This release adds 5 notable features for engineering teams evaluating rollout.
✓ No known CVEs patched in this version
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Summary
AI summaryAdds 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_errorfield) strict=Truetransform 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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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]