# Data Quality Observability

> Use when designing or reviewing data quality checks, freshness alerts, anomaly detection, null/duplicate/orphan checks, quarantine, repair, or data incident handoff.

- **Type:** Skill
- **Install:** `agentstack add skill-jukrap-ai-agent-playbook-data-quality-observability`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [jukrap](https://agentstack.voostack.com/s/jukrap)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [jukrap](https://github.com/jukrap)
- **Source:** https://github.com/jukrap/ai-agent-playbook/tree/main/skills/data/data-quality-observability
- **Website:** https://www.npmjs.com/package/ai-agent-playbook

## Install

```sh
agentstack add skill-jukrap-ai-agent-playbook-data-quality-observability
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Data Quality Observability

Use this as the primary data skill for quality checks, freshness signals, alerts, and data incident handoff.

## Workflow

1. Identify data source, transformation boundary, dataset grain, quality dimensions, owner, consumers, and freshness or SLA expectations.
2. Choose bounded checks for nulls, duplicates, orphans, ranges, enums, referential integrity, volume, drift, completeness, and freshness.
3. Define alert threshold, run cadence, sample window, quarantine/repair path, and owner handoff.
4. Verify with source counts, sampled rows, reconciliation queries, historical baselines, and alert evidence when possible.

## Reference

Read `references/quality-check-design.md` for source, transform, consumer, and repair check design.

Read `references/freshness-anomaly-and-alerts.md` for freshness, anomaly, threshold, alert, and incident handoff checks.

## Source & license

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

- **Author:** [jukrap](https://github.com/jukrap)
- **Source:** [jukrap/ai-agent-playbook](https://github.com/jukrap/ai-agent-playbook)
- **License:** MIT
- **Homepage:** https://www.npmjs.com/package/ai-agent-playbook

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

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-jukrap-ai-agent-playbook-data-quality-observability
- Seller: https://agentstack.voostack.com/s/jukrap
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.