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Data Quality Triage

skill-clarentcinematics-codex-skills-for-enterprise-data-quality-triage · by ClarentCinematics

Review CSV exports, dataset samples, schema notes, metric inputs, or dashboard source data for null rates, duplicate keys, stale dates, inconsistent enum values, schema gaps, and quality caveats. Use when Codex needs to turn data-quality evidence into a triage report without inventing missing values or metric definitions.

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Install

$ agentstack add skill-clarentcinematics-codex-skills-for-enterprise-data-quality-triage

✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • ✓ Prompt-injection patterns
  • ✓ Secret / credential exfiltration
  • ✓ Dangerous shell & filesystem operations
  • ✓ Untrusted network calls
  • ✓ Known-malicious package signatures

What it can access

  • ✓ Network access No
  • ✓ Filesystem access No
  • ✓ Shell / process execution No
  • ✓ Environment & secrets No
  • ✓ Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

Data Quality Triage

Workflow

  1. Identify dataset purpose, grain, source date, key fields, date fields, and business use.
  2. Detect missing values, duplicate records, stale dates, inconsistent categories, and schema gaps.
  3. Separate deterministic data-quality findings from metric interpretation or business conclusions.
  4. Prioritize risks by downstream impact on reporting, operations, decisions, and automation.
  5. Recommend cleanup actions, source-system checks, and owner questions.

Script-Assisted Workflow

When given a CSV sample, run scripts/audit_data_quality.py --input before writing the triage. Add --key-fields, --date-fields, --stale-days, and --today when the dataset contract is known. Use --json for structured evidence. Do not let the helper infer missing values, metric definitions, or business truth.

Output Standard

Use this structure by default:

  • Triage Summary: dataset, scope, row count, and overall quality risk.
  • Critical Findings: duplicate keys, high-null fields, stale dates, schema blockers, or enum conflicts.
  • Field-Level Evidence: field, issue, examples, and affected count or rate.
  • Downstream Risk: dashboard, metric, workflow, or decision impact.
  • Cleanup Actions: source-system checks, owner actions, and validation queries.
  • Questions To Resolve: missing grain, key, owner, metric definition, or freshness context.
  • Caveats: sample limitations and non-inferred fields.

Rules

  • Do not invent missing values, metric definitions, row ownership, or source-of-truth status.
  • Mark sample-based findings as sample-based.
  • Treat high-impact reporting, finance, customer, or compliance data as requiring human review.
  • Prefer deterministic checks before narrative interpretation.

References

Read references/data-quality-rubric.md when prioritizing findings or mapping data-quality risks to reporting, automation, or operational impact.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.

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