Michael 673b902377 feat(license): datatools-admin CLI for the mint API
New operator CLI at src/admin_cli.py: mint, list, revoke, ping —
talks to the server's /internal/* endpoints over a local SSH tunnel.
Stdlib-only on the desktop side (urllib + typer), no new top-level
deps. Auth via $DATATOOLS_ADMIN_TOKEN.

scripts/generate_license.py is now annotated as a break-glass tool
for when the server is unreachable — routine work goes through the
new CLI so the authoritative `licenses` row is created.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-14 00:47:01 +00:00

🌐 Language: English · Español

DataTools

Local CSV / Excel cleaning. CLI + browser GUI, no cloud, no install ceremony. GUI ships with English and Spanish language packs.

Tools

# Tool Status
01 Deduplicator — exact + fuzzy match, 5 normalizers, survivor rules, audit Ready
02 Text Cleaner — whitespace, smart chars, BOM, line endings, case ops Ready
03 Format Standardizer — dates, phones, emails, addresses, names, currencies, booleans Ready
04 Missing Value Handler — disguised-null detection, profile, mean/median/mode/ffill/bfill/interpolate, drop strategies Ready
05 Column Mapper — fuzzy auto-rename, target schema with type coercion, required fields with defaults, drop/reorder Ready
06 Outlier Detector Coming Soon
07 Multi-File Merger Coming Soon
08 Validator & Reporter Coming Soon
09 Pipeline Runner — chain tools with recommended (not forced) order, save/load JSON, automate weekly cleanups Ready

Download (non-technical users)

Pre-built installers — no Python required:

Platform Download First-launch note
macOS DataTools-X.Y.Z-mac.dmg Drag DataTools.app into /Applications, then double-click.
Windows DataTools-X.Y.Z-win-setup.exe Run the installer; launches from Start Menu.
Linux DataTools-X.Y.Z-linux-x86_64.AppImage chmod +x the file, then double-click.

Latest release: see GitHub Releases (or the Gumroad listing). The installers are ~150200 MB; the launcher boots a local server at http://127.0.0.1:8501 and opens your browser. Nothing is sent to the cloud.

Install from source (developers)

pip install -r requirements.txt

Python 3.10+ required.

Run

GUI (recommended):

streamlit run src/gui/app.py

CLI — seven entry points:

python -m src.cli            customers.csv [--apply]   # dedup
python -m src.cli_text_clean messy.csv     [--apply]   # text clean
python -m src.cli_format     intl.csv      [--apply]   # format standardize (auto-streams >100 MB)
python -m src.cli_missing    holes.csv     [--apply]   # missing values
python -m src.cli_column_map vendor.csv    [--apply]   # column mapper
python -m src.cli_pipeline   any_file.csv  [--apply]   # chain tools end-to-end
python -m src.cli_analyze    any_file.csv  [--json]    # scan only

Every CLI runs preview-only by default; add --apply to write output.

Language

The GUI sidebar has a language picker. Packs ship for English and Español (src/i18n/packs/); the choice persists for the session. Adding a language: drop a <code>.json next to en.json mirroring its key tree, then list it in LANGUAGES. See Developer Guide §i18n.

Review & Normalize gate

Every uploaded file passes through a CSV-normalization gate before any tool sees it. The analyzer flags ~15 issue types (whitespace, NBSP / zero-width chars, BOM, encoding, smart punct, dirty headers, null sentinels, mojibake, …) tagged by confidence (high / medium / low) and fix action. The GUI shows each finding with Auto-fix / Skip / Customize, a live before/after preview, and an encoding-override picker. Tool pages refuse to load until the gate passes.

Output

Every run writes:

  • {input}_<tool>.csv — the cleaned data
  • {input}_changes.csv (text cleaner) or {input}_match_groups.csv (dedup) — audit trail
  • logs/<tool>_YYYYMMDD_HHMMSS.log — debug-level run log

Original input file is never modified.

Docs

Dependencies

pandas, openpyxl, rapidfuzz, phonenumbers, typer, loguru, charset-normalizer, streamlit. Optional: ftfy for mojibake repair.

License

Proprietary.

Description
Data tools development
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