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GoldenMatch

v1.4.1 Feature

This release adds 5 notable features for engineering teams evaluating rollout.

Published 3mo Data Pipelines & ETL
✓ 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

Adds three MCP tools and two A2A skills for data‑quality scanning and transformation.

Full changelog

Data quality and transform tools for MCP and A2A

3 new MCP tools (30 total) and 2 new A2A skills (10 total) expose GoldenCheck data quality scanning and GoldenFlow data transformation to external agents.

New MCP Tools

| Tool | What It Does |
|------|-------------|
| scan_quality | Run GoldenCheck scan, return issues without fixing |
| fix_quality | Scan + apply fixes (safe or moderate mode), optionally save output |
| run_transforms | Run GoldenFlow transforms (phone E.164, dates ISO, Unicode) |

New A2A Skills

| Skill | What It Does |
|-------|-------------|
| quality | Scan and fix data quality issues via GoldenCheck |
| transform | Normalize data formats via GoldenFlow |

Robustness

  • File path validation with actionable error messages
  • Safe output writes — results preserved if write fails (write_error field)
  • strict=True transform mode surfaces failures instead of silently returning unmodified data
  • Temp file cleanup handles Windows file locks
  • Logging at entry/exit of all handlers
  • Graceful degradation when optional deps not installed

Install

pip install goldenmatch==1.4.1
# For quality/transform tools:
pip install goldenmatch[quality,transform]

Full Changelog: https://github.com/benzsevern/goldenmatch/blob/main/CHANGELOG.md

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About GoldenMatch

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Related context

Related tools

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.

Beta — feedback welcome: [email protected]