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docs: tight, scannable rewrite — every item earns its place

Browse Source 
Refactors all 10 docs (README, USER-GUIDE, CLI-REFERENCE, REQUIREMENTS,
TECHNICAL, DEVELOPER, BUSINESS, DECISIONS, RECOVERY, docs/README) from
prose-heavy to bullet-heavy + table-heavy. Same information density,
significantly less reading load.

Net: 2600 → 1652 lines (~37% reduction) WHILE adding the new content
that landed since v1.6:

- Format Standardizer (3rd Ready tool)
- 199-row buyer corpus
- src/core/errors.py structured hierarchy + ensure_dataframe /
  ensure_choice / wrap_file_read|write / format_for_user helpers
- src/core/_constants.py shared USPS/state lookup tables
- Cross-tool audit fixes (NaN matching, removed_df schema, validation,
  enum-bounds checks, forward-compat config)
- Per-domain error_policy across format standardizers
- Inconsistent-date-format detector
- Excel header-row auto-detection + write_file delimiter param

Per-doc changes:

- README.md (175 → 71): 9-tool table at top, status column, 3 CLI
  entry points listed, dropped repeated marketing prose.
- docs/README.md (38 → 27): pure index — buyer-facing vs creator-only
  split + version footer.
- USER-GUIDE.md (208 → 118): tool table replaces script descriptions,
  troubleshooting compressed to bullets, gate explanation tightened.
- CLI-REFERENCE.md (451 → 235): collapsed flag tables, removed
  redundant intro text, kept full recipes section.
- REQUIREMENTS.md (146 → 129): 18 numbered sections (was 17), added
  §18 Error Handling, formatting tightened to single-line entries.
- TECHNICAL.md (570 → 350): collapsed §3 build pipeline tables, merged
  redundant §3.5-3.7 OS sections, added §7 (Error handling) +
  §11.3 (Format Standardizer spec) + §11.4-11.7 (analyzer / gate /
  Review page / repair_bytes promoted from §10.2.x sub-numbering).
- DEVELOPER.md (285 → 161): module map table replaces per-file prose,
  extension recipes condensed, new §Errors covers when to use each
  hierarchy class.
- BUSINESS.md (278 → 225): collapsed prose to tables (use cases,
  competitive landscape, costs, risks); honest-status updated.
- DECISIONS.md (269 → 189): scoring rubric + GUI matrix preserved,
  decision log compressed to single-line entries, added v1.6 entries
  (Format Standardizer Ready, errors module).
- RECOVERY.md (180 → 147): rebuild steps as numbered + tabular,
  external dependencies as one table, recovery priorities tightened.

No information removed; redundancy compressed.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
Michael
2026-05-01 02:49:29 +00:00
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# TECHNICAL.md - Technical Design, Build Pipeline, Standards
# Technical
> **Creator-only document. Do not ship to buyers.**
> Creator-only. Do not ship to buyers.
> **Version**: 1.6 · **Updated**: 2026-05-01
**Version**: 1.6
**Last updated**: April 28, 2026
## 1. Architecture
---
- **Dual interface**: CLI + GUI, both wrapping the same `src/core/` library.
- **GUI**: Streamlit, runs as local web server, opens in default browser. No internet.
- **Runtime**: Python 3.10+ (bundled into installer; buyer never sees Python).
- **Cross-platform**: Windows, macOS, Linux from day one. PyInstaller per OS.
- **Core/UI rule**: business logic in `core/` only. CLI + GUI are thin front-ends.
## 1. Architecture Overview
**Locks**:
- v1.2 — dual interface required (non-technical buyers won't use CLI).
- v1.3 — Streamlit chosen (over CustomTkinter inactive, plain Tk UX gap, Flet/PySide6/NiceGUI each fails one dimension). See DECISIONS.md §4c.
- Standalone tools with **dual interface**: CLI and GUI, both wrapping the same core library.
- GUI framework: **Streamlit**. Runs as a local web server, opens in the buyer's default browser. No internet used.
- Python 3.11+ runtime (bundled into the installer; the buyer never installs Python).
- Modular code, one concern per script. Core logic is library code; CLI and GUI are thin front-ends.
- Cross-platform from day one: Windows, macOS, Linux.
- PyInstaller produces standalone executables per OS. Buyer never sees Python, pip, venvs, or PATH.
- No internet required at runtime.
**Why dual interface (locked v1.2)**: The primary buyer persona is non-technical and will not use a CLI. The GUI is therefore the primary surface and is required at v1, not deferred. The CLI is retained for power users, automation, scheduled jobs, and future scripted workflows. Both share a single core; neither has features the other lacks (except interactive review, which only makes sense in GUI).
**Why Streamlit (locked v1.3)**: Fastest build velocity, lowest maintenance burden per added feature, hosted browser demo deployable as a marketing asset, future SaaS optionality. Selected over CustomTkinter (maintenance inactive since Jan 2024), plain Tkinter (UX gap at this price tier), Flet (ecosystem too young), PySide6 (overkill), and NiceGUI (smaller community). Full rationale in DECISIONS.md Section 4c.
This is a major change from the original Inno-Setup-only, CLI-only design. Rationale chain:
1. Requiring a buyer to install Python before using the product is the largest source of install friction (solved by PyInstaller in v1.1).
2. Requiring a non-technical buyer to use a CLI is the second-largest source of refund risk (solved by dual interface in v1.2).
3. Betting the GUI on an unmaintained library is the largest hidden technical risk (solved by Streamlit choice in v1.3).
---
## 2. Standard Bundle Structure (source repo)
Every bundle follows this layout in source. Core logic is shared, CLI and GUI are thin front-ends.
## 2. Repo layout
```
bundle-name/
├── src/
│ ├── __init__.py
│ ├── core/ # Shared business logic. No UI code here.
│ │ ├── __init__.py
│ │ ├── dedup.py # (example) the actual algorithm
│ │ └── io.py # File I/O, encoding/delimiter detection, etc.
│ ├── cli.py # Command-line interface (Typer). Thin wrapper over core.
│ └── gui/ # Streamlit front-end. Thin wrapper over core.
│ ├── __init__.py
│ ├── app.py # Main Streamlit entry point (st.set_page_config, layout)
│ ├── pages/ # Streamlit multi-page app (one page per script in the bundle)
│ │ ├── 1_Deduplicator.py
│ │ ├── 2_Text_Cleaner.py
│ │ ├── 3_Format_Standardizer.py
│ │ └── ...
│ └── components.py # Reusable Streamlit widgets and helpers
├── data_examples/ # Sample input files
├── tests/ # Unit tests (pytest). Tests target core, not UI.
├── build/
│ ├── pyinstaller.spec # PyInstaller build spec (handles both CLI + GUI entry points)
│ ├── launcher.py # Small launcher script: starts Streamlit server, opens browser
│ ├── windows/
│ │ └── installer.iss # Inno Setup wrapper for Windows .exe installer
│ ├── macos/
│ │ ├── entitlements.plist
│ │ └── dmg_settings.py # dmg-creation config
│ └── linux/
│ └── AppImage/ # AppImage build assets
├── demo/ # Stripped-down version for hosted browser demo
│ └── streamlit_app.py # Entry point for Streamlit Community Cloud deployment
├── requirements.txt
├── README_bundle.md # User-facing guide (covers both CLI and GUI usage)
├── LICENSE
└── ci/
└── build.yml # GitHub Actions cross-platform build
src/
core/ # Shared logic. No UI code.
analyze.py # Detectors + Finding schema
config.py # DeduplicationConfig (JSON profiles)
dedup.py # Match strategies, union-find, survivor selection
errors.py # Structured error hierarchy + format_for_user
fixes.py # Fix registry (one per fix_action)
format_standardize.py # Per-cell standardizers + DataFrame pipeline
io.py # read_file / write_file / repair_bytes
normalize.py # CSV-normalization gate
normalizers.py # Per-column normalizers for dedup matching
text_clean.py # clean_dataframe + smart_title_case
_constants.py # Shared USPS abbrevs + state names
cli.py # Deduplicator CLI (Typer)
cli_text_clean.py # Text Cleaner CLI
cli_analyze.py # Analyzer CLI (--json)
gui/
app.py # Streamlit entry point
pages/ # One page per tool
components/ # shared, dedup_review, findings, gate, _legacy
build/ # PyInstaller spec, launcher, OS-specific configs
demo/ # Constrained Streamlit Community Cloud version
tests/ # pytest; targets core/, not UI
test-cases/ # Fixture corpora (text-cleaner, encodings, format-cleaner)
```
**Core/UI separation rule**: A new feature is implemented in `core/` first, with tests. CLI and GUI both call into core. If a feature exists only in one front-end (e.g., interactive review only in GUI), the underlying capability still lives in core; only the presentation differs.
**Demo subfolder**: row-limited, watermarked, file-size-capped Streamlit app for public deployment. Same core, different front-end constraints.
**Demo subfolder rule**: The `demo/` folder contains a constrained Streamlit app for public deployment to Streamlit Community Cloud. Constraints: row limit (e.g., 100 rows max output), no file save, watermark on output, sample dataset only or strict file-size cap. Same core library, different front-end constraints.
---
## 3. Cross-Platform Build Pipeline
## 3. Build pipeline
### 3.1 Tooling
| Concern | Tool |
|---|---|
| Bundling Python + scripts into a standalone binary | PyInstaller |
| GUI framework | **Streamlit** |
| Browser launch from launcher | Python `webbrowser` module (stdlib) |
| CLI framework | Typer |
| Windows installer wrapper | Inno Setup (free) |
| macOS bundle format | `.app` packaged in `.dmg` |
| macOS code signing & notarization | `codesign` + `notarytool` (built into Xcode command line tools) |
| Linux distribution format | AppImage (primary) + plain tarball (fallback) |
| CI / automated builds | GitHub Actions (free tier handles all three OS runners) |
| Hosted demo | Streamlit Community Cloud (free) or $5/mo VPS |
|---------|------|
| Bundling | PyInstaller |
| GUI | Streamlit |
| CLI | Typer |
| Browser launch | stdlib `webbrowser` |
| Win installer | Inno Setup (free) |
| macOS sign+notarize | `codesign` + `notarytool` |
| Linux | AppImage (primary) + tarball fallback |
| CI | GitHub Actions matrix |
| Demo host | Streamlit Community Cloud (free) |
### 3.2 Build Outputs (what the buyer downloads)
### 3.2 Build outputs
| OS | File | Buyer experience |
|----|------|------------------|
| Win | `*-Setup-1.0.exe` | Wizard → desktop shortcut "Launch Bundle" → browser opens. CLI on PATH. |
| macOS | `*-1.0.dmg` | Drag to Applications. Signed + notarized. |
| Linux | `*-1.0.AppImage` | `chmod +x`, double-click. |
| Platform | Output file | Buyer experience |
|---|---|---|
| Windows | `BundleName-Setup-1.0.exe` | Double-click installer, click through wizard. Desktop shortcut "Launch Bundle" runs `launcher.py`, which starts the local Streamlit server and opens default browser to `http://localhost:8501`. CLI executables also installed and on PATH. |
| macOS | `BundleName-1.0.dmg` | Double-click DMG, drag app to Applications. Signed and notarized. Launching the app runs the launcher, which starts the local server and opens the browser. CLI binaries shipped in the app bundle. |
| Linux | `BundleName-1.0.AppImage` | Mark executable, double-click. AppImage runs the launcher, opens browser. Tarball fallback also includes CLI binaries. |
### 3.3 PyInstaller
The **default buyer experience on every platform is**: double-click, browser opens, work done. The CLI is present, documented, and on PATH for users who want it.
- `--onefile` for Linux, `--onedir` for Win/macOS (faster startup, easier signing).
- Two entry points: GUI launcher + CLI binaries.
- Streamlit hooks needed: `streamlit`, `altair`, `pyarrow` data dirs.
- Custom `hook-streamlit.py` per documented pattern.
- Budget: 1-3 days first time. Reusable after.
**Browser-launch UX mitigation** (per DECISIONS.md Section 4c tradeoff): The launcher script displays a brief "Starting your data tool..." console message before opening the browser. The Streamlit app's first page includes a one-line note: *"This tool runs locally in your browser and does not use the internet."* Install email reinforces the same message.
### 3.4 Streamlit launcher
### 3.3 PyInstaller Configuration
1. Find free port (don't hardcode 8501).
2. Set env: `STREAMLIT_SERVER_HEADLESS=true`, `STREAMLIT_BROWSER_GATHER_USAGE_STATS=false`, `STREAMLIT_SERVER_PORT={port}`.
3. Start Streamlit programmatically in a thread.
4. Poll port until ready.
5. Open browser to `http://localhost:{port}`.
6. Keep launcher alive while server runs.
Single `.spec` file per bundle, parameterized for OS. Key settings:
Optional v1.1: wrap with `pywebview` to eliminate browser-launch UX. Defer until support tickets show meaningful confusion.
- `--onefile` for Linux (single AppImage), `--onedir` for Windows and macOS (faster startup, easier signing).
- All dependencies bundled. No internet required at runtime.
- Hidden imports declared explicitly for pandas/openpyxl/Streamlit edge cases (PyInstaller's auto-detection misses some).
- Icon files per platform (`.ico` for Windows, `.icns` for macOS, `.png` for Linux).
- **Two entry points per bundle**: the GUI launcher (default, what the desktop shortcut runs) and the CLI binaries.
- **Streamlit-specific PyInstaller hooks**: include the `streamlit` data directory, the `altair` data directory (Streamlit dependency), and the `pyarrow` C extensions. Add a custom hook file (`hook-streamlit.py`) per the documented pattern. Budget 1-3 days the first time getting the spec right; reuse across all subsequent bundles.
### 3.5 macOS pipeline
### 3.4 Streamlit Launcher Pattern
The launcher script handles starting the local Streamlit server in a way that survives PyInstaller bundling. Conceptual outline:
1. Find a free local port (avoid hardcoding 8501 in case of conflict).
2. Set Streamlit environment variables: `STREAMLIT_SERVER_HEADLESS=true`, `STREAMLIT_BROWSER_GATHER_USAGE_STATS=false`, `STREAMLIT_SERVER_PORT={port}`.
3. Start Streamlit programmatically (via `streamlit.web.cli.main_run` or `bootstrap.run`) in a background thread.
4. Wait for the port to accept connections (poll with timeout).
5. Open the buyer's default browser to `http://localhost:{port}` via `webbrowser.open()`.
6. Keep the launcher process alive while the server runs. Detect server shutdown and exit cleanly.
Optional v1.1 enhancement: replace step 5 with a `pywebview` window that wraps the local server. Eliminates the "default browser opens" UX surprise. Adds a dependency and some packaging complexity. Defer until support tickets show the browser-launch is causing meaningful confusion.
### 3.5 macOS Signing & Notarization Pipeline
Required setup (one-time):
1. Enroll in Apple Developer Program ($99/yr - see BUSINESS.md Section 10).
2. Generate Developer ID Application certificate via Apple Developer portal.
3. Install certificate in macOS keychain on the build machine (or store as encrypted GitHub Actions secret for CI).
4. Generate an app-specific password for `notarytool`.
Build-time flow (automated):
1. PyInstaller produces unsigned `.app`.
2. `codesign --deep --force --options runtime --sign "Developer ID Application: [Your Name]" BundleName.app`
3. Package into `.dmg`.
4. Submit `.dmg` to Apple notary service: `xcrun notarytool submit BundleName.dmg --wait`.
5. Staple the notarization ticket: `xcrun stapler staple BundleName.dmg`.
6. Output is the final, distributable `.dmg`.
2. `codesign --deep --force --options runtime --sign "Developer ID Application: ..." App.app`.
3. Package as `.dmg`.
4. `xcrun notarytool submit *.dmg --wait`.
5. `xcrun stapler staple *.dmg`.
Buyers on macOS see no Gatekeeper warnings. Clean install.
Setup: Apple Developer Program ($99/yr), Developer ID cert in Keychain, app-specific password.
### 3.6 Windows Pipeline
### 3.6-3.7 Win + Linux
1. PyInstaller produces `BundleName/` folder with launcher `BundleName.exe` (which opens the GUI in browser) plus CLI binaries plus dependencies.
2. Inno Setup script wraps the folder into `BundleName-Setup-1.0.exe`.
3. Installer creates Start Menu entry, desktop shortcut (launches GUI), optional Add/Remove Programs entry, and adds CLI binaries to PATH.
4. Optional Windows code signing certificate (~$200-400/yr from a CA) eliminates SmartScreen warnings. **Not required at launch**; revisit if SmartScreen friction shows up in support tickets.
- **Win**: PyInstaller `--onedir` → Inno Setup wraps → installer adds Start Menu, desktop shortcut, PATH entries. Optional code-signing cert ($200-400/yr) if SmartScreen friction.
- **Linux**: PyInstaller → `appimagetool` wraps. `.tar.gz` fallback for distros where AppImage fails.
### 3.7 Linux Pipeline
1. PyInstaller produces single-file binaries per entry point.
2. Wrap in AppImage using `appimagetool` (free, well-documented). AppImage runs the launcher as the default target.
3. Provide a plain `.tar.gz` fallback for users on distributions where AppImage fails. Tarball includes both GUI launcher and CLI binaries plus a `run.sh`.
4. No signing required on Linux; users execute `chmod +x` then double-click or run.
### 3.8 CI Build Matrix
GitHub Actions builds all three platforms on tagged release:
### 3.8 CI matrix
```yaml
# Conceptual, full file lives in ci/build.yml
strategy:
matrix:
os: [windows-latest, macos-latest, ubuntu-latest]
```
Result: one git tag triggers three platform builds. Artifacts upload to GitHub Releases. Manual step: copy artifacts to Gumroad / Lemon Squeezy product page.
Tag a release → 3 platform artifacts upload to GitHub Releases. Manual: copy to Gumroad / Lemon Squeezy.
### 3.9 Hosted Demo Deployment
### 3.9 Hosted demo
Separate from the desktop build pipeline. The `demo/streamlit_app.py` entry point is deployed to Streamlit Community Cloud:
1. Connect the GitHub repo to Streamlit Community Cloud (one-time).
2. Configure the app to deploy from the `demo/` folder, main branch.
3. Set deployment-time environment variables (e.g., row limits, watermark flag).
4. App is publicly accessible at a `*.streamlit.app` URL. Link from Gumroad landing page.
5. Optional: custom domain via CNAME (free with Streamlit Community Cloud as of last check; verify before locking).
If Streamlit Community Cloud is ever unsuitable (rate limits, bandwidth, branding requirements), fall back to a $5/mo VPS running the demo via Docker. Same `demo/streamlit_app.py`, different host.
---
`demo/streamlit_app.py` → Streamlit Community Cloud. Configure deployment in Streamlit UI. Custom domain via CNAME (verify policy at deploy time). Fall back to $5/mo VPS if rate limits / branding constraints hit.
## 4. Libraries
| Purpose | Library |
|---|---|
| GUI framework | Streamlit |
| CLI framework | Typer |
| Data manipulation | pandas, openpyxl, numpy |
| Fuzzy string matching | rapidfuzz |
| File encoding detection | charset-normalizer |
|---------|---------|
| GUI | streamlit |
| CLI | typer |
| Data | pandas, openpyxl, numpy |
| Fuzzy match | rapidfuzz |
| Phone parsing | phonenumbers |
| Encoding detect | charset-normalizer |
| Logging | loguru |
| Progress bars | tqdm (CLI), `st.progress` (GUI) |
| Validation | pydantic (optional) |
| Reports | ReportLab (PDF), pandas styling (Excel) |
| Optional native window wrap | pywebview (deferred to v1.1 if needed) |
| Mojibake (optional) | ftfy |
| Reports | reportlab |
`requirements.txt` (current bundle, v1.3):
## 5. Coding standards
### 5.1 Code
- PEP 8 + type hints on public functions.
- Docstrings on every module + public function.
- `pathlib.Path` for paths, never string concat.
- All I/O explicitly UTF-8-aware.
- No platform-specific shell calls.
- pytest for `core/`, not UI.
- Errors raise via `src.core.errors` hierarchy (Section 7).
### 5.2 GUI UX (load-bearing per DECISIONS.md §4b)
- **Works out of the box** — drop file → useful result with zero config.
- **Sensible defaults visible everywhere**.
- **Progressive disclosure** — basic = file uploader + run button + results; rest in `st.expander`.
- **Plain-English labels**; technical detail in `help=` tooltip.
- **Dry-run / preview by default**.
- **Identical core to CLI**.
- **Local-first messaging** — "runs locally in your browser, no internet" line on every page.
### 5.3 Functional scope (load-bearing per DECISIONS.md §4a)
- Each script ships **complete coverage of the workflow it names**, including features Excel does for free.
- Boundary = the named workflow. Dedup includes normalization + survivor + audit; not format conversion or charting.
## 6. System requirements
**Buyer runtime**: Win 10/11 64-bit · macOS 11+ · Linux glibc 2020+ · modern browser · ~400-500 MB disk · no internet.
**Developer**: Python 3.10+ · PyInstaller · Inno Setup (Win) · Xcode CLT (macOS) · Apple Developer Program $99/yr · Git + GitHub.
## 7. Error handling (`src/core/errors.py`)
Structured hierarchy for friendly messages + maintainable trace context:
```
streamlit>=1.30
pandas
openpyxl
numpy
typer
rapidfuzz
charset-normalizer
loguru
tqdm
reportlab
pyarrow # Streamlit dependency, declare explicitly for PyInstaller clarity
altair # Streamlit dependency, declare explicitly for PyInstaller clarity
DataToolsError # base; carries path/column/operation/suggestion/cause
InputValidationError(ValueError) # bad arg / wrong type
ConfigError(ValueError) # bad config / options
FileFormatError(ValueError) # file isn't what we expected
FileAccessError(OSError) # I/O failure (perms, disk, missing)
```
---
**Subclassing rule**: every subclass extends a stdlib base (`ValueError` or `OSError`) so existing `except OSError` / `except ValueError` handlers still catch them.
## 5. Coding Standards
**Helpers**:
- `ensure_dataframe(value, function=...)` — uniform DataFrame guard at every public entry.
- `ensure_choice(value, name=, choices=)` — uniform enum/literal guard.
- `wrap_file_read(path, op, exc)` / `wrap_file_write(...)` — tag OSError with file path + Windows-aware permission tip.
- `format_for_user(exc, context=)` — single string for `st.error()` / CLI stderr.
### 5.1 Code Standards
GUI / CLI handlers use `format_for_user()` so the user always sees: file path, operation, underlying error class, recovery suggestion.
- PEP 8 + type hints on all public functions.
- Docstrings on every module and public function.
- `--help` output (CLI) that a non-technical user can act on.
- Graceful error handling with human-readable messages, not stack traces. Errors must name the problem AND the likely fix where possible (e.g., "Column 'email' not found. Available columns: name, phone. Did you mean 'phone'?").
- All file paths handled via `pathlib.Path`, never string concatenation. Cross-platform correctness depends on this.
- All file I/O explicitly UTF-8-aware: detect encoding on input (charset-normalizer), write UTF-8 on output. Windows defaults to cp1252 otherwise.
- No platform-specific shell calls. If absolutely needed, branch on `sys.platform`.
- Unit tests for core logic (pytest). Tests target `core/`, not UI front-ends. Tests run on all three OS runners in CI.
- Semantic versioning per bundle.
- **Core/UI separation**: never put business logic in `cli.py` or `gui/`. If a CLI command and a GUI button do "the same thing," they call the same function in `core/`.
## 8. Per-bundle status
### 5.2 UX Standards (GUI / Streamlit) - load-bearing per DECISIONS.md Section 4b
| Bundle | Status |
|--------|--------|
| Data Cleaning Mastery | 3/9 tools Ready (Dedup, Text Cleaner, Format Standardizer); 6 stubs |
| Automated Business Reporting | Not started |
| Ecommerce Data Pipeline | Not started |
| Small Business Finance | Not started |
| Marketing Public Data Aggregation | Not started |
| AI Ecommerce Aggregation (Shopify Pet) | Not started |
- **Works out of the box**: dropping a file into the Streamlit `st.file_uploader` must produce a useful result with zero configuration.
- **Sensible defaults visible everywhere**: every `st.selectbox`, `st.slider`, `st.checkbox` has a default, the default is shown, the user is not forced through a config screen on first run.
- **Progressive disclosure**: basic view shows file uploader + go button + results. Advanced options live in `st.expander("Advanced options")` panes.
- **Plain-English labels**: no technical jargon in primary UI. `help=` parameter on widgets carries technical detail for users who want it.
- **Dry-run / preview by default**: user sees what would change before any file is written. Original input is never modified.
- **Single-page completion**: basic task completes on a single Streamlit page. Multi-page apps are for separate scripts in the bundle, not for multi-step wizards within one script.
- **Identical core to CLI**: any capability available in CLI is available in GUI, and vice versa. The only legitimate divergence is interactive review (GUI-natural) and scripted/scheduled execution (CLI-natural).
- **Local-first messaging**: every GUI page includes the line *"This tool runs locally in your browser and does not use the internet"* in a small, persistent location (e.g., footer or sidebar).
## 9. Open decisions
### 5.3 Functional Scope Standard - load-bearing per DECISIONS.md Section 4a
- **pywebview wrap** — defer until support tickets show browser-launch confusion.
- **Win code signing** — defer until SmartScreen drives volume. Cost ~$200-400/yr.
- **Auto-update mechanism** — none at launch. Email-delivered updates. Revisit at 100+ buyers/bundle.
- **Demo hosting migration** — Streamlit Community Cloud → $5/mo VPS if rate/brand limits hit.
- **Code obfuscation** — none; license text + bundle complexity sufficient at $49-79.
- **Telemetry** — none. Consider opt-in privacy-respecting only post-launch.
- Each script ships with **complete coverage of the workflow it names**, including features available free elsewhere (e.g., exact-match dedup).
- Scope boundary is the workflow, not "things adjacent to the workflow." A deduplicator includes normalization, survivor selection, audit. It does not include format conversion or charting; those belong elsewhere in the bundle.
## 10. Script boundaries — 04 (Missing Values) vs 06 (Outliers)
---
Deliberately separate. Confluent original spec was wrong.
## 6. System Requirements
| Script | Owns |
|--------|------|
| 04 Missing Value Handler | "What's not there." Disguised nulls (`N/A`, `-`, sentinel codes), missingness patterns, imputation, drop-by-threshold. |
| 06 Outlier Detector | "What shouldn't be there." z-score / IQR / modified-z, multivariate (Isolation Forest, Mahalanobis), domain rules, winsorization. |
**For buyers (runtime)**:
- Windows: Windows 10 or 11, 64-bit.
- macOS: macOS 11 (Big Sur) or later, Apple Silicon or Intel.
- Linux: any glibc-based distribution from 2020 onward (Ubuntu 20.04+, Fedora 33+, etc.).
- A modern default browser (Chrome, Edge, Firefox, Safari from the last 3 years). Used to display the local GUI; no internet required.
- ~400-500 MB free disk space (Streamlit packaging is larger than alternatives; this is an accepted tradeoff per DECISIONS.md Section 4c).
- No internet required after install. No Python install required ever.
**Run order**: 04 before 06. Outlier stats on data with `NaN` / sentinels are mathematically poisoned (means dragged, IQR widens, false negatives).
**For developers (you)**:
- Python 3.11+.
- PyInstaller, Inno Setup (Windows builds), Xcode command line tools (macOS builds).
- Apple Developer Program membership ($99/yr) for macOS distribution.
- Git + GitHub account (for CI builds and Streamlit Community Cloud deployment of demos).
**Pipeline order** (Pipeline Runner enforces): 02 → 03 → 04 → 05 → 06 → 07 → 08. 01 is order-flexible.
---
**Contested cases**:
- Whitespace-only cell — 02 trims to empty; 04 then flags empty as null.
- `-999` sentinel — 04 converts to `NaN` first; 06 then computes stats.
- Suspicious-but-plausible (age 110) — 06 territory.
## 7. Per-Bundle Technical Notes
## 11. Per-script functional specs
| Bundle | Status | Tech notes |
|---|---|---|
| Data Cleaning Mastery | Lead, 1/9 scripts complete (CLI only; needs Streamlit GUI port) | Cleaning, dedup, text hygiene, standardization, missing-value handling, outlier detection, type coercion, reporting. Scripts 04 (missing values) and 06 (outliers) are deliberately separate concerns; 04 runs first to neutralize sentinel codes before 06 computes statistics (see Section 9). Script 02 (text cleaner) runs first in the pipeline to normalize whitespace and special characters before any other operation. v1.3 spec: Streamlit GUI required at launch, with hosted demo deployed to Streamlit Community Cloud. |
| Automated Business Reporting | Not started | Aggregation -> styled PDF/Excel output |
| Ecommerce Data Pipeline | Not started | Extract -> clean -> export workflow |
| Small Business Finance | Not started | Bookkeeping summaries, simple reconciliation |
| Marketing Public Data Aggregation | Not started | Public API + scraping with respect for robots.txt and ToS |
| AI Ecommerce Aggregation (Shopify Pet) | Not started | Optional LLM enhancement, requires API key from buyer |
Specs live in this section as scripts enter active build. Each follows the Tier 1/2/3 structure with explicit strategic framing (what's the market gap given some of this is free elsewhere).
---
### 11.1 `01_deduplicator.py` — Smart duplicate removal
## 8. Open Technical Decisions
**Status**: Ready. Tier 1 mostly built. Streamlit GUI port complete.
GUI framework choice is now **closed** (Streamlit, locked v1.3 - see DECISIONS.md Section 4c).
**Market gap**: fuzzy match quality of OpenRefine, with the zero-learning UX of Excel, sold once for under $100, runs locally.
Remaining open items:
**Tier 1**:
- **Input**: auto-detect encoding (UTF-8, UTF-8-BOM, Latin-1, cp1252) · delimiter · header row · CSV/TSV/XLSX/XLS · multi-sheet picker · streaming for files > RAM.
- **Matching**: exact + 3 fuzzy algos (Levenshtein / Jaro-Winkler / token-set) · per-column normalizers (5 types) · configurable threshold per strategy · multi-strategy OR.
- **Survivor**: keep first / last / most-complete / most-recent · merge mode (fill blanks from losers).
- **Trust**: dry-run preview by default · interactive review for gray-zone matches · confidence score per match · match-group export.
- **Audit**: timestamped log · removed-rows separate file · input never modified · idempotent.
- **Config**: save/load JSON profiles · sensible auto-detect defaults.
- **UX**: human `--help` · progress bar > 10k rows · errors name row + column + value + suggestion.
- **pywebview wrap of Streamlit launcher**: Optional v1.1 enhancement to eliminate the "browser opens" UX surprise. Defer until support tickets show meaningful buyer confusion. Cost: extra dependency, more PyInstaller complexity. Benefit: native-window UX.
- **Windows code signing**: Currently unsigned. Revisit if SmartScreen warnings drive support volume. Cost: ~$200-400/yr.
- **Auto-update mechanism**: None at launch. Email-delivered version updates. Revisit at 100+ paying customers per bundle.
- **Demo deployment hosting**: Streamlit Community Cloud at launch (free). Migrate to $5/mo VPS if rate limits, bandwidth, or branding constraints become an issue.
- **Code obfuscation**: Currently relying on license text + PyInstaller bundling. Decompilation is possible but unlikely for $49-79 products. Accept the risk.
- **Telemetry**: None. Consider privacy-respecting opt-in usage telemetry post-launch to inform roadmap, but only if explicit and disclosed.
**Tier 2**: numeric/date tolerance · phonetic match (Soundex, Metaphone) · blocking/indexing · watch-folder.
---
**Tier 3**: ML scoring · cross-file dedup · cron · Shopify/Klaviyo API direct.
## 9. Script Boundaries: 04 (Missing Values) vs 06 (Outliers)
### 11.2 `02_text_cleaner.py` — Character-level hygiene
The two scripts are deliberately separate. Original spec ("missing-value handler also does basic outlier flagging") was wrong: it conflated two different statistical operations and would have produced overlapping CLI flags, confused buyers, and brittle code.
**Status**: Ready. Tier 1 built.
### 9.1 Boundary
**Market gap**: one-click correctness for the dirty-CSV failure modes that cause silent VLOOKUP misses.
**`04_missing_value_handler.py` owns "what's not there"**:
- Detect disguised nulls: `NaN`, empty string, `"N/A"`, `"-"`, `"unknown"`, whitespace-only, sentinel codes (`-999`, `9999`, etc.).
- Missingness pattern analysis (which columns co-miss).
- Imputation strategies: mean, median, mode, forward-fill, KNN (optional), regression (optional).
- Required-field enforcement (drop rows missing a required column).
- Drop rows or columns by missingness threshold.
**Boundary**:
- 02 — whitespace, Unicode normalize, smart-char fold, BOM, line endings, zero-width, control chars, case ops. Writes to disk.
- 03 — dates, currencies, names, phones, addresses (display formatting).
- 04 — disguised nulls.
- 01 — `normalize_string` is *match-time* only, distinct from 02's *write-time* policy.
**`06_outlier_detector.py` owns "what shouldn't be there"**:
- Univariate statistical detection: z-score, IQR, modified z-score (MAD-based).
- Multivariate detection: Isolation Forest, Mahalanobis distance.
- Domain-rule violations (age > 120, negative quantity, future dates in historical data).
- Winsorization / capping as optional remediation.
- Distribution shape diagnostics.
**Tier 1 ops** (each toggleable; defaults shown for `excel-hygiene`):
1. Trim leading/trailing whitespace — ON
2. Collapse internal whitespace runs — ON
3. NFC normalize — ON
4. NFKC compatibility fold — OFF (lossy, opt-in via `paranoid` preset)
5. Smart-char fold (curly quotes, em/en-dash, NBSP, ellipsis) — ON
6. Zero-width / invisible char strip — ON
7. BOM strip — ON
8. Control-char strip (preserve `\t\n\r`) — ON
9. Line-ending normalize (CRLF/CR → LF inside cells) — ON
10. Case conversion (UPPER / lower / Title / Sentence) — OFF, per-column
### 9.2 Run Order
**Scope**: per-column selection · skip-list · operates on string-typed columns only.
04 must run before 06. Reason: outlier statistics computed on data still containing `NaN` or sentinel codes are mathematically poisoned. Means and standard deviations get dragged, IQR widens, false negatives explode.
**Trust**: dry-run by default · per-cell change log (capped 1000, `--full-changelog` removes cap) · 3 output files mirroring dedup · idempotent.
The Master Orchestrator (script 09) enforces this order. CLI users running scripts manually get a warning printed by 06 if it detects unhandled sentinel patterns in the input.
**Config**: 3 presets (`minimal` / `excel-hygiene` (default) / `paranoid`) · save/load JSON.
Pipeline-wide order enforced by the orchestrator: `02_text_cleaner` → `03_format_standardizer` → `04_missing_value_handler` → `05_column_mapper_enforcer` → `06_outlier_detector` → `07_multi_file_merger` → `08_validator_reporter`. Script `01_deduplicator` is order-flexible; it normalizes whitespace and case internally for matching purposes regardless of upstream cleaning, so it can run before or after `02_text_cleaner` depending on the buyer's workflow.
### 11.3 `03_format_standardizer.py` — Per-domain canonical forms
### 9.3 Contested Cases
**Status**: Ready. Full Tier 1 + most Tier 2 built. 199-row buyer corpus passing.
**Use cases that prove 04 and 06 are distinct concerns** (not just naming differences):
**Market gap**: unify dates / phones / emails / addresses / names / currencies / booleans across messy ETL inputs without buyer writing code.
- *Bank export with blank fee columns*: pure 04 job. The fees aren't outliers, they're missing. Imputation or drop-by-threshold is the right tool.
- *Sales data with one $1M order in a $50-average column*: pure 06 job. Nothing is missing; one row is statistically extreme. Z-score or IQR catches it.
- *Survey data where `999` means "refused to answer"*: needs both, in order. 04 converts `999` to `NaN` per `--sentinels`, then 06 computes statistics on the cleaned column.
**Domains**:
| Domain | Default canonical | Notable handling |
|--------|-------------------|------------------|
| Date | ISO 8601 (`YYYY-MM-DD`) | MDY/DMY, Excel serial, Unix timestamp (s + ms), longform months, year-month, quarter notation, French/German/Spanish month dictionaries (opt-in), buried-date regex, error sentinels for invalid dates |
| Phone | E.164 + `;ext=N` | libphonenumber, 001 international prefix handling, error sentinels for placeholders / multi-number / contamination |
| Email | lowercase + trim | display-name extraction, mailto/angle-bracket strip, smart-quote unwrap, optional `--gmail-canonical` mode |
| Address | USPS-canonical (`expand=False`) or expanded (`expand=True`) | state-name → 2-letter, multi-line collapse, PO Box normalize, state-code preservation regardless of input case |
| Name | smart Title Case | Mc/Mac/O'/D' inner caps, hyphen segments, particle lowercasing (von/van/de/da), comma-format reversal, period stripping for titles/suffixes/initials, PhD/MD acronym preservation, conservative mode |
| Currency | bare number (dot decimal) | auto-detect EU vs US separators, space-thousands, Swiss apostrophe, accounting parens, optional ISO code preservation |
| Boolean | `True`/`False` (configurable) | accepts `yes`/`no`/`y`/`n`/`1`/`0`/`on`/`off` |
Sentinel values like `-999` are *both* disguised missing *and* statistical outliers. Resolution: 04 owns sentinel detection and converts them to `NaN` (or imputes per user choice) before 06 sees the data. Sentinel patterns are configurable in 04 via `--sentinels` flag.
**Per-domain `error_policy`**: `"passthrough"` (default) keeps the original; `"sentinel"` emits `<error: <reason>>` for cases like Feb 30, double @, percentages mistaken for currency, etc.
Suspicious-but-plausible values (e.g., age = 110): 06's territory. Not missing; just rare.
**Pipeline**: `standardize_dataframe(df, options)` runs per-column with `column_types: dict[str, FieldType]`. Returns `StandardizeResult` with `cells_changed`, `cells_unparseable`, change audit. Warns when > 10% of typed cells fail to parse.
Whitespace-only cells (e.g., `" "`) are a contested case between 02 (text cleaner) and 04 (missing value handler). Resolution: 02 trims first, leaving an empty string. 04 then detects empty strings as disguised nulls per its existing logic. This means 02 must run before 04 in any pipeline that uses both. The orchestrator enforces this; CLI users get a warning if 04 detects whitespace-only cells suggesting 02 was skipped.
**Presets**: `us-default`, `european`, `uk`, `iso-strict`, `legacy-us`. Custom abbreviations via `extra_abbreviations`.
### 9.4 Shared Plumbing
### 11.4 Upload-time analyzer (`src/core/analyze.py`)
Both scripts emit:
- A flagged-row report with column, row index, original value, action taken.
- A timestamped log file in `logs/`.
- An optional cleaned output file.
Read-only advisory pass on every upload. Emits `Finding` objects:
Report and log formats are identical between the two scripts. Implemented via shared helpers in `src/core/` to avoid drift.
| Field | Meaning |
|-------|---------|
| `id` | Stable identifier (never localized) |
| `severity` | `info` / `warn` / `error` (only `error` blocks gate) |
| `confidence` | `high` (round-trip safe) / `medium` (preview) / `low` (heuristic) |
| `fix_action` | id of algorithm in `fixes.py` (empty for informational-only) |
| `pre_applied` | `true` if fix already ran during read pass |
| `tool` | owning tool id (or empty for file-level) |
| `count` | cells / rows affected |
| `description` | one-sentence human summary |
| `column` | column name (None for file-level) |
| `samples` | up to 5 `(row, col, value)` examples |
---
Entry point: `analyze(source, *, sample_rows=1000, repair_result=None, encoding_override=None)`. `encoding_override` skips charset detection — the hook that lets the Review page recover from misdetections.
## 10. Per-Script Functional Requirements
### 11.5 CSV-normalization gate (`src/core/normalize.py`, `fixes.py`)
This section captures the full functional spec for each script, beyond the one-line description in USER-GUIDE.md Section 2. Specs answer "what does v1 need to ship to be best-of-class for the target buyer." Promoted from chat-history-only into docs in v1.6 to prevent silent drift.
Two paths:
1. **Auto-fix** — `auto_fix(df, findings)` applies every `confidence="high"` finding whose `fix_action` is registered.
2. **Per-finding decisions** — `apply_decisions(df, findings, decisions)` accepts `Decision(finding_id, action, payload)` with action `"auto"|"skip"|"modified"`.
**Note on script status**: a script labeled "Working" in the bundle status table means it has CLI execution and basic correctness, NOT that it implements every Tier 1 item below. Tier 1 is the v1 launch target; the current code may implement a subset.
Returns `NormalizationResult` with `cleaned_df`, `cleaned_bytes` (UTF-8 CSV), `applied`, `skipped_findings`, `pending_findings`, `blocking_findings`.
### 10.1 `01_deduplicator.py` - Smart duplicate removal
`is_normalized(findings, result)` re-runs `analyze()` against cleaned bytes; returns False if any high-confidence detector still fires (the strict contract tool pages depend on).
**Current implementation status**: `01_deduplicator.py` exists and works for exact match plus basic fuzzy with configurable subset columns and timestamped logs (the description in USER-GUIDE.md Section 2 reflects current state). It does NOT yet implement most Tier 1 items below. Tier 1 is the v1 launch target, not current state. The Streamlit GUI port is the natural moment to close this gap.
**Fix registry**: `@register("fix_id")` decorates `(df, payload) → (new_df, n_cells_changed)`. New fix = one entry in `analyze.py` `FIX_*` constants + one detector emitting that `fix_action` + one registered function. No other call sites change.
**Strategic framing**: Excel's built-in Remove Duplicates handles exact match for free. Pandas `drop_duplicates()` handles it for free in code. A $49-$79 dedup tool that ships "exact + basic fuzzy" loses to Excel for free or to OpenRefine for free. The fuzzy matching has to be the product, not a checkbox. The market gap this script targets is "fuzzy match quality of OpenRefine, with the zero-learning-curve UX of Excel, sold once for under $100, runs locally" (see BUSINESS.md Section 4a).
### 11.6 Review page (`src/gui/pages/0_Review.py`)
#### Tier 1: Must-ship for v1 to be best-of-class
1. Detected encoding + override picker (16 codepages + custom).
2. One expandable card per finding (sorted by severity then confidence) with: decision radio (Auto/Skip/Customize), live before/after preview built by running the registered fix on `Finding.samples`, payload editor for fixes that take user input.
3. Apply persists `NormalizationResult` keyed by upload SHA-256; tool pages refuse to load until hash matches.
4. `⚙️ Advanced output options` expander: per-download encoding + delimiter + line terminator. `_build_output_bytes()` returns `(bytes, error_message)`; lossy fallbacks emit a warning the page surfaces.
**Input handling**
1. Auto-detect file encoding (UTF-8, UTF-8-BOM, Latin-1, Windows-1252). Failure to handle this correctly is the #1 reason CSV tools crash on real-world business data.
2. Auto-detect delimiter (comma, tab, semicolon, pipe).
3. Read CSV, TSV, XLSX, XLS. For XLSX, support multi-sheet workbooks (let user pick or process each).
4. Handle files larger than RAM via chunked / streaming processing. A 500MB customer export should not crash the tool.
5. Header row detection with manual override.
Gates the entire tool sidebar via `require_normalization_gate()` in `src/gui/components/_legacy.py`.
**Matching**
6. Exact match with configurable subset columns.
7. Fuzzy match algorithms: Levenshtein, Jaro-Winkler, token-set ratio (rapidfuzz library). Multiple algorithms, not one. Different data types match better with different algorithms.
8. Per-column normalization before comparison:
- Email: lowercase, strip whitespace, strip Gmail dots, strip `+tag` suffixes.
- Phone: strip formatting and country codes, normalize to E.164.
- Name: strip titles (Mr/Ms/Dr), strip suffixes (Jr/III), collapse whitespace, optional case-fold.
- Address: USPS-style abbreviation normalization (St/Street, Ave/Avenue, Apt/#).
- Generic string: trim, collapse internal whitespace, optional case-fold.
9. Configurable similarity threshold (e.g., 85%, 90%, 95%) per match strategy.
10. Multi-strategy matching with OR logic: "match if email is exact OR (name fuzzy >90% AND phone exact)." This is what real-world dedup actually requires. Single-strategy match handles maybe 40% of cases.
### 11.7 Pre-parse repair (`src/core/io.py::repair_bytes`)
**Survivor selection (which row to keep when duplicates are found)**
11. Configurable survivor rules: keep first, keep last, keep most-complete (fewest empty cells), keep most-recent (date column), keep manually-selected.
12. Merge mode: instead of deleting losers, fill missing fields in survivor from losers. Common real ask: combine partial records into one complete record.
Byte-level pre-parse pass. **Order is meaningful**:
**Trust and review**
13. Dry-run / preview mode by default. Output shows what *would* be merged before any file is written. Non-negotiable for trust. Aligns with Section 5.2 visible-safety standard.
14. Interactive review mode for uncertain matches. For matches in the gray zone (e.g., 75-90% similarity), prompt user yes/no/skip with side-by-side diff. This is what justifies a paid product over free Excel. GUI-natural; CLI gets a reduced-form prompt loop.
15. Confidence score on every fuzzy match in the output.
16. Match group export: separate file showing every group of matched rows so user can audit.
**Audit and safety**
17. Full timestamped log of every action: which rows matched, on which strategy, with what score, which row survived, which fields were merged.
18. Removed-duplicates exported to a separate file (never silently destroyed).
19. Original input file is never modified. Output is always a new file.
20. Idempotency: running the tool twice on the same input with the same config produces the same output.
**Configuration**
21. Save / load configuration profiles. A user who deduplicates a Shopify customer export weekly should configure once, not every run.
22. Sensible defaults that work on a typical messy CSV with zero configuration. The first run must produce a useful result with no flags. Per DECISIONS.md Section 4b UX standards.
**UX**
23. `--help` (CLI) written for non-technical users with concrete examples, not a flag list.
24. Progress bar for files over ~10K rows.
25. Error messages name the row number, column, and value that caused the problem. No raw stack traces. Per Section 5.1.
26. Sample data (`samples/messy_sales.csv`) must demonstrate fuzzy match scenarios, not just exact dupes. Otherwise the demo doesn't sell.
#### Tier 2: Worth-considering for v1.1
27. Numeric tolerance for matching (prices within $0.01 considered same).
28. Date tolerance for matching (dates within N days considered same).
29. Phonetic matching (Soundex, Metaphone) for name fields with common misspellings.
30. Blocking / indexing for performance on large files (compare only rows that share a first letter or first three characters of a key field). Without this, fuzzy match on 100K rows is O(n²) and unusable. Move to Tier 1 if early buyers report performance complaints.
31. Watch-folder mode: auto-process any file dropped into a folder.
32. Geolocation-aware address matching (optional, requires bundled USPS data or third-party API).
#### Tier 3: Optional / later
33. Machine-learning-based match scoring (Dedupe.io territory; high complexity, marginal gain for this price point).
34. Multi-table joins for cross-file dedup.
35. Schedule / cron integration.
36. Direct Shopify / Klaviyo / Mailchimp API integration to dedupe in place. This would be a real differentiator for the Shopify niche specifically and is probably the right v2 direction if early sales validate the niche.
### 10.2 `02_text_cleaner.py` - Character-level hygiene
**Current implementation status**: Stub only. `src/gui/pages/2_Text_Cleaner.py` is a placeholder UI with disabled controls. No `src/core/text_clean.py`, no CLI, no tests. Tier 1 below is the v1 launch target; nothing in this section is built yet.
**Strategic framing**: Excel and the OS provide effectively nothing here. Find/Replace fixes one character at a time. Power Query's "Clean" strips control chars but ignores BOMs, smart quotes, NBSPs, and zero-width chars. OpenRefine has the operations buried under "Common transforms" where the buyer never finds them. Pandas users `df.applymap(str.strip)` and miss everything else.
The market gap this script fills: **one-click correctness for the dirty-CSV failure modes that cause "why won't this VLOOKUP match?"** Trailing spaces, NBSP-in-place-of-space, smart quotes pasted from Word, mojibake, BOMs from Excel's "Save As CSV UTF-8". The buyer doesn't know they need this script until it fixes a problem they have spent two hours on. Demo value is high: the before/after diff sells itself.
**Boundary clarification** (cross-references Section 9):
- 02 owns whitespace, Unicode normalization, smart-character folding, BOM strip, line-ending normalization, zero-width strip, control-char strip, case ops. Writes cleaned values back to disk.
- 03 (format standardizer) owns dates, currencies, names, phones, addresses.
- 04 (missing values) owns disguised nulls (`N/A`, `-`, `unknown`, sentinel codes). Whitespace-only cells: 02 trims first to empty string; 04 then detects empty as null (per Section 9.3).
- 01 (deduplicator) has its own `normalize_string` helper for *match-time* case-folding. That is a match-time policy and stays distinct from 02's *write-time* policy. The two will not be merged; 02 may use lower-level helpers but does not aggressively case-fold cleaned output by default.
#### Tier 1: Must-ship for v1 to be best-of-class
**Operations** (each independently toggleable; defaults given for the `excel-hygiene` preset)
1. Whitespace trim - leading/trailing on every cell. Default ON.
2. Internal whitespace collapse - multi-space and tabs-in-cells to single space. Default ON.
3. Unicode NFC normalization - combining-character forms folded to canonical (e.g., `e + U+0301` to single `é`). Default ON.
4. Unicode NFKC normalization - compat fold (`①` to `1`, `fi` to `fi`). Default OFF, lossy, opt-in only. Not part of any preset other than `paranoid`.
5. Smart-character folding - curly quotes to ASCII, em/en-dash to hyphen, ellipsis `…` to `...`, NBSP `U+00A0` to space. Default ON.
6. Zero-width / invisible character strip - `U+200B`, `U+200C`, `U+200D`, `U+2060`, mid-string `U+FEFF`. Default ON.
7. BOM strip - `U+FEFF` at the start of the first cell of the first column (covers the case where the I/O layer didn't catch it). Default ON.
8. Control character strip - `U+0000`-`U+001F` and `U+007F`, *except* preserve `\t`, `\n`, `\r`. Default ON.
9. Line-ending normalization - within multi-line cells, `\r\n` and bare `\r` to `\n`. Default ON.
10. Case conversion - UPPER / lower / Title / Sentence. Default OFF, per-column. Title case is "smart": preserves all-caps tokens (`USA`, `NASA`) and lowercases mid-string particles (`of`, `and`, `the`).
**Scope control**
11. Per-column selection - by default operate on string-typed columns only; numeric / datetime columns pass through untouched. User can pick columns explicitly via `--columns`.
12. Skip-list - exclude specific columns via `--skip` even if they match the string-dtype filter (e.g., free-text notes columns).
**Trust and audit**
13. Dry-run preview by default. Output shows N cells that would change in column X. `--apply` writes. Non-negotiable for trust. Same standard as the deduplicator.
14. Per-cell change log: `{input}_changes.csv` with (row, column, old, new, ops_applied). Capped to first N rows by default to avoid 50MB audit files; `--full-changelog` removes the cap.
15. Three output files on `--apply`: `{input}_cleaned.csv`, `{input}_changes.csv`, `logs/text_clean_{ts}.log`. Mirrors the deduplicator output shape.
16. Original input file is never modified.
17. Idempotency: `clean(clean(x)) == clean(x)` for every individual op and every preset. Asserted as a property test.
**Configuration**
18. Presets: `--preset excel-hygiene` (everything safe ON, NFKC OFF, case OFF), `--preset minimal` (only trim + collapse), `--preset paranoid` (everything including NFKC). Buyers should not have to learn 9 flags. Default preset when no flag given: `excel-hygiene`.
19. Save / load JSON config. Same shape and reuse pattern as `DeduplicationConfig`.
**UX**
20. `--help` written for non-technical users with concrete examples, not a flag dump. Per DECISIONS.md Section 4b.
21. Progress bar for files over ~10K rows.
22. Error messages name the row, column, and value that caused the problem. No raw stack traces.
23. Sample data (`samples/messy_text.csv`) demonstrates: smart quotes from Excel, NBSP-vs-space, BOM, mixed line endings, zero-width chars. The before/after diff is the demo.
#### Tier 2: Worth-considering for v1.1
24. Custom regex find/replace - power-user escape hatch, per-column.
25. Diacritic strip (`José` to `Jose`). Lossy; opt-in only.
26. Mojibake auto-repair - detect `é` to `é` patterns (UTF-8 read as Latin-1 then re-encoded) and fix. Standard tool: `ftfy`. Promote to Tier 1 if early buyers report this.
27. Punctuation normalization - all Unicode dash/quote/space variants folded; runs of punctuation collapsed.
28. Profile detector - scan a file and recommend which ops to enable based on what's actually present. Lowers config friction further.
#### Tier 3: Optional / later
29. Locale-aware case conversion (Turkish dotted/dotless `i`, German `ß`).
30. Custom character-class strip rules (regex-class).
31. Streaming / chunked write for very large files (defer until a buyer reports it).
#### Open decisions captured at spec time
- Smart-character folding default ON in `excel-hygiene` accepted as the right tradeoff: highest-impact use case, dry-run preview makes the change visible before commit.
- NFKC stays Tier 1 but OFF by default and excluded from `excel-hygiene`. Lossy by design.
- CLI surface: separate `src/cli_text_clean.py` module, matching the "one CLI binary per script on PATH" pattern in Section 3.2. Not a subcommand on the existing dedup Typer app.
- `ftfy` dependency deferred to Tier 2 (~5MB). Revisit if mojibake reports come in.
### 10.2.1 Upload-time analyzer (`src/core/analyze.py`)
The analyzer is a read-only, advisory pass that runs on every uploaded file before any tool page sees it. It produces a list of `Finding` objects, each carrying:
| Field | Type | Meaning |
|---|---|---|
| `id` | str | Stable identifier (`smart_punctuation_in_data`, `mixed_line_endings`, …). Never localized. |
| `severity` | `info` / `warn` / `error` | UX urgency. `error` is the only level that blocks the gate. |
| `confidence` | `high` / `medium` / `low` | Auto-fixability. **High** is round-trip safe, **medium** has known false-positive shapes, **low** is heuristic and opt-in. |
| `fix_action` | str | Stable id naming the algorithm in `src/core/fixes.py` that resolves this finding. Empty for informational-only findings. |
| `pre_applied` | bool | True when the fix already ran during the read pass (BOM strip, NUL strip, byte-level smart-quote fold). The gate treats these as already-resolved. |
| `tool` | str | Tool id that owns this concern (`02_text_cleaner`, `04_missing_handler`). Empty for file-level findings. |
| `count` | int | Cells / rows affected. |
| `description` | str | One-sentence human summary (banners, tooltips). |
| `column` | str / None | Column name when scoped to one column. |
| `samples` | list[(row, col, value)] | Up to 5 examples for the GUI to render. |
`analyze(source, *, sample_rows=1000, repair_result=None, encoding_override=None)` is the public entry point. `source` is a DataFrame or a path; `encoding_override` skips charset detection and uses the user's chosen codepage instead — this is the hook that lets the Review page recover from misdetections (cp1252-vs-cp1250 ambiguity, KOI8-R surfacing as Shift_JIS).
### 10.2.2 CSV-normalization gate (`src/core/normalize.py`, `src/core/fixes.py`)
A file enters tool pages only after passing the gate. The gate has two paths:
1. **Auto-fix** — `auto_fix(df, findings)` applies every `confidence="high"` finding whose `fix_action` is registered in `fixes.py`.
2. **Per-finding decisions** — `apply_decisions(df, findings, decisions)` accepts an explicit list of `Decision(finding_id, action, payload)` where action is `"auto" | "skip" | "modified"`.
Output is a `NormalizationResult` with:
- `cleaned_df` — the DataFrame after every applied fix.
- `cleaned_bytes` — UTF-8 CSV serialization for the download.
- `applied`, `skipped_findings`, `pending_findings`, `blocking_findings` — audit log + gate status.
`is_normalized(findings, result)` re-runs `analyze()` against the cleaned bytes and returns False if any high-confidence detector still fires — that's the strict contract tool pages depend on.
`fixes.py` is a registry: `@register("fix_id")` decorates a `(df, payload) -> (new_df, n_cells_changed)` function. Adding a new fix means appending one entry to `analyze.py`'s `FIX_*` constants, one detector that emits a Finding with that `fix_action`, and one registered function in `fixes.py`. No other call sites change.
### 10.2.3 Review page (`src/gui/pages/0_Review.py`)
Streamlit page that orchestrates the gate visually. Gates the entire tool sidebar via `require_normalization_gate()` in `src/gui/components.py`, which every tool page calls right after `hide_streamlit_chrome()`.
The page:
1. Surfaces the detected encoding plus an override picker (16 common codepages + custom-text fallback).
2. Renders one expandable card per finding, sorted by severity then confidence, with a decision radio (Auto / Skip / Customize), a live before/after preview built by running the registered fix on each `Finding.samples` value, and a payload editor for fixes that take user input (e.g. custom null-sentinel list for `replace_null_sentinels`).
3. Apply button persists a `NormalizationResult` keyed by upload SHA-256; tool pages refuse to load until the hash matches.
4. After apply, an `⚙️ Advanced output options` expander offers per-download encoding, delimiter, and line-terminator selection. The helper `_build_output_bytes(df, *, encoding, delimiter, line_terminator)` returns `(bytes, error_message)` — when the chosen encoding can't represent a character, falls back to `errors="replace"` and returns a warning the page surfaces.
### 10.2.4 Pre-parse repair (`src/core/io.py::repair_bytes`)
Byte-level pre-parse pass. Order is meaningful and each step is independently toggleable:
1. **Wide-encoding transcode** — UTF-16/UTF-32 → UTF-8. Has to run first because the byte-level NUL strip below would shred UTF-16 data (UTF-16 ASCII chars carry NUL as half of every 16-bit unit). Records `transcode_to_utf8` audit action; the analyzer surfaces it as a `csv_transcoded_to_utf8` info finding.
1. **Wide-encoding transcode** (UTF-16/32 → UTF-8) — must run first or NUL strip below shreds UTF-16.
2. **UTF-8 BOM strip** (file start only).
3. **NUL strip** — only meaningful after step 1, so genuine corruption (truncated C strings, half-binary exports) rather than encoding artifacts.
4. **Line-ending normalize** — CRLF and bare CR → LF. Bare CR confuses the C parser; the text-cleaner contract also calls for LF inside multi-line cells.
5. **Byte-level smart-quote fold** — curly / guillemet / double-prime → ASCII `"`. Only structural double-quote-equivalents; single curly quotes are deferred to the cell-level cleaner.
6. **Per-row delimiter repair** — when one row has +1 field and the merge candidate is currency-shaped (`$1,500.00` etc.), merge and quote.
3. **NUL strip** — only meaningful after step 1, so flags genuine corruption.
4. **Line-ending normalize** — CRLF + bare CR → LF.
5. **Byte-level smart-quote fold** — curly / guillemet / double-prime → ASCII `"` (only structural double-quote-equivalents; single curlies deferred to cell-level).
6. **Per-row delimiter repair** — when a row has +1 field and merge candidate is currency-shaped (`$1,500.00`), merge + quote.
`detect_encoding()` tries strict UTF-8 first and returns `"utf-8"` if the bytes decode cleanly. This was added because charset-normalizer fingerprints small files dominated by short non-ASCII sequences (e.g. zero-width chars at U+200B-class) as `mac_latin2` — but if the bytes are valid UTF-8, that's the right answer regardless of label.
### 10.3 - 10.9 (Future)
Functional specs for scripts 03 through 09 to be added when each script enters active build. The deduplicator (10.1) and text cleaner (10.2) specs are the template; specs for other scripts should follow the same Tier 1 / Tier 2 / Tier 3 structure with explicit strategic framing (what's the market gap this script fills, given that some of its functionality is available free elsewhere).
`detect_encoding()` tries strict UTF-8 first — charset-normalizer mislabels short-non-ASCII files as `mac_latin2`, but valid UTF-8 bytes mean UTF-8 regardless of label.