Audit one local prospectus PDF, DOC, or DOCX through PDRouter and download the annotated document with AutoDoc NEXT2.0.
The English summary is being prepared. Showing the original description.
Create and operate ChatDOC Studio knowledge bases through pd_router using a Bearer API key and JavaScript helpers. Use when Codex needs to upload one or more PDF/DOC/DOCX files, skip failed files without aborting the whole job, create a knowledge base from successful uploads, or call the ChatDOC Studio knowledge-base skill endpoints through `https://platform.paodingai.com/openapi/chatdoc-studio/...`.
The English summary is being prepared. Showing the original description.
Review the instructions the agent will receive before you install.
Run a JavaScript workflow that uploads local PDF/DOC/DOCX files to ChatDOC Studio through PDRouter, waits for successful parsing, and creates a knowledge base from the successful uploads in one step.
This is suitable for creating knowledge bases from local files or folders, then using the returned library_id for document listing, retrieval, grep, syllabus reading, and block reading.
npx skills add PaodingAI/skills
For a local Codex installation, place this skill directory under:
$CODEX_HOME/skills/chatdoc-studio-knowledgemate
Create a knowledge base from files:
node scripts/knowledge-mate.mjs upload-and-create \
--name "Quarterly KB" \
--file ./docs/a.pdf \
--file ./docs/b.docx \
--file ./docs/c.pdf
Create a knowledge base from a folder recursively:
node scripts/knowledge-mate.mjs upload-and-create \
--name "Quarterly KB" \
--dir ./docs
Query an existing knowledge base:
node scripts/knowledge-mate.mjs retrieval --library-id <id> --query <text>
node scripts/knowledge-mate.mjs list-documents --library-id <id>
node scripts/knowledge-mate.mjs stats --library-id <id> --upload-id <id>
node scripts/knowledge-mate.mjs grep --library-id <id> --upload-id <id> --pattern <text>
node scripts/knowledge-mate.mjs read --library-id <id> --upload-id <id> --offset <n>
node scripts/knowledge-mate.mjs read-syllabus --library-id <id> --upload-id <id>
scripts/knowledge-mate.mjs directly. Do not reimplement the PDRouter or ChatDOC Studio API flow yourself during normal use./openapi/chatdoc-studio/....Authorization: Bearer <PAODINGAI_API_KEY>.chatdoc_studio directly.X-PD-* signature headers in this skill.upload-and-create for new knowledge bases.PAODINGAI_API_KEY: Required. The Bearer API key for PDRouter. If it is missing, the script fails immediately and tells the user to create a Bearer API Key in pdrouter, then export it before retrying.The PDRouter base URL is fixed in the script as https://platform.paodingai.com. The service code is fixed in the script as chatdoc-studio; users do not need to set base-url or service-code environment variables.
The API key can be obtained from the PDRouter platform.
5..pdf, .doc, and .docx.--dir is scanned recursively.--file and --dir can be mixed in one command.300 supported files are found, the script stops before uploading and asks the user to clean the folder to at most 300 PDF/DOC/DOCX files.PAODINGAI_API_KEY from the environment and use the fixed PDRouter base URL https://platform.paodingai.com.--file inputs and recursively scanned --dir inputs into one de-duplicated file list..pdf, .doc, and .docx files before uploading. If the count is greater than 300, fail immediately without uploading anything.POST /openapi/chatdoc-studio/knowledge-base/upload with concurrency 5.POST /openapi/chatdoc-studio/knowledge-base only with successful upload_ids.data payload. Failed requests print a clear error reason and include the backend response when available.upload-and-create returns a library_id, use the read/query commands for retrieval, listing documents, stats, grep, syllabus reading, or block reading.No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Audit one local prospectus PDF, DOC, or DOCX through PDRouter and download the annotated document with AutoDoc NEXT2.0.
The English summary is being prepared. Showing the original description.
Compare two local documents and convert differences into LLM-ready Markdown in one synchronous call. Supports PDF, Word, PPT, and image documents accepted by Calliper, and returns a structured markdown diff table suitable for change review, clause comparison, downstream extraction, and rule-based validation.
The English summary is being prepared. Showing the original description.
Convert unstructured documents into LLM-ready structured data. Supports PDF, Word, PPT, and images; extracts paragraphs, formulas, tables, charts, and other elements in one step; generates up to 8 levels of headings; and outputs Markdown organized in reading order. Useful for field extraction, comparison and validation, knowledge retrieval, and intelligent Q&A.
The English summary is being prepared. Showing the original description.