Skip to main content
Back to the directory
bytedance/deer-flowSoftware EngineeringFrontend and Design

podcast-generation

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.

SkillJury keeps community verdicts, source metadata, and external repository signals in separate lanes so ranking data never pretends to be a review.

SkillJury verdict
Pending

No approved reviews yet

Would recommend
Pending

Waiting on enough review volume

Install signal
730

Weekly or total install activity from catalog data

Sign in to review
0 review requests
Install command
npx skills add https://github.com/bytedance/deer-flow --skill podcast-generation
SkillJury does not have enough approved reviews to publish a community verdict yet. Source metadata and repository proof are still available above.
SkillJury Signal Summary

As of Apr 30, 2026, podcast-generation has 730 weekly installs, 0 community reviews on SkillJury. Community votes currently stand at 0 upvotes and 0 downvotes. Source: bytedance/deer-flow. Canonical URL: https://skills.sh/bytedance/deer-flow/podcast-generation.

Security audits
Gen Agent Trust HubPASS
SocketFAIL
SnykPASS
About this skill
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: Generate a structured JSON script file in /mnt/user-data/workspace/ with naming pattern: {descriptive-name}-script.json The JSON structure: Call the Python script: Parameters: [!IMPORTANT] The script JSON file must follow this structure: Fields: When 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 : Step 2: Execute generation: This will generate: Read the following template file only when matching the user request. The generated podcast follows the "Hello Deer" format: After generation: The following environment variables must be set: - 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 - Source content: The text/article/report to convert into a podcast - Language: English or Chinese (based on content) - Output location:...

Source description provided by the upstream listing. Community review signal and install context stay separate from this narrative layer.

Community reviews

Latest reviews

No community reviews yet. Be the first to review.

Browse this skill in context
FAQ
What does podcast-generation do?

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.

Is podcast-generation good?

podcast-generation does not have approved reviews yet, so SkillJury cannot publish a community verdict.

Which AI agents support podcast-generation?

podcast-generation currently lists compatibility with Skills CLI.

Is podcast-generation safe to install?

podcast-generation has been scanned by security audit providers tracked on SkillJury. Check the security audits section on this page for detailed results from Socket.dev and Snyk.

What are alternatives to podcast-generation?

Skills in the same category include grimoire-morpho-blue, conversation-memory, second-brain-ingest, zai-tts.

How do I install podcast-generation?

Run the following command to install podcast-generation: npx skills add https://github.com/bytedance/deer-flow --skill podcast-generation

Related skills

More from bytedance/deer-flow

Related skills

Alternatives in Software Engineering