podcast-generation

A skill that turns written material into a conversational podcast with two hosts. It creates a dialogue script and converts it into spoken audio.

In plain words
What is it for?
Use it to turn text content into podcast episodes in English or Chinese. It can produce a final MP3 audio file.
Why use it?
It lets people listen to articles, reports, or documentation instead of reading them. The two-host format gives the source material a conversational structure.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/bytedance/deer-flow/podcast-generation
Any agent
npx skills add bytedance/deer-flow --skill podcast-generation
Clone the repo
git clone --depth 1 https://github.com/bytedance/deer-flow

Made for: Claude Code, Codex.

Per session 38 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,885 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00038 $0.01885
Opus 5 $0.00019 $0.00942
Sonnet 5 $0.00008 $0.00377
Haiku 4.5 $0.00004 $0.00188

Measured yesterday against content hash bd6929fb8047, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

podcast-generation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/generate.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

skills/public/podcast-generation/SKILL.md · 204 lines

How it starts

The opening of the file, as written. The whole thing — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.
  • The TTS provider and its concurrency are selected automatically from environment variables — you do not choose or tune them.

Read the full file on GitHub · 204 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 204 lines · 38 tokens per session scan A bd6929fb8047

Subscribe to this mod's changes

podcast-generation is a skill published in the GitHub repository bytedance/deer-flow (81,129 stars, last pushed yesterday), licensed MIT. It adds 38 tokens to every session and 1,885 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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