Add data-quality helpers: row schema validation, field extraction, masking#227
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| Metric | Results |
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| Complexity | 84 |
| Duplication | 0 |
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Round-4 multi-agent web-research follow-up, batch 9 — three pure-stdlib data-quality helpers (the gate between ingestion and downstream entry), each wired through all five layers (facade,
AC_*, MCP, Script Builder) with headless tests + EN/Zh v19 docs + README sections.Features (
utils/data_quality)validate_rows(rows, schema): declarative per-field rules (type/required/regex/min/max/min_len/max_len/allowed/unique); returns{ok, valid, invalid, errors}.AC_validate_rows+ac_validate_rows.extract_fields(text, fields, patterns): named regex presets (email/url/ipv4/phone/date_iso/amount/hashtag) + custom patterns.AC_extract_fields+ac_extract_fields.mask_rows(rows, rules): redact / hash (SHA-256) / partial (keep last 4) per column before export.AC_mask_rows+ac_mask_rows.Why
The round-4 data/validation agent verified there's no validation/extraction/masking layer between
load_rows/OCR and downstream entry — the #1 data-quality gap. Pure-stdlib (re/hashlib), no new deps.Verification
test/unit_test/headless/test_data_quality_batch.py— 8 tests pass._range_errorinto number/length helpers); bandit clean;import je_auto_controlPySide6-free.