成的靜態與語意稽核未偵測到已確認的安全發現。這並不能證明該 Skill 沒有副作用。

審計單位: codex

分享與引用此報告

分享版本化評估報告、中性徽章、嵌入卡片與引用。Skillstore 只報告證據，不判斷該 Skill 是否安全。

開啟版本化報告

複製報告連結

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

Markdown 徽章

[![Skillstore security assessment](https://skillstore.io/badges/skills/dhturing-data-product-incubation-expert/security.svg)](https://skillstore.io/skills/dhturing-data-product-incubation-expert?utm_source=security_passport_badge)

HTML 徽章

<a href="https://skillstore.io/skills/dhturing-data-product-incubation-expert?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/dhturing-data-product-incubation-expert/security.svg" alt="Skillstore security assessment" loading="lazy"></a>

嵌入卡片

<iframe src="https://skillstore.io/embed/skills/dhturing-data-product-incubation-expert.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>

學術引用 (APA · BibTeX · CFF)

APA 引用

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

BibTeX 引用

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

Skillstore 評分

評分依據證據置信度: 中

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你可以打造什麼

Prioritize Data Opportunities

Evaluate available datasets and rank product opportunities by buyer pain, feasibility, and measurable value.

Design a Validatable MVP

Define product specifications, sample outputs, validation questions, and success criteria before major development.

Prepare a Product Launch

Develop positioning, pricing logic, product documentation, quality checks, and listing preparation.

試試這些提示

Assess One Dataset

Assess [dataset] using the five productization questions. Identify missing evidence and recommend the next practical step.

Discover Buyer Scenarios

Find three product scenarios for [data resource] in [industry]. Rank them by buyer pain, measurable value, feasibility, and repeatability.

Design and Validate an MVP

Design a seven-element data product for [scenario]. Define its MVP, seed-customer plan, validation questions, success metrics, and decision thresholds.

Build an Incubation Roadmap

Create a phased incubation roadmap for [portfolio]. Include prioritization, value hypotheses, product specifications, governance gates, pricing evidence, launch dependencies, and iteration decisions.

最佳實務

Start with verified buyer problems, then match available data to those problems.
Use measured behavior and payment signals instead of favorable interview comments.
Treat data rights, privacy, quality, and repeatable delivery as launch gates.

避免

Do not build a complete product before testing a focused MVP.
Do not claim financial value without evidence, assumptions, and measurement methods.
Do not sell internally collected data before reviewing rights and compliance.