Data Quality Audit
Use when existing data correctness, freshness, completeness, or constraint integrity is in question at runtime, distinct from migration-time safety.
Install
$ agentstack add skill-motao123-dev-workflow-kit-data-quality-audit ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo 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.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-motao123-dev-workflow-kit-data-quality-audit) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Data Quality Audit
Use this skill when the runtime data itself may be wrong, stale, incomplete, or inconsistent.
Trigger Conditions
Use this skill when:
- a dataset, table, or event stream has suspect values
- nullability, uniqueness, foreign-key, or enum integrity may be violated
- freshness or completeness is in question
- duplicate, drifted, or orphaned records are suspected
- the team wants a data-trust pass before a release or downstream use
Do not use this skill for migration planning, code-path debugging, or schema design.
Workflow
- Identify the dataset under review and its purpose.
- List the relevant quality dimensions: correctness, completeness, freshness, uniqueness, referential integrity, schema conformance.
- Surface anomalies and likely failure modes.
- Separate critical issues from low-priority cleanup.
- Recommend remediation and verification.
- Note follow-up monitoring or contracts to add.
Output
For non-trivial work, provide:
- dataset under review
- quality dimensions checked
- key anomalies and severity
- recommended remediation
- verification or monitoring suggestions
Coordination
After data audit:
- use
data-migration-safety-reviewif remediation needs schema or backfill changes - use
systematic-debuggingif anomalies look code-driven - use
docs-writerif data contracts or runbooks need to be published
Invocation Examples
- "Use data-quality-audit from dev-workflow-kit to assess freshness, completeness, and correctness of this dataset."
- "Use data-quality-audit to check whether this table has duplicate or orphaned rows before we expose it."
- "Use data-quality-audit because downstream reports look wrong but the code path seems fine."
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: motao123
- Source: motao123/dev-workflow-kit
- License: MIT
- Homepage: https://motao123.github.io/dev-workflow-kit/
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.