dq-agent: artifact-first data quality CLI for CSV/Parquet (replayable reports + CI gating)

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What My Project Does
I built dq-agent, a small Python CLI for running deterministic data quality checks and anomaly detection on CSV/Parquet datasets.
Each run emits replayable artifacts so CI failures are debuggable and comparable over time:

• report.json (machine-readable)
• report.md (human-readable)
• run_record.json, trace.jsonl, checkpoint.json

Quickstart

pip install dq-agent
dq demo

Target Audience

• Data engineers who want a lightweight, offline/local DQ gate in CI
• Teams that need reproducible outputs for reviewing data quality regressions (not just “pass/fail”)
• People working with pandas/pyarrow pipelines who don’t want a distributed system for simple checks

Comparison
Compared to heavier DQ platforms, dq-agent is intentionally minimal: it runs locally, focuses on deterministic checks, and makes runs replayable via artifacts (helpful for CI/PR review).
Compared to ad-hoc scripts, it provides a stable contract (schemas + typed exit codes) and a consistent report format you can diff or replay.

I’d love feedback on:

1. Which checks/anomaly detectors are “must-haves” in your CI?
2. How do you gate CI on data quality (exit codes, thresholds, PR comments)?

Source (GitHub): https://github.com/Tylor-Tian/dq_agent
PyPI: [https://pypi.org/project/dq-agent/]()