NV oOS Toolkit Data Ingestion

Reusable playbook for ingesting heterogeneous owner data into a Pro toolkit as structured rows, independent of filename or fixed column layout. First built for the food-beverage toolkit's open-format track (docs/project/proposals/078-fnb-data-ingestion-open-format-proposal.md), Phases 1–10. Complements mcp-ai-wpoos-toolkit-creation (build a toolkit) and mcp-ai-wpoos-toolkit-universalisation (de-demo / de-tenant a toolkit).

When to use this skill

• "Ingest owner files in as many formats as possible" / "auto-map if no mapping is provided"
• Adding CSV/TSV/JSON/JSONL/XLSX/ODS/PDF/photo import to a toolkit
• "Replicate the vitals-log ingestion" / bind loose records onto a canonical table
• Building or extending an ingest & review dashboard

1. Resolve what a file is by structure, not filename

Owners rename files; headers are stable. Maintain a per-table canonical column list (the toolkit's single source of truth) and match input headers against it.

• Configurable mapping first (table_map): operator-authored table_id → { sheet|file => name }, stored in the toolkit's settings option. Highest trust.
• Structural header matcher second: confidence = matched / required, where required is the idempotency + natural keys, not every optional column (so sparse owner files still map). Report matched, missing, required.
• Legacy name/slug guess last: the old hard-coded filename map, demoted to a low-priority fallback so pre-existing fixtures still resolve with zero config.

The slug-first regression (hard-won)

Deterministic filename/slug resolution must always win ahead of structural inference. In the F&B build, running the header matcher before the slug match let ingredients.csv (which also carries Supplier ID, Supplier columns) shadow suppliers.csv, silently corrupting any read that depended on supplier lead_time/order_cutoff (the PR cutoff gate). Always:

1. table_map hint,
2. deterministic slug/filename match,
3. structural header match — last resort only,
4. actionable error (no source / wrong shape / unrecognised columns).

2. Multi-format loader → one matrix contract

Normalise every format to the shared { columns, rows } matrix the rest of the toolkit already consumes.

• CSV/TSV: sniff delimiter (, \t ; |), strip UTF-8/UTF-16 BOM, convert to UTF-8, detect header row; skip fully-empty rows.
• JSON / JSONL: infer headers from object keys (union in first-seen order); JSONL parses line-by-line.
• XLSX/XLS/ODS: \PhpOffice\PhpSpreadsheet\IOFactory::createReaderForFile (setReadDataOnly, toArray), then route through the same matrix normalisation. PhpSpreadsheet is already a Pro-addon dep — no new dependency.
• PDF (text layer): \Smalot\PdfParser\Parser (also already a Pro dep, the extract_pdf_text fallback). Scanned/image-only PDFs (empty text layer) return a clear pointer to the OCR tools.
• Photo / scanned images: return an actionable "use analyze_image / extract_image_text / extract_pdf_text with enable_ocr / ocr_pdf_text" error — do not silently fail or fabricate text.

Guard every dependency with class_exists(...) + a clean WP_Error fallback so a missing autoload degrades gracefully instead of fatals.

Universal ingest: expose a scan_sources() that walks the source folder(s) and auto-detects each file's table by header structure (calling best_table), returning {file, table_id, confidence, format} sorted by confidence. This is schema-on-read: no operator mapping or explicit table choice needed; files that don't parse or don't resolve above threshold are omitted, never guessed.

Media Worker offload (Proposal 079): when a Media Worker sidecar is configured, load_file() parses the heavy formats in the worker first (POST /api/ingest/parse - ExcelJS for XLSX, pdf-parse for PDFs with a render→OCR chain for scans, tesseract for photos), returning the same {columns, rows} matrix contract; PDF/photo results arrive as extracted text and convert through the same delimited path. Every worker failure falls back to the local PHP parsers unchanged, so the cascade is invisible when the sidecar is absent. Text formats (CSV/TSV/JSON/JSONL) are never offloaded. Gates: the use_media_worker setting (auto default), the wp_mcp_ai_fnb_worker_enabled filter, and the wp_mcp_ai_fnb_worker_client filter (client-replacement test seam). An opt-in ocr_vision_fallback setting adds a provider-vision rung for photos (temporary attachment + extract_image_text, then deleted; failures keep the actionable pointer with the vision error in data; tests inject via the wp_mcp_ai_fnb_vision_ocr_result filter). Related Appendix B wiring: WP_MCP_AI_OCR_Service::extract_text_from_pdf() routes auto/tesseract scans through the worker ingest chain, and optimise_images_batch routes its non-dry-run batch through /api/image/batch.

3. Observation + terminology map (the vitals/FHIR analogue)

For semi-structured, measurement-shaped owner data, accept a loose observation row instead of a bespoke wide-column layout:

{ "type": "waste", "item": "tomatoes", "qty": 2.5, "unit": "kg",
  "recorded_at": "2026-10-15", "source": "supplier_csv", "raw_value": "2.5 kg" }

• Terminology map: type (measurement_type) → table_id, normalised (lowercase, _/-→space). This is the F&B analogue of the Health Records vitals LOINC_MAP.
• Alias binding: map loose keys onto canonical columns (qty → qty_sold/qty_delivered, recorded_at → date/delivery_date/ count_date, item ↔ ingredient, party_size → guests, etc.).
• Always preserve the raw form: store raw_value/_raw_observation beside the normalised columns (the vitals raw_value/value split) so a bad unit/price transform is reversible and auditable.
• Unknown/missing type → a per-row WP_Error, accumulating in map_records so one bad observation never sinks the batch.

4. Dedupe, retention and out-of-band banding

Port from the Health Records vitals CCT (is_near_duplicate, run_retention_sweep, the assess_*/generate_alerts banding):

• Dedupe keyed on idempotency columns with a small numeric tolerance; empty-on-existing-but-present-incoming is not a duplicate (re-import still fills gaps).
• Retention as a daily bounded-batch cron, gated on native storage being on and a retention_days setting (0 = keep forever). For CPT-backed storage prune posts via wp_delete_post; for custom tables use bounded DELETE ... LIMIT.
• Banding — domain thresholds (e.g. negative qty → high, zero price with qty → medium) surfaced in the import dry-run preview, never enforced as write blockers.

5. Import integration + diagnostics

• Route every value through the canonical column list; never trust model math.
• Keep dry-run first; the write step is ACT-tier (manage_options) and default-off.
• Return a mapping confidence report + flags in the dry-run result, and a three-way source error: no source configured / source found but table not in it / source found but columns unrecognised — never a bare "file not found".

Complete-by-default, resumable imports

A row cap in the import bridge silently turns into missing data downstream. In the F&B build fnb_import_table defaulted to limit = 500 (hard cap 5,000) while the Drive workbook held 15,405 daily-sales rows: the import returned success: true, the native CPT held only January, and every September metric returned 0. The source was intact; the copy was truncated.

• Never make a row cap the default. limit = 0 (or omitted) must mean "every remaining row". Apply the same ceiling the reader uses (F&B: WP_MCP_AI_Fnb_Cpt_Reader::MAX_ROWS = 25000) so an import can never write more rows than a subsequent read can see.
• Expose resumability. Accept offset; return source_total, offset, next_offset, has_more, complete, and (after a write) native_count. A partial import must be visible in the response, never implicit.
• Loop batches in the admin surface. The F&B Ingest & Preview dashboard sends 500 rows per AJAX request and recurses on next_offset until complete, showing x of y source rows processed. A single all-rows request can exceed PHP/AJAX time limits on a 15k-row workbook.
• Invalidate the read cache after a write. The table reader caches for minutes (F&B: fnb_table_<md5(table_id[:cpt])> transients). Delete both the native (:cpt) and source cache keys after a successful import, otherwise a verification read sees the stale partial table.
• Verify the tail, not just the head. After import assert native_count === source_total and query a row/period from the newest month; an aggregate-only check can pass on a truncated table.

6. Read-only ingest-review dashboard

Mirror the Medical Vitals dashboard anatomy for the review surface:

• add_submenu_page() under the toolkit parent at edit_posts (review), while the import write stays manage_options — keep the split.
• Bundled chart.min.js + inline JS/CSS + wp_localize_script for ajaxUrl/nonce/strings; no build step, no framework.
• AJAX endpoint calls the same import tool's dry-run path (not a parallel query) so the dashboard shows exactly what the assistant sees (preserves the cross-assistant figure rule).
• Research-grounded mapping-preview UX: auto-map high-confidence, highlight low/unmatched columns, show sample values, one-click remap, imported-vs- skipped-vs-errored counts, and never label a file "corrupt" without a fix path.
• Add a source-folder scan control ("Scan source folder") wired to scan_sources() so universal ingest works without a pre-selected table. It lists detected files + table + confidence for one-click review.
• Enqueue hook gotcha (silent "button does nothing" bug): a submenu under a top-level menu page gets a sanitization-dependent hook whose exact string ("{parent}_page_{slug}" with the parent title slugified) is fragile. Match the page by substring — if ( false === strpos( $hook, self::PAGE_SLUG ) ) { return; } — the same pattern NV_oOS_Graphify_Settings / WebLLM use. An exact === here silently skips enqueueing and the page's JS never runs.
• Wrap inline JS in jQuery(document).ready (or a DOMContentLoaded/readyState fallback) and guard typeof Chart !== "undefined" + the button's existence so a missing Chart.js or an early execution never leaves the UI inert.

Also load

• mcp-ai-wpoos-toolkit-creation — build/register the toolkit (§7a has the ingestion pitfalls summary)
• mcp-ai-wpoos-toolkit-audit — the four failure classes to scan before a PR
• mcp-ai-wpoos-test-suite — Docker cross-worktree test environment + patterns
• wp-plugin-architecture — one-class-per-file, composition-root discipline
• Proposal: docs/project/proposals/078-fnb-data-ingestion-open-format-proposal.md