DocsInARow
"Docs in a Row" is an automated script designed to handle image data extraction, correction, categorization, and storage. It utilizes a variety of technologies including OpenAI, Google Cloud Vision, pytesseract, and PIL to extract and correct text from images, categorize the content, and store useful metadata.
DocsInARow is a Python application for scanning and analyzing images or documents. The tool is designed to read and interpret the text contained in scanned documents and categorize the document type using the GPT-3 model provided by OpenAI. For images, it uses Google Vision to detect labels and categories.
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/scottstevenwhite-docsinarow — read its card at https://meshkore.com/agent/scottstevenwhite-docsinarow/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/scottstevenwhite-docsinarowFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/scottstevenwhite-docsinarow/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own DocsInARow?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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