/install aiparse-ocr
\r \r
AI Parse\r
\r A skill for parsing PDF files using Large Language Models.\r \r
Capabilities\r
\r
- Extract information from PDF files\r
- Return results in JSON or Markdown format\r
- Resume processing from existing task ID\r
- Save task ID information to JSON file for reference\r \r
Parameters\r
\r
| Parameter | Type | Required | Description |\r
|-----------|------|----------|-------------|\r
| pdf_path | string | required | Path to the PDF file to process |\r
| result_path | string | required | Path to save the parsing result |\r
| format | string | required | Output format: "json" or "md" |\r
| task_id_path | string | required | Path to save task ID information (JSON format) |\r
| --task-id | string | optional | Existing task ID to resume processing |\r
\r
Usage Examples\r
\r
Normal Upload Mode\r
\r
python handler.py \x3Cpdf_path> \x3Cresult_path> \x3Cformat> \x3Ctask_id_path>\r
```\r
\r
### Resume from Existing Task or Check Status\r
\r
```bash\r
python handler.py --task-id \x3Ctask_id> \x3Cresult_path> \x3Cformat>\r
```\r
\r
## Task ID File Format\r
\r
When using normal upload mode, a task ID file will be created at `task_id_path` with the following JSON structure:\r
\r
```json\r
{\r
"task_id": "AAFXKO",\r
"pdf_path": "test.pdf",\r
"submit_time": "2026-04-04 00:33:27"\r
}\r
```\r
\r
This file can be used to:\r
- Track the submitted task\r
- Retrieve the task ID later for status checking\r
- Resume processing if interrupted\r
\r
## Implementation\r
\r
Implemented by `handler.py` which:\r
- Uploads PDF files to the processing service\r
- Polls for processing completion\r
- Downloads and saves results in the requested format\r
- Supports resuming from existing task IDs\r
- Saves task ID information to JSON file\r
\r
## Environment Requirements\r
\r
- Python 3.6+\r
- requests library\r
\r
## Return Value\r
\r
The parsed result will be saved to the specified `result_path` in the requested format:\r
- **JSON format:** Structured JSON with task details and extracted content\r
- **Markdown format:** Formatted Markdown with page-by-page content\r
\r
## Notes\r
\r
- For large PDF files, processing may take multiple minutes\r
- Free users can process 30 PDF pages - visit https://api.pinocch.com/index for extra trial credits\r
- The `--task-id` parameter can be used to resume processing if interrupted\r
- Check the console output for processing progress and status updates\r
- The task ID file is created immediately after successful upload\r
- **IMPORTANT FOR AGENTS:** Before declaring a task as failed, always use the task ID to check the current status of the task. Use the `--task-id` parameter to resume or verify the task status. The task may still be processing or have completed successfully.\r
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install aiparse-ocr - 安装完成后,直接呼叫该 Skill 的名称或使用
/aiparse-ocr触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
aiparse-ocr 是什么?
Parse PDF files using LLM. **No registration required - free trial available!** Extract information from PDF files and return results in JSON or Markdown for... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 170 次。
如何安装 aiparse-ocr?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install aiparse-ocr」即可一键安装,无需额外配置。
aiparse-ocr 是免费的吗?
是的,aiparse-ocr 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
aiparse-ocr 支持哪些平台?
aiparse-ocr 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 aiparse-ocr?
由 do0388309(@do0388309)开发并维护,当前版本 v1.0.2。