parse-integration
SolidExtract 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.
Install
Quality Score: 78/100
Skill Content
Details
- Author
- conversiontools
- Repository
- conversiontools/agent-skills
- Created
- 5 months ago
- Last Updated
- yesterday
- Language
- N/A
- License
- MIT
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
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 — certificates, confirmations, cover sheets) and TABLE extraction (list a document's tables, pick one, pull it). Intent-first; normalises via the shared engine (dates → DD MMM YYYY, currency → amount + code) and flags anything unfound or uncertain — never invents values. Extraction is computed locally (local OCR only). NOT for already-tabular data (use data-tidy) or deal-document intelligence like lease abstraction/model review (out of scope).
chat-complex-documents
Chat 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.
docparse
Parse documents (PDF, DOCX, PPTX, XLSX) with spatial bounding boxes via LiteParse. Answer questions with visual citations showing exact source locations on page images. Generate 31C-branded HTML/PDF reports. Maintains a parse cache for efficiency. Use when: "parse this document", "docparse", "visual citations", "show me where it says", "document analysis with citations", "parse with bounding boxes", "extract from this PDF". NOT for: plain text extraction without spatial data (use datastore-extract.py), email analysis (use /email-intel), web scraping (use /playwright or /firecrawl).