deer-flow/skills/public/podcast-generation/SKILL.md

name, description

name	description
podcast-generation	Use this skill when the user requests to generate, create, or produce podcasts from text content. Converts written content into a two-host conversational podcast audio format with natural dialogue.

Podcast Generation Skill

Overview

This skill generates high-quality podcast audio from text content using a multi-stage pipeline. The workflow includes script generation (converting input to conversational dialogue), text-to-speech synthesis, and audio mixing to produce the final podcast.

Core Capabilities

• Convert any text content (articles, reports, documentation) into podcast scripts
• Generate natural two-host conversational dialogue (male and female hosts)
• Synthesize speech audio using text-to-speech
• Mix audio chunks into a final podcast MP3 file
• Support both English and Chinese content

Workflow

Step 1: Understand Requirements

When a user requests podcast generation, identify:

• Source content: The text/article/report to convert into a podcast
• Language: English or Chinese (auto-detected from content)
• Output location: Where to save the generated podcast
• You don't need to check the folder under /mnt/user-data

Step 2: Prepare Input Content

The input content should be plain text or markdown. Save it to a text file in /mnt/user-data/workspace/ with naming pattern: {descriptive-name}-content.md

Step 3: Execute Generation

Call the Python script directly without any concerns about timeout or the need for pre-testing:

python /mnt/skills/public/podcast-generation/scripts/generate.py \
  --input-file /mnt/user-data/workspace/content-file.md \
  --output-file /mnt/user-data/outputs/generated-podcast.mp3 \
  --locale en

Parameters:

• --input-file: Absolute path to input text/markdown file (required)
• --output-file: Absolute path to output MP3 file (required)
• --locale: Language locale - "en" for English or "zh" for Chinese (optional, auto-detected if not specified)

Important

• Execute the script in one complete call. Do NOT split the workflow into separate steps (e.g., testing script generation first, then TTS).
• The script handles all external API calls and audio generation internally with proper timeout management.
• Do NOT read the Python file, just call it with the parameters.

Podcast Generation Example

User request: "Generate a podcast about the history of artificial intelligence"

Step 1: Create content file /mnt/user-data/workspace/ai-history-content.md with the source text:

# The History of Artificial Intelligence

Artificial intelligence has a rich history spanning over seven decades...

## Early Beginnings (1950s)
The term "artificial intelligence" was coined by John McCarthy in 1956...

## The First AI Winter (1970s)
After initial enthusiasm, AI research faced significant setbacks...

## Modern Era (2010s-Present)
Deep learning revolutionized the field with breakthrough results...

Step 2: Execute generation:

python /mnt/skills/public/podcast-generation/scripts/generate.py \
  --input-file /mnt/user-data/workspace/ai-history-content.md \
  --output-file /mnt/user-data/outputs/ai-history-podcast.mp3 \
  --locale en

Specific Templates

Read the following template file only when matching the user request.

• Tech Explainer - For converting technical documentation and tutorials

Output Format

The generated podcast follows the "Hello Deer" format:

• Two hosts: one male, one female
• Natural conversational dialogue
• Starts with "Hello Deer" greeting
• Target duration: approximately 10 minutes
• Alternating speakers for engaging flow

Output Handling

After generation:

• Podcasts are saved in /mnt/user-data/outputs/
• Share generated podcast with user using present_files tool
• Provide brief description of the generation result (topic, duration, hosts)
• Offer to regenerate if adjustments needed

Requirements

The following environment variables must be set:

• OPENAI_API_KEY or equivalent LLM API key for script generation
• VOLCENGINE_TTS_APPID: Volcengine TTS application ID
• VOLCENGINE_TTS_ACCESS_TOKEN: Volcengine TTS access token
• VOLCENGINE_TTS_CLUSTER: Volcengine TTS cluster (optional, defaults to "volcano_tts")

Notes

• Always execute the full pipeline in one call - no need to test individual steps or worry about timeouts
• Input content language is auto-detected and matched in output
• The script generation uses LLM to create natural conversational dialogue
• Technical content is automatically simplified for audio accessibility
• Complex notations (formulas, code) are translated to plain language
• Long content may result in longer podcasts