本文へ移動
cccskills
無料GitHub で公開

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.

インストール方法を見る

含まれるファイル(1)

  • SKILL.md7.4 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Podcast Generation Skill

Overview

This skill generates high-quality podcast audio from text content. The workflow includes creating a structured JSON script (conversational dialogue) and executing audio generation through text-to-speech synthesis.

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 (based on content)
  • Output location: Where to save the generated podcast
  • You don't need to check the folder under /mnt/user-data

Step 2: Create Structured Script JSON

Generate a structured JSON script file in /mnt/user-data/workspace/ with naming pattern: {descriptive-name}-script.json

The JSON structure:

{
  "locale": "en",
  "lines": [
    {"speaker": "male", "paragraph": "dialogue text"},
    {"speaker": "female", "paragraph": "dialogue text"}
  ]
}

Step 3: Execute Generation

Call the Python script:

python /mnt/skills/public/podcast-generation/scripts/generate.py \
  --script-file /mnt/user-data/workspace/script-file.json \
  --output-file /mnt/user-data/outputs/generated-podcast.mp3 \
  --transcript-file /mnt/user-data/outputs/generated-podcast-transcript.md

Parameters:

  • --script-file: Absolute path to JSON script file (required)
  • --output-file: Absolute path to output MP3 file (required)
  • --transcript-file: Absolute path to output transcript markdown file (optional, but recommended)

[!IMPORTANT]

  • Execute the script in one complete call. Do NOT split the workflow into separate steps.
  • The script handles all TTS API calls and audio generation internally.
  • Do NOT read the Python file, just call it with the parameters.
  • Always include --transcript-file to generate a readable transcript for the user.

Script JSON Format

The script JSON file must follow this structure:

{
  "title": "The History of Artificial Intelligence",
  "locale": "en",
  "lines": [
    {"speaker": "male", "paragraph": "Hello Deer! Welcome back to another episode."},
    {"speaker": "female", "paragraph": "Hey everyone! Today we have an exciting topic to discuss."},
    {"speaker": "male", "paragraph": "That's right! We're going to talk about..."}
  ]
}

Fields:

  • title: Title of the podcast episode (optional, used as heading in transcript)
  • locale: Language code - "en" for English or "zh" for Chinese
  • lines: Array of dialogue lines
    • speaker: Either "male" or "female"
    • paragraph: The dialogue text for this speaker

Script Writing Guidelines

When creating the script JSON, follow these guidelines:

Format Requirements

  • Only two hosts: male and female, alternating naturally
  • Target runtime: approximately 10 minutes of dialogue (around 40-60 lines)
  • Start with the male host saying a greeting that includes "Hello Deer"

Tone & Style

  • Natural, conversational dialogue - like two friends chatting
  • Use casual expressions and conversational transitions
  • Avoid overly formal language or academic tone
  • Include reactions, follow-up questions, and natural interjections

Content Guidelines

  • Frequent back-and-forth between hosts
  • Keep sentences short and easy to follow when spoken
  • Plain text only - no markdown formatting in the output
  • Translate technical concepts into accessible language
  • No mathematical formulas, code, or complex notation
  • Make content engaging and accessible for audio-only listeners
  • Exclude meta information like dates, author names, or document structure

Podcast Generation Example

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

Step 1: Create script file /mnt/user-data/workspace/ai-history-script.json:

{
  "title": "The History of Artificial Intelligence",
  "locale": "en",
  "lines": [
    {"speaker": "male", "paragraph": "Hello Deer! Welcome back to another fascinating episode. Today we're diving into something that's literally shaping our future - the history of artificial intelligence."},
    {"speaker": "female", "paragraph": "Oh, I love this topic! You know, AI feels so modern, but it actually has roots going back over seventy years."},
    {"speaker": "male", "paragraph": "Exactly! It all started back in the 1950s. The term artificial intelligence was actually coined by John McCarthy in 1956 at a famous conference at Dartmouth."},
    {"speaker": "female", "paragraph": "Wait, so they were already thinking about machines that could think back then? That's incredible!"},
    {"speaker": "male", "paragraph": "Right? The early pioneers were so optimistic. They thought we'd have human-level AI within a generation."},
    {"speaker": "female", "paragraph": "But things didn't quite work out that way, did they?"},
    {"speaker": "male", "paragraph": "No, not at all. The 1970s brought what's called the first AI winter..."}
  ]
}

Step 2: Execute generation:

python /mnt/skills/public/podcast-generation/scripts/generate.py \
  --script-file /mnt/user-data/workspace/ai-history-script.json \
  --output-file /mnt/user-data/outputs/ai-history-podcast.mp3 \
  --transcript-file /mnt/user-data/outputs/ai-history-transcript.md

This will generate:

  • ai-history-podcast.mp3: The audio podcast file
  • ai-history-transcript.md: A readable markdown transcript of the podcast

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 and transcripts are saved in /mnt/user-data/outputs/
  • Share both the podcast MP3 and transcript MD 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:

  • 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
  • The script JSON should match the content language (en or zh)
  • Technical content should be simplified for audio accessibility in the script
  • Complex notations (formulas, code) should be translated to plain language in the script
  • Long content may result in longer podcasts

🔗 Связи

  • [[000 - Map of Maps]] — REPO_DOCS
  • [[000 - Map of Maps]] — Map of Maps

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

1password

無料

Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.

日本語の概要は準備中です。原文の説明を表示しています。

ibragimov-oasis/vibe-coder22026年6月24日 更新

Use this skill when the user requests to review, analyze, critique, or summarize academic papers, research articles, preprints, or scientific publications. Supports comprehensive structured reviews covering methodology assessment, contribution evaluation, literature positioning, and constructive feedback generation. Trigger on queries involving paper URLs, uploaded PDFs, arXiv links, or requests like "review this paper", "analyze this research", "summarize this study", or "write a peer review".

日本語の概要は準備中です。原文の説明を表示しています。

ibragimov-oasis/vibe-coder22026年6月24日 更新

Add descriptions for new models from the HuggingFace router to chat-ui configuration. Use when new models are released on the router and need descriptions added to prod.yaml and dev.yaml. Triggers on requests like "add new model descriptions", "update models from router", "sync models", or when explicitly invoking /add-model-descriptions.

日本語の概要は準備中です。原文の説明を表示しています。

ibragimov-oasis/vibe-coder22026年6月24日 更新

Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.

日本語の概要は準備中です。原文の説明を表示しています。

ibragimov-oasis/vibe-coder22026年6月24日 更新

Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.

日本語の概要は準備中です。原文の説明を表示しています。

ibragimov-oasis/vibe-coder22026年6月24日 更新

Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.

日本語の概要は準備中です。原文の説明を表示しています。

ibragimov-oasis/vibe-coder22026年6月24日 更新

ibragimov-oasis のスキルをすべて見る

このスキルの問題を報告する