Skills data-quality-assessment-expert

📦

data-quality-assessment-expert

Name: data-quality-assessment-expert
Author: 图灵数盟

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

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.

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-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.

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

Test it

Using "data-quality-assessment-expert". Assess a customer dataset with 96% accuracy, 82% completeness, and delayed monthly updates.

Expected outcome:

The assessment highlights strong accuracy, weak completeness, and material timeliness risk. It recommends filling critical fields and enforcing update deadlines before final grading.

Using "data-quality-assessment-expert". Explain whether a C-grade dataset should proceed to accounting recognition.

Expected outcome:

A C grade indicates material quality gaps. Complete targeted remediation and reassessment before recognition, while documenting evidence, ownership, and remaining limitations.

Using "data-quality-assessment-expert". Create a report outline for transaction due diligence.

Expected outcome:

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

Security Audit

Safe

v1 • 9/27/2026 Open versioned report

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.

Files scanned

391

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

Share & cite this report

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Open versioned report

Copy report link

https://skillstore.io/skills/dhturing-data-quality-assessment-expert/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-quality-assessment-expert security audit report (audit version 1) [Author version unspecified]. Skillstore. https://skillstore.io/skills/dhturing-data-quality-assessment-expert/audits/1

BibTeX citation

@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 Score

Why this score Evidence Confidence: Medium

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

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.

Try These Prompts

Create a basic quality checklist

Create a six-dimension data quality checklist for [dataset]. State one measurable indicator and one evidence source for each dimension.

Design a weighted scorecard

Design a GB/T 36344-2018 scorecard for [purpose]. Explain each weight, scoring rule, threshold, and required evidence.

Analyze assessment results

Assess these quality results: [results]. Calculate dimension scores, the weighted total, the grade, major defects, and recommended remediation priorities.

Develop an assessment and valuation package

Develop a complete assessment for [dataset and purpose]. Include scope, sampling, rules, scores, grade, delta range, caveats, remediation, and monitoring.

Best Practices

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.

Avoid

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.