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v1.0.2 — one-command demo & GitHub onboarding
DataSentry v1.0.2
This release makes DataSentry much easier to understand, try, and integrate into a real data-quality workflow.
Highlights
- New
datasentry demozero-configuration product tour - Generate dirty data → detect issues → inspect evidence → safely repair → verify
- Runs fully locally — no dataset, API key, cloud service, or LLM required
- Keeps
datasentry-demoas a direct console alias - Reworked onboarding around Find → Explain → Fix safely → Verify
- Scenario-first examples for common workflows
- Copy-paste GitHub Actions data-quality gate
- Stronger MCP onboarding for AI-agent workflows
- Improved package metadata and GitHub project discovery
- Backward-compatible demo artifact behavior
Try it
pip install --upgrade datasentry-ai
datasentry demoDataSentry keeps deterministic detection as the authority. Repairs are previewed, applied to a copy, verified by re-scanning, and remain reversible.
Why DataSentry
Most data-quality tools work well once you already know which expectations or rules to write.
DataSentry focuses on the step before that too:
Find → Explain → Fix safely → Verify
Discover problems automatically, see the evidence behind them, repair without overwriting the source, and verify that the repair did not introduce regressions.