Interactive Literature Explainer

Purpose

Engage in multi-turn dialogue with AI to deeply understand literature content. Supports free-form Q&A grounded in the literature context, and automatically generates structured study notes after the conversation ends.

No need to worry about hallucination

AI responses must pass through a verification gate. Answers with uncertainty are explicitly flagged, so you can confidently discuss paper details with the AI.

Use Cases

• Encountering concepts or terminology you don't understand while reading a paper
• Wanting to dive deeper into a specific part of the paper (methods, experiments, derivations)
• Working with AI to trace the paper's reasoning and contributions

Input Constraints

Constraint Type	Description
Input Unit	Attachment
Accepted Types	text/markdown, text/x-markdown, text/plain, application/pdf
Per-parent limit	At most 1 attachment

Trigger Methods

• Directly select a PDF or Markdown attachment
• Select the parent item, and the plugin will automatically expand its first qualifying attachment

Execution Flow

1. Build Request
   └── Upload source file to Skill-Runner
       └── Invoke skill_id: "literature-explainer"

2. Skill-Runner Processing
   └── Launch interactive mode
       └── Open Dashboard chat panel

3. User Interaction
   └── Converse with AI in Task Dashboard
       └── Send messages, view replies

4. End Conversation
   └── User manually closes or cancels
       └── Generate conversation results

Interaction Flow

1. After the workflow starts, the Task Dashboard automatically opens the chat panel
2. Type questions or instructions in the chat input
3. AI replies are displayed in real-time in the panel
4. The conversation can continue until the user chooses to end it
5. Closing the panel triggers result processing

Estimated Duration

Depends on the number of conversation turns. Literature loading and initialization takes approximately 1-2 minutes, after which the conversation proceeds in real-time.

Model Recommendation

🟡 Models with web search capability are recommended. Literature Explainer has a built-in evidence verification mechanism — if the model can search the web to verify citations and facts in the paper, verification quality improves significantly. When web access is unavailable, the verification feature is severely limited, but reasoning and Q&A based on literature content is still possible.

Outputs

After execution completes, 1 Study Note (Conversation Note) is created under the parent item:

• Type: data-zs-note-kind="conversation"
• Content: Q&A history (HTML format), which can be kept as study notes
• Update strategy: Each execution creates a new conversation note (rather than overwriting)

Parameters

Parameter	Type	Description	Default
language	string	Conversation language	zh-CN

Available values: zh-CN, en-US, ja-JP, ko-KR, de-DE, fr-FR, es-ES, ru-RU. Custom input is also supported.

Dependencies

• Backend: Skill-Runner service
• Backend Configuration: Configure a Skill-Runner type backend in Backend Manager
• Skill: The literature-explainer skill must be deployed on the Skill-Runner

Related Workflows

• Literature Analysis — Automatically generate literature digests (recommended to run first)
• Deep Reading — Generate a structured deep reading view