populate-xlsx-from-source

name	populate-xlsx-from-source
description	Extract tabular data from one or more source documents and populate a saved xlsx template — composes extract-tabular-data + xlsx-template-apply
model	deepseek/deepseek-v4-flash
tier	flash
inputs	[{"name":"source_document_ids","type":"list[str]","required":true,"description":"Prisma Document.id values to extract tables from (PDF/DOCX/PPTX/XLSX all supported)"},{"name":"template_id","type":"str","required":true,"description":"Saved xlsx template id (from /api/templates)"},{"name":"hint","type":"str","required":false,"description":"Optional context to focus extraction"},{"name":"title","type":"str","required":false,"description":"Optional title override for the rendered workbook"},{"name":"mapping","type":"dict","required":false,"description":"Explicit {\"template_table_name\": \"extracted_table_name\"} overrides; otherwise fuzzy-matched"}]
outputs	[{"name":"file_path","type":"str"},{"name":"file_url","type":"str"},{"name":"title","type":"str"},{"name":"template_id","type":"str"},{"name":"source_document_count","type":"int"},{"name":"extracted_table_count","type":"int"},{"name":"mapping_report","type":"list[dict]","description":"List of {template_table, filled_from, source_document_id, row_count, similarity} per template table"},{"name":"model","type":"str"}]

populate-xlsx-from-source

This skill implements the headline OfficePlane flow: ingest a document → fill an Excel template automatically.

Two-Step Composition

Step 1 — Extract (extract-tabular-data)

The handler calls extract-tabular-data once per source_document_id. Each call scans the ingested document's pages using a vision model and returns a list of tables:

{ "tables": [{"name": "...", "headers": [...], "rows": [[...]]}] }

All tables from all sources are pooled into a single candidate set.

Step 2 — Map + Apply (xlsx-template-apply)

The template is loaded from disk to discover its table sections (name + expected headers). Each template table is matched against the pool of extracted tables:

1. Explicit mapping (mapping input): if the caller provides {"Revenue": "Q3 Regional"}, the named extracted table is used directly.
2. Fuzzy header similarity (Jaccard on lowercased word-token sets): the extracted table whose header set is most similar to the template table's declared headers wins, provided the score is above a threshold (0.35). This handles minor label differences such as "Revenue ($)" vs "Revenue".

The matched tables are assembled into a {table_name: [[row]]} payload and forwarded to xlsx-template-apply, which renders the final .xlsx file.

Header Similarity

Jaccard similarity is computed on lowercased word tokens:

similarity = |tokens(A) ∩ tokens(B)| / |tokens(A) ∪ tokens(B)|

A threshold of 0.35 is used. Template tables with no match above the threshold appear in mapping_report with filled_from: null and row_count: 0.

Outputs

• file_path / file_url — location of the rendered .xlsx file
• mapping_report — list of {template_table, filled_from, source_document_id, row_count, similarity} entries, one per template table
• extracted_table_count — total tables extracted across all sources
• source_document_count — number of source documents processed