data-quality-assessment-expert
Assess Data Quality with Six-Dimension Scoring
Unclear data quality weakens asset, transaction, and governance decisions. This skill scores six dimensions, assigns grades, and recommends focused remediation.
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。
Review the Skillstore skill "data-quality-assessment-expert" from https://skillstore.io/skills/dhturing-data-quality-assessment-expert.md and its manifest at https://skillstore.io/api/skills/dhturing-data-quality-assessment-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。
測試它
正在使用「data-quality-assessment-expert」。 Assess a customer dataset with 96% accuracy, 82% completeness, and delayed monthly updates.
預期結果:
The assessment highlights strong accuracy, weak completeness, and material timeliness risk. It recommends filling critical fields and enforcing update deadlines before final grading.
正在使用「data-quality-assessment-expert」。 Explain whether a C-grade dataset should proceed to accounting recognition.
預期結果:
A C grade indicates material quality gaps. Complete targeted remediation and reassessment before recognition, while documenting evidence, ownership, and remaining limitations.
正在使用「data-quality-assessment-expert」。 Create a report outline for transaction due diligence.
預期結果:
- Purpose, scope, and evaluation date
- Standards, methods, sampling, and rules
- Six-dimension results and issue evidence
- Weighted score, grade, and reference delta range
- Remediation plan, limitations, and appendices
安全審計
安全All twelve static findings are false positives caused by Markdown formatting, installation documentation, ordinary website links, and the entropy of Chinese-language text. No executable scripts, network requests, obfuscated payloads, prompt injection, or unsafe data-handling intent were found.
風險因素
📁 檔案系統存取 (2)
⚙️ 外部命令 (3)
🌐 網路存取 (2)
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。
複製報告連結
https://skillstore.io/skills/dhturing-data-quality-assessment-expert/audits/1?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportMarkdown 徽章
[](https://skillstore.io/skills/dhturing-data-quality-assessment-expert?utm_source=security_passport_badge)HTML 徽章
<a href="https://skillstore.io/skills/dhturing-data-quality-assessment-expert?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/dhturing-data-quality-assessment-expert/security.svg" alt="Skillstore security assessment" loading="lazy"></a>嵌入卡片
<iframe src="https://skillstore.io/embed/skills/dhturing-data-quality-assessment-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-quality-assessment-expert security audit report (audit version 1) [Author version unspecified]. Skillstore. https://skillstore.io/skills/dhturing-data-quality-assessment-expert/audits/1BibTeX 引用
@techreport{dhturing-data-quality-assessment-expert-2026,
author = {图灵数盟},
title = {data-quality-assessment-expert security audit report (audit version 1)},
institution = {Skillstore},
year = {2026},
number = {1},
url = {https://skillstore.io/skills/dhturing-data-quality-assessment-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-quality-assessment-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-quality-assessment-expert/audits/1"
identifiers:
- type: other
value: "skillstore:dhturing-data-quality-assessment-expert:audit:1"
description: "Skillstore immutable audit report identifier"
Skillstore 評分
評分依據 證據置信度: 中你可以打造什麼
Prepare data for accounting recognition
Build a six-dimension assessment, document evidence, and identify quality gaps before data resource recognition.
Support buyer due diligence
Evaluate transaction data, explain its grade, and present reproducible checks for buyer review.
Prioritize quality remediation
Rank defects by severity and define focused actions for the weakest quality dimensions.
試試這些提示
Create a six-dimension data quality checklist for [dataset]. State one measurable indicator and one evidence source for each dimension.
Design a GB/T 36344-2018 scorecard for [purpose]. Explain each weight, scoring rule, threshold, and required evidence.
Assess these quality results: [results]. Calculate dimension scores, the weighted total, the grade, major defects, and recommended remediation priorities.
Develop a complete assessment for [dataset and purpose]. Include scope, sampling, rules, scores, grade, delta range, caveats, remediation, and monitoring.
最佳實務
- Define the business purpose, dataset scope, evaluation date, and evidence requirements before scoring.
- Use read-only access and avoid copying or retaining source data during assessment.
- Document formulas, samples, thresholds, and exceptions so reviewers can reproduce every score.
避免
- Do not raise scores to satisfy a preferred accounting, transaction, or valuation outcome.
- Do not infer a final grade from undocumented samples or incomplete dimension results.
- Do not present reference delta ranges as certified appraisal or regulatory conclusions.
常見問題
Which quality dimensions does this skill assess?
Can it score data for different business purposes?
Does it need the complete dataset?
What does the A-D grade mean?
Can it provide a valuation coefficient?
Can it replace a third-party quality review?
開發者詳細資訊
作者
图灵数盟授權
MIT
Skillstore 修訂
r1
版本提示
作者未聲明版本。
引用
50b73f83991cf073cbdf3f5afad2bbac71dd3bae
維護新鮮度
2026/9/30
使用量
0 次下載 · 0 次瀏覽
檔案結構