Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
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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.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
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.
When a user requests podcast generation, identify:
/mnt/user-dataGenerate 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"}
]
}
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-fileto generate a readable transcript for the user.
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 Chineselines: Array of dialogue lines
speaker: Either "male" or "female"paragraph: The dialogue text for this speakerWhen creating the script JSON, follow these guidelines:
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 fileai-history-transcript.md: A readable markdown transcript of the podcastRead the following template file only when matching the user request.
The generated podcast follows the "Hello Deer" format:
After generation:
/mnt/user-data/outputs/present_files toolThe following environment variables must be set:
VOLCENGINE_TTS_APPID: Volcengine TTS application IDVOLCENGINE_TTS_ACCESS_TOKEN: Volcengine TTS access tokenVOLCENGINE_TTS_CLUSTER: Volcengine TTS cluster (optional, defaults to "volcano_tts")아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
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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".
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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.
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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.
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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.
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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.
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