GoldenMatch

vgoldencheck-types-v0.2.0 scope: goldencheck-types Feature

This release adds 1 notable feature for engineering teams evaluating rollout.

Published 10d Data Pipelines & ETL

View tool

✓ No known CVEs patched

Read the diff → Tool health → What is this tool? →

✓ No known CVEs patched in this version

Topics

data-cleaning data-engineering data-matching data-quality deduplication entity-resolution

+14 more

fellegi-sunter fuzzy-matching knowledge-graph llm master-data-management mcp-server polars pprl python record-linkage rust splink typescript zero-config

Summary

AI summary

Domain‑pack expanded from 4 to 16 verticals.

Changes in this release

Type	Severity	Summary	CVE
Feature	Low	Adds support for 16 verticals in domain-pack (up from 4). Adds support for 16 verticals in domain-pack (up from 4). Source: llm_adapter@2026-07-16 Confidence: high	—

Full changelog

Minor release: domain-pack rollout from 4 to 16 verticals (hr, insurance, telecom, real_estate, education, logistics, energy, automotive, legal, hospitality, manufacturing, marketing and more).

View diff on GitHub

Weekly OSS security release digest.

The CVE patches and breaking changes that affected production tools this week. One email, every Sunday.

No spam, unsubscribe anytime.

Share this release

Share on X Share on Bluesky

Track GoldenMatch

Get notified when new releases ship.

About GoldenMatch

All releases →

Related context

Related tools

vector

Earlier breaking changes

v3.1.0 `GOLDENMATCH_FRAME=polars` now requires the `[polars]` extra; raises error without it.
vgoldencheck-v3.0.0 `inferred_type` emits neutral dtype vocabulary (str/int/uint/float/date/datetime/bool/other) instead of raw Polars dtypes.
vgoldencheck-v3.0.0 'inferred_type' now emits a neutral dtype vocabulary instead of raw Polars dtype strings.
vgoldencheck-v3.0.0 `scan_file`, `scan_dataframe`, and CLI `check` now run without Polars, using Arrow-native pyarrow.Table.
v3.0.0 Result frames now return pyarrow.Table instead of Polars DataFrame.