Data · Global library

Data Quality Sentinel

Instrument data pipelines with freshness, completeness, and anomaly detection checks that fail usefully.

CodexClaude CodeKimi Codeorchestrator-mcp

Best use case

Use Data Quality Sentinel when you need to instrument data pipelines with freshness, completeness, and anomaly detection checks that fail usefully, especially when the work is driven by great expectations and data quality.

Trigger signals

great expectationsdata qualityanomaly detection

Validation hooks

verify_data_freshness

Install surface

Copy the exact command path you need.

Inspect

pip install "orchestrator-mcp[dashboard]"
orchestrator-mcp skills show data-quality-sentinel

Use

orchestrator-mcp skills export data-quality-sentinel --to ./skillforge-packs
# copy the exported pack into your preferred agent environment

Export

cp -R skills/data-quality-sentinel ./your-agent-skills/data-quality-sentinel
# or open skills/data-quality-sentinel/SKILL.md in a markdown-first client

File patterns

**/*.py**/etl/****/dbt/**

Model preferences

deepseek-ai/deepseek-v3.2qwen3-coder:480b-clouddeepseek-r1:32b

Related skills

Adjacent packs to compose next.

Implements Great Expectations data quality framework with comprehensive validation, profiling, and automated quality gates

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Implements comprehensive data pipeline monitoring, anomaly detection, and incident response for data reliability

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Build cohort retention logic and churn views that survive product evolution and messy subscription edge cases.

CodexClaude Code

Apache-2.0 licensedUse it, fork it, redistribute it. No strings attached.Safety-first by designSecret redaction, release scanning, and no shell execution. Constraints are public.GitHub nativeEvery pack is a real folder. Fork, edit, PR. No hidden backend or proprietary pipeline.

Data Quality Sentinel

Copy the exact command path you need.

Adjacent packs to compose next.

Data Quality Gatekeeper

Data Observability Engineer

Cohort Retention Engineer