技能 data-standardization-expert

📦

data-standardization-expert

Name: data-standardization-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

Standardize Enterprise Data Practices

Organizations struggle to map data practices to current standards and produce consistent governance documents. This skill structures assessments, implementation plans, and standards drafting.

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-standardization-expert" from https://skillstore.io/skills/dhturing-data-standardization-expert.md and its manifest at https://skillstore.io/api/skills/dhturing-data-standardization-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-standardization-expert.md 已簽名 manifest GET /api/skills/dhturing-data-standardization-expert/manifest 已簽名 lockfile GET /api/skills/dhturing-data-standardization-expert/lockfile

測試它

正在使用「data-standardization-expert」。 Map standards for a data asset registration initiative.

預期結果:

Priority 1: define data asset categories, identifiers, ownership fields, and registration evidence.
Priority 2: align quality measures with GB/T 36344 and classification controls with GB/T 43697.
Deliverables: applicable standards list, registration field standard, implementation plan, and review checklist.

正在使用「data-standardization-expert」。 Outline an enterprise data quality standard.

預期結果:

Proposed sections: scope, references, terms, quality dimensions, measurement rules, thresholds, exception handling, responsibilities, evidence, review cycle, and appendices.

正在使用「data-standardization-expert」。 Assess whether we should join an association standard project.

預期結果:

Evaluate strategic relevance, technical contribution, participation terms, total cost, staff capacity, publication rights, evidence of participation, and reputational risk before approval.

安全審計

安全

v1 • 2026/9/27 開啟版本化報告

All 12 static findings are false positives caused by documentation paths, Markdown code spans, attribution links, Chinese text, or CRLF formatting. The reviewed files contain no executable commands, network requests, encoded payloads, prompt injection, or other semantic security concerns.

已掃描檔案

460

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-standardization-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-standardization-expert/security.svg)](https://skillstore.io/skills/dhturing-data-standardization-expert?utm_source=security_passport_badge)

HTML 徽章

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

嵌入卡片

<iframe src="https://skillstore.io/embed/skills/dhturing-data-standardization-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-standardization-expert security audit report (audit version 1) [Author version unspecified]. Skillstore. https://skillstore.io/skills/dhturing-data-standardization-expert/audits/1

BibTeX 引用

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

Skillstore 評分

評分依據證據置信度: 中

架構

可維護性

內容

社群

規範符合度

你可以打造什麼

Build an applicable standards map

Classify relevant standards, explain their purpose, and rank implementation priorities for a data governance program.

Draft an enterprise data standard

Create a review-ready structure for terminology, data quality, classification, registration, or management requirements.

Assess association standard participation

Evaluate participation routes, expected evidence, responsibilities, costs, timelines, and reputational risks.

試試這些提示

Identify relevant standards

List standards relevant to [business scenario]. Group them by data elements, datasets, business terms, and management. Explain priority and applicability.

Analyze standardization gaps

Compare our current practice [describe practice] with [named standard]. Create a gap list with evidence needed, priority, owner, and recommended action.

Draft an enterprise standard

Draft an enterprise standard for [topic] using the documented GB/T 1.1 structure. Include scope, terms, requirements, methods, evidence, and appendices.

Design a standards program

Design a phased standards program for [organization]. Cover governance, applicable standards, implementation, conformity assessment, association participation, deliverables, dependencies, and risks.

最佳實務

Confirm each standard's status, scope, and effective date with an authoritative source.
Separate mandatory obligations, voluntary guidance, contractual commitments, and internal policy.
Attach owners, evidence, review criteria, and realistic milestones to every implementation action.

避免

Do not treat a standards list as proof of operational compliance.
Do not claim certification, legal approval, or official endorsement without valid evidence.
Do not join a drafting project only for attribution without meaningful technical contribution.

常見問題

What types of standards can this skill organize?

It covers national, industry, local, association, and enterprise standards related to data governance, quality, security, registration, and management.

Can it draft a complete enterprise standard?

It can create a structured draft and review checklist. Qualified stakeholders must validate technical, legal, and organizational requirements.

Does it confirm that a standard is current?

No. Verify status, amendments, replacements, and effective dates through authoritative standards sources before relying on any citation.

Can it certify compliance?

No. It can organize a self-assessment, gaps, and evidence needs, but certification requires an authorized assessment body.

How does it support association standard participation?

It structures opportunity review, application preparation, drafting contributions, meeting records, publication steps, expected costs, and risk checks.

What information produces the best result?

Provide the business scenario, jurisdiction, current processes, target standards, available evidence, stakeholders, deadlines, and intended deliverables.