deepcode

name	deepcode
description	DeepCode integration - automated code generation from papers and text requirements. Trigger phrases: "deepcode integration - automated code generation from papers and text requirements".

DeepCode - AI Code Generation Engine

You have access to DeepCode, a powerful multi-agent AI code generation engine that can:

• Paper2Code: Reproduce research paper algorithms as working code
• Chat2Code: Generate complete projects from text descriptions

Available Tools

Tool	Purpose
deepcode_paper2code	Submit a paper URL or file for code reproduction
deepcode_chat2code	Submit text requirements for code generation
deepcode_status	Check task progress and results
deepcode_list_tasks	List active and recent tasks
deepcode_cancel	Cancel a running task
deepcode_respond	Respond to User-in-Loop interactions

When to Use DeepCode

Automatically trigger deepcode_paper2code when user:

• Sends an arxiv URL (e.g. https://arxiv.org/abs/... or https://arxiv.org/pdf/...)
• Sends a paper URL from other academic sites
• Asks to "reproduce", "implement", or "replicate" a paper
• Sends a PDF file and asks for code generation
• Says something like "帮我复现这篇论文" or "把这篇论文的代码跑出来"

Automatically trigger deepcode_chat2code when user:

• Describes a coding project they want to build
• Asks to create a web app, backend service, algorithm implementation, etc.
• Provides detailed requirements for a software project
• Says something like "帮我写一个..." or "生成一个项目..."

Workflow Guidelines

1. Submitting a Task

When the user wants to generate code:

1. Identify if it's a paper (use deepcode_paper2code) or requirements (use deepcode_chat2code)
2. Submit the task and note the task_id
3. Tell the user the task has been submitted and the estimated wait time (10-60 minutes for papers, 5-30 minutes for chat)
4. Offer to check progress periodically

2. Monitoring Progress

• When user asks about progress, use deepcode_status with the task_id
• Report the progress percentage and current phase
• If the task is complete, share the result summary

3. Handling User-in-Loop Interactions

• Check deepcode_status - if status is "waiting_for_input", there's a pending interaction
• Read the interaction details (questions, plan review, etc.)
• Present the questions/plan to the user in a natural conversational way
• Collect the user's response
• Use deepcode_respond to submit the response back to DeepCode

4. Delivering Results

When a task completes:

• Report the generated file structure
• Mention key files (e.g. model.py, train.py, requirements.txt)
• The generated code is in the shared deepcode_lab/ directory
• Offer to read specific files if the user wants to review them

Response Style

• Be concise and informative about task status
• Use progress percentages to show advancement
• When a task completes, provide a brief summary of what was generated
• For Chinese-speaking users, respond in Chinese (follow the user's language)

Important Notes

• Code generation tasks run in the background and take time (10-60 minutes)
• Do NOT spawn subagents for DeepCode tasks - use the tools directly
• If DeepCode backend is unreachable, inform the user that the service may not be running
• Generated code is stored in /app/deepcode_lab/papers/ directory