Skills data-standardization-expert

📦

data-standardization-expert

Name: data-standardization-expert
Author: 图灵数盟

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

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.

Supports: Claude Codex Code(CC)

🥉 77 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-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.

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-standardization-expert.md Signed manifest GET /api/skills/dhturing-data-standardization-expert/manifest Signed lockfile GET /api/skills/dhturing-data-standardization-expert/lockfile

Test it

Using "data-standardization-expert". Map standards for a data asset registration initiative.

Expected outcome:

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.

Using "data-standardization-expert". Outline an enterprise data quality standard.

Expected outcome:

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

Using "data-standardization-expert". Assess whether we should join an association standard project.

Expected outcome:

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

Security Audit

Safe

v1 • 9/27/2026 Open versioned report

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.

Files scanned

460

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:33

🌐 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: codex

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Open versioned report

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Academic citations (APA · BibTeX · CFF)

APA citation

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 citation

@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 Score

Why this score Evidence Confidence: Medium

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

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.

Try These Prompts

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.

Best Practices

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.

Avoid

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.

Frequently Asked Questions

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.