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cumtyc

chatdoc-studio-api

by cumtyc · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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Install in OpenClaw
/install chatdoc-studio-api
Description
ChatDOC Studio API usage guide - complete documentation and examples for PDF parsing, chat applications, agent applications, content retrieval, and data extr...
README (SKILL.md)

Overview

ChatDOC Studio is an AI-powered document processing and conversation platform providing multiple API capabilities:

  • PDF Parser - Parse PDF documents into structured data (JSON, Markdown, Excel)
  • Chat App - Create document-based Q&A chat applications
  • Agent App - Run task-based document analysis with published Agent Apps
  • RAG App - Content retrieval applications based on documents
  • Extract App - Extract structured data from documents

API Basics

Base URL

https://api.chatdoc.studio/v1

Authentication

All API requests require a JWT Token in the HTTP Header:

Authorization: Bearer YOUR_API_KEY

Environment Variables

Manage API configuration through environment variables:

Environment Variable Description Default Value
CHATDOC_STUDIO_BASE_URL API Base URL https://api.chatdoc.studio/v1
CHATDOC_STUDIO_API_KEY API authentication key -

Supported File Types

API PDF DOC DOCX MD TXT
PDF Parser
Chat App
Agent App
RAG App
Extract App

API Module Documentation

Uploads API

Required for all apps except PDF Parser. Upload documents to your team before using them in Chat Apps, Agent Apps, RAG Apps, or Extract Apps.

Documentation: uploads/uploads_api.md Code Examples: uploads/uploads_api_examples.md

PDF Parser API

Parse PDF documents into structured data, supporting JSON, Markdown, and Excel exports.

Documentation: parsers/pdf_parser.md Code Examples: parsers/pdf_parser_examples.md

Chat App API

Create document-based Q&A chat applications with multi-turn conversations and source tracing.

Documentation: chat/chat_app.md Code Examples: chat/chat_app_examples.md

Agent App API

Submit uploaded files to published Agent Apps, poll task status, and fetch final task results.

Documentation: agent/agent_app.md Code Examples: agent/agent_app_examples.md

RAG App API

Perform semantic retrieval based on document content to retrieve relevant document fragments.

Documentation: retrieval/rag_app.md Code Examples: retrieval/rag_app_examples.md

Extract App API

Extract structured data from documents based on JSON Schema definitions.

Documentation: extraction/extract_app.md Code Examples: extraction/extract_app_examples.md

Apps API

Manage all types of applications (Chat, Agent, Extract, RAG) in your team - list and delete apps.

Documentation: apps/apps.md Code Examples: apps/apps_examples.md

Document Status (DocumentStatus)

All uploaded documents go through a processing status flow. Understanding document status is crucial for proper API usage.

Documentation: docs/document_status.md

Common Response Format

All API responses follow a unified format:

Success Response:

{
  "type": "System",
  "code": "success",
  "data": { ... },
  "detail": null
}

Error Response:

{
  "type": "...",
  "code": "...",
  "data": ...,
  "detail": "...."
}

Common Error Codes

In addition to API-specific error codes, the following error codes may be returned by any API endpoint:

Plan Error Codes (PlanErrorEnum)

These errors are related to your subscription plan's credit and capacity limits. Your API usage consumes credits and counts against your plan's capacity.

Error Code Description
credit_not_enough Insufficient credits to perform the operation. Top up your credits or upgrade your plan.
capacity_not_enough Storage capacity exceeded. Delete unused documents or upgrade your plan.
app_count_not_enough Maximum number of apps allowed by your plan has been reached.
member_count_not_enough Maximum number of team members allowed by your plan has been reached.
upgrade_plan_error Error occurred during plan upgrade process.
not_found Plan not found. Contact support.

System Error Codes (SystemErrorEnum)

These are general system-level errors that may occur during API operations.

Error Code Description
unknown_error An unexpected error occurred. Try again or contact support if it persists.
validation_error Request validation failed. Check your request parameters.
project_expired The project or subscription has expired. Renew your subscription to continue.
handshake_error Authentication handshake failed. Check your API key.

Rate Limiting

API calls are subject to rate limits based on your subscription plan. HTTP 429 status code will be returned when limits are exceeded.

Getting Started

Basic Workflow

  1. Obtain an API Key from the ChatDOC Studio console
  2. Configure environment variables
  3. Review the module's documentation and examples
  4. Upload documents (for Chat/Agent/RAG/Extract Apps) using the Uploads API
  5. Immediately create your app or task using the upload IDs (processing is auto-triggered when referenced)
  6. Wait for the app or task to become ready before using downstream features
  7. Integrate into your application

Quick Start Examples

PDF Parser: Upload and parse → Get JSON/Markdown/Excel

Chat App: Upload documents → Create Chat App → Send messages

Agent App: Upload document → Create Agent task → Poll status → Get final result

RAG App: Upload documents → Create RAG App → Query content

Extract App: Create Extract App with schema → Upload document → Get extracted data

Additional Resources

Usage Guidance
This skill is primarily documentation and code samples for the ChatDOC Studio API (PDF parsing, chat, extraction, RAG). That purpose matches the files, but the package metadata did not declare the API key (CHATDOC_STUDIO_API_KEY) that the docs repeatedly require — treat that as a transparency/information mismatch. Before using the skill: 1) Confirm the API base URL (https://api.chatdoc.studio/v1) is the official endpoint you expect. 2) Only provide a minimal, least-privileged API key; avoid using high-privilege or long-lived credentials. 3) Do not upload sensitive documents (PII, secrets, proprietary files) until you verify the service's privacy/retention policy. 4) Inspect the omitted files (e.g., agents/openai.yaml) referenced in the package to understand any surprises or external integrations. 5) If you need strict guarantees, obtain official SDK/docs from the provider and compare them to these files. The skill is not evidently malicious, but the metadata/README mismatch and potential for sending sensitive data to a remote API justify caution.
Capability Analysis
Type: OpenClaw Skill Name: chatdoc-studio-api Version: 1.0.0 The bundle provides comprehensive documentation and multi-language code examples (Python, TypeScript, Rust, and cURL) for the ChatDOC Studio API, covering document parsing, chat, RAG, and data extraction services. All code examples follow standard practices for REST API integration, utilizing environment variables for authentication and targeting a legitimate service domain (api.chatdoc.studio). No indicators of malicious intent, data exfiltration, unauthorized execution, or prompt injection were found.
Capability Tags
cryptocan-make-purchases
Capability Assessment
Purpose & Capability
The files are comprehensive API documentation and code samples for a document-processing/chat/RAG service (PDF parsing, uploads, chat/agent/extract apps). That matches the skill name/description and the examples are coherent with that purpose.
Instruction Scope
The runtime instructions and examples require making authenticated HTTP calls and uploading files (open local files for upload). The SKILL.md and examples instruct use of environment variables (CHATDOC_STUDIO_API_KEY, CHATDOC_STUDIO_BASE_URL) and reading local files for upload — expected for this API — but the skill metadata did not declare these required env vars. No instructions request broad system reads or unexpected remote endpoints beyond https://api.chatdoc.studio/v1, and there is no guidance to exfiltrate unrelated data.
Install Mechanism
This is an instruction-only skill with no install spec and no code to write to disk. That is the lowest-risk install mechanism.
Credentials
The documentation clearly expects an API key in CHATDOC_STUDIO_API_KEY (and optionally CHATDOC_STUDIO_BASE_URL), but the skill metadata declares no required environment variables or primary credential. This is an inconsistency: if you plan to use the skill you will need to provide an API key, but the package does not surface that requirement. Requesting an API key for the documented service would be proportionate; failing to declare it is a transparency issue. Also note uploads imply you may transmit sensitive documents to the remote service — that the skill will do if you follow examples.
Persistence & Privilege
The skill is not always-enabled and does not request persistent privileges. It is user-invocable and allows autonomous model invocation by default (normal). There is no indication it modifies other skills or system configurations.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install chatdoc-studio-api
  3. After installation, invoke the skill by name or use /chatdoc-studio-api
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release of ChatDOC Studio API usage guide. - Complete documentation for API modules: PDF parsing, chat, agent, RAG, and extract apps. - Includes authentication, environment variable setup, and supported file type guides. - Outlines workflow for uploading, processing, app creation, and integration. - Details error codes, rate limiting, response formats, and document status. - Provides links to detailed docs and code examples for each API module.
Metadata
Slug chatdoc-studio-api
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is chatdoc-studio-api?

ChatDOC Studio API usage guide - complete documentation and examples for PDF parsing, chat applications, agent applications, content retrieval, and data extr... It is an AI Agent Skill for Claude Code / OpenClaw, with 79 downloads so far.

How do I install chatdoc-studio-api?

Run "/install chatdoc-studio-api" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is chatdoc-studio-api free?

Yes, chatdoc-studio-api is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does chatdoc-studio-api support?

chatdoc-studio-api is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created chatdoc-studio-api?

It is built and maintained by cumtyc (@cumtyc); the current version is v1.0.0.

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