chat-complex-documents
FeaturedChat with and search your complex documents — ask questions, extract tables and fields, and get answers grounded in the source. Connects the hosted Unstructured Transform MCP server to parse, structure, and enrich PDFs, Word/Excel/PowerPoint, images, scanned files, emails, and 60+ other formats into clean, AI-ready text for search, Q&A, and summarization without building a custom parsing pipeline.
Install
Quality Score: 92/100
Skill Content
Details
- Author
- vellum-ai
- Repository
- vellum-ai/vellum-assistant
- Created
- 7 months ago
- Last Updated
- today
- Language
- TypeScript
- License
- MIT
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
document-chunker
Use this skill when the task involves processing large files or document sets with Codex as the AI coworker: document ingestion, file inventory, parsing, chunking, structured extraction, embeddings preparation, retrieval QA, SEC/Form D processing, HypeStake knowledge ingestion, or auditing .chunks.jsonl outputs. All AI interpretation must be performed by Codex, not by bundled scripts.
data-extract
Get STRUCTURED data OUT of documents — PDFs (incl. multi-table/scanned via local OCR), Word, PowerPoint (.pptx), and Outlook .msg — into a clean .xlsx plus an audit report. Use when the user wants to "extract data from this PDF/document", "pull the table out of this report", "get the figures from these statements/certificates", "turn these confirmations into a table", "read the fields off this form", or "extract these line items". Two modes: key-value/FORM extraction (label → value, one record per document) and TABLE extraction (list a document's tables, pick one, pull it). NOT for already-tabular data (use data-tidy) or deal-document intelligence like lease abstraction/model review (out of scope).
parse-integration
Extract structured data from documents (invoices, receipts, purchase orders, bills of lading, bank statements, contracts, forms, ID documents, scans, photos of paper) with Parse by Conversion Tools, and build a production integration against its API. Covers getting an API key, defining an extraction schema, submitting a document, polling until the extraction reaches a terminal state, handling failures, replacing polling with webhooks, and exporting results to CSV or XLSX. Use when the user wants document data extraction, PDF-to-JSON with named fields, an OCR-and-structure pipeline, or asks how to integrate a document parsing API into their own code.