extract-tabular-data

name	extract-tabular-data
description	Walk an ingested document's pages and extract every detected table into a canonical {name, headers, rows} structure with source attributions
model	deepseek/deepseek-v4-flash
tier	flash
inputs	[{"name":"document_id","type":"str","required":true,"description":"Prisma Document.id (any format — PDF/DOCX/PPTX/XLSX)"},{"name":"max_tables","type":"int","required":false,"description":"Soft cap on number of tables to return (default 20)"},{"name":"hint","type":"str","required":false,"description":"Optional context to focus extraction — e.g. \"financial figures only\" or \"patient measurements\""}]
outputs	[{"name":"document_id","type":"str"},{"name":"title","type":"str"},{"name":"table_count","type":"int"},{"name":"tables","type":"list[dict]","description":"[{name, headers, rows, source_page, attribution: {document_id, section_id, page_numbers}}]"},{"name":"model","type":"str"}]

extract-tabular-data

Walks every page of any ingested OfficePlane document (PDF, DOCX, PPTX, XLSX) and extracts all detected tables into a canonical structured format ready for downstream structured consumers such as Excel population (Phase 37).

How it works

1. Fetches the document record from the database using document_id. Works with any format — not restricted to Excel.
2. Walks the chapter → section → page hierarchy produced by the ingestion pipeline, collecting up to 6 000 characters per page. Falls back to a flat page list for documents ingested with older pipelines.
3. Assembles all page content into a single prompt blob with page-number and section-title markers, then sends it to DeepSeek (OFFICEPLANE_AGENT_MODEL_FLASH, default deepseek/deepseek-v4-flash) with temperature=0 and response_format={"type": "json_object"} for deterministic output.
4. Normalises every table returned by the model:
   • Tables with no headers list or no rows list are silently dropped.
   • Each row is padded with null or truncated to match the header width.
   • Tables with no valid rows after normalisation are dropped.
   • The output list is hard-capped at max_tables.
5. Builds an attribution block for each table containing document_id, section_id, and page_numbers so downstream consumers can trace data back to its source page.
6. Writes a SkillInvocation audit row via persist_skill_invocation.

Extraction prompt rules

The model is instructed to:

• Not invent rows — only include data literally present in the source pages.
• Coerce numbers — strings like "1,200" become 1200; "12.5%" becomes 0.125.
• Normalise dates — date-like strings are kept as ISO-8601 ("YYYY-MM-DD") when the conversion is unambiguous.
• Infer headers — when a table has no explicit header row the model derives column names from surrounding context or assigns generic labels (Col1, Col2, …).
• Skip trivially small tables — tables with fewer than 2 data rows are omitted unless they are clearly a standalone summary block.
• Name each table — a short descriptive name (≤ 60 characters) that distils the table's purpose is required for every extracted table.
• Classify column types — each column is tagged as "text", "number", "date", "percent", or null when type cannot be determined.

The optional hint input narrows extraction focus, e.g. "financial figures only" or "patient measurements". The model is instructed to skip tables that do not match the hint.

Output shape

{
  "document_id": "cuid...",
  "title": "Q3 Sales Report",
  "table_count": 2,
  "tables": [
    {
      "name": "Regional Revenue by Quarter",
      "headers": ["Region", "Q1", "Q2", "Q3"],
      "rows": [
        ["North America", 1200000, 1350000, 1480000],
        ["Europe",         900000,  980000, 1050000]
      ],
      "source_page": 3,
      "row_count": 2,
      "column_types": ["text", "number", "number", "number"],
      "attribution": {
        "document_id": "cuid...",
        "section_id": "sec-uuid...",
        "page_numbers": [3]
      }
    }
  ],
  "model": "deepseek/deepseek-v4-flash"
}

If the document has no page content the skill returns early with table_count=0, tables=[], and a note field explaining the situation.