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Data quality observability

Skill jukrap/ai-agent-playbook/skills/data/data-quality-observability

Reusable AI agent skills, project templates, and guardrails for safer software maintenance and delivery.

Install
npx -y skills add jukrap/ai-agent-playbook --skill data-quality-observability

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  • 2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

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

SKILL.md

1.1 KB, 187 tokens by cl100k_base, as published. Nobody here has run it

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.

Gives 0 of the 12 instructions most monitoring observability skills give in 187 tokens

Counted across 481 of the 483 authors here whose files we hold, read 2026-08-06

  • link every alert to a runbookin 43 of 481, across 35 files
  • use structured json loggingin 36 of 481, across 31 files
  • alert on user-facing symptomsin 20 of 481, across 15 files
  • emit structured JSON logs with stable event namesin 18 of 481, across 13 files
  • propagate trace context across boundariesin 16 of 481
  • use histograms for latency trackingin 14 of 481, across 9 files
  • use OpenTelemetry for distributed tracingin 13 of 481, across 8 files
  • include a correlation ID on every log linein 13 of 481, across 8 files
  • Define service level objectivesin 10 of 481, across 7 files
  • Call useAzureMonitor before importing other modulesin 9 of 481, across 2 files
  • stop and ask for clarification if inputs are missingin 9 of 481, across 2 files
  • define on-call questions before adding telemetryin 9 of 481, across 4 files

Said here and by no other author read

  • identify source, grain, dimensions, owners, and expectations
  • choose bounded checks for data quality dimensions
  • define alert thresholds, cadence, and handoff paths
  • verify using counts, samples, baselines, and evidence

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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