# Podcast Generation

> Se usa cuando el usuario pide generar, crear o producir podcasts a partir de contenido escrito. Convierte el texto en un podcast conversacional de dos presentadores con diálogo natural.

Fuente: https://skillsagentes.com/skills/bytedance/deer-flow/podcast-generation
Markdown: https://skillsagentes.com/skills/bytedance/deer-flow/podcast-generation.md

Repositorio: https://github.com/bytedance/deer-flow
Autor: bytedance
Licencia: MIT
Actualizado: hace 2 meses
Coste de contexto: 49 tok instalada, 2.1k tok al activarse, 6.2k tok con todos los archivos del bundle
Bundle: 3 archivos, 24 KB
Permisos que pide: ninguno declarado

## Instalación

Un skill son archivos markdown: los mismos archivos valen para cualquier agente y lo único que cambia es el directorio de destino, es decir la bandera `--agent`. Añade `-g` para instalarlo en todos los proyectos de la máquina.

```bash
# Claude Code
npx -y skills add bytedance/deer-flow --skill podcast-generation --agent claude-code
# Cursor
npx -y skills add bytedance/deer-flow --skill podcast-generation --agent cursor
# Codex
npx -y skills add bytedance/deer-flow --skill podcast-generation --agent codex
# Gemini CLI
npx -y skills add bytedance/deer-flow --skill podcast-generation --agent gemini
# Windsurf
npx -y skills add bytedance/deer-flow --skill podcast-generation --agent windsurf
# Cline
npx -y skills add bytedance/deer-flow --skill podcast-generation --agent cline
```

## Qué hace

- Convierte contenido de texto (artículos, informes, documentación) en un podcast de dos presentadores (hombre y mujer) con diálogo natural.
- Genera un guion JSON estructurado y lo sintetiza a audio MP3 mediante texto a voz.
- Sigue el formato 'Hello Deer': el presentador masculino saluda primero, duración objetivo de ~10 minutos, 40-60 líneas alternadas.
- Produce también una transcripción en Markdown legible junto al MP3.

## Cuándo usarla

- El usuario pide generar, crear o producir un podcast a partir de contenido escrito.

## Qué la activa

- "Convierte este artículo en un podcast de dos presentadores"
- "Genera un podcast sobre la historia de la inteligencia artificial"
- "Crea un episodio en audio a partir de esta documentación técnica"

## Antes de instalar

- Requiere credenciales de Volcengine TTS (VOLCENGINE_TTS_APPID/ACCESS_TOKEN) o MINIMAX_API_KEY.
- Necesita en el PATH: python
- Variables de entorno: MINIMAX_API_HOST, MINIMAX_API_KEY, MINIMAX_TTS_MAX_RETRIES, MINIMAX_TTS_MODEL, MINIMAX_TTS_VOICE_FEMALE, MINIMAX_TTS_VOICE_MALE, VOLCENGINE_TTS_ACCESS_TOKEN, VOLCENGINE_TTS_APPID, VOLCENGINE_TTS_CLUSTER
- makes network requests
- needs API credentials

## Archivos

- SKILL.md — 8 KB
- scripts/generate.py — 14 KB
- templates/tech-explainer.md — 2 KB

## SKILL.md

Reproducido tal cual desde bytedance/deer-flow bajo MIT. Esta sección es el documento original y está en inglés.

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

### Step 3: Execute Generation

Call the Python script:
```bash
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.

## Script JSON Format

The script JSON file must follow this structure:

```json
{
  "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`:
```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:
```bash
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](templates/tech-explainer.md) - 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:
- For Volcengine: `VOLCENGINE_TTS_APPID` and `VOLCENGINE_TTS_ACCESS_TOKEN`
- For MiniMax: `MINIMAX_API_KEY`
- `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

## Providers (Volcengine / MiniMax)

Auto-selected by environment variables:

- `VOLCENGINE_TTS_APPID` + `VOLCENGINE_TTS_ACCESS_TOKEN` set → Volcengine TTS (default).
- Only `MINIMAX_API_KEY` set → MiniMax TTS (`/v1/t2a_v2`).
- Force with `PODCAST_GENERATION_PROVIDER=volcengine|minimax`.

MiniMax overrides: `MINIMAX_API_HOST` (default `https://api.minimaxi.com`),
`MINIMAX_TTS_MODEL` (default `speech-2.6-hd`), `MINIMAX_TTS_VOICE_MALE`
(default `male-qn-qingse`), `MINIMAX_TTS_VOICE_FEMALE` (default `female-tianmei`).

Concurrency is owned by each provider internally — MiniMax runs single-threaded
to reduce rate-limit failures, Volcengine uses 4 workers. There is no
caller-facing concurrency knob; transient rate limits are handled by automatic
retry with backoff.

## Dónde encaja

- Categoría: [Redacción y contenido](https://skillsagentes.com/categorias/redaccion-contenido.md) — Redacción, edición y estructuración de contenido extenso.
- Creador: [bytedance](https://skillsagentes.com/creators/bytedance.md) — 27 skills en el directorio
- [Todas las skills](https://skillsagentes.com/skills.md)
- [Ranking de instalaciones](https://skillsagentes.com/ranking.md)

## Otras skills del mismo repositorio

- [Engineer System Change](https://skillsagentes.com/skills/bytedance/deer-flow/engineer-system-change.md): Evalúa y ejecuta cambios de sistema no triviales desde primeros principios: RFCs, features, refactors, migraciones o nuevas APIs, exigiendo consumidores concretos, la solución mínima suficiente y evidencia proporcional al riesgo.
- [Skill Reviewer](https://skillsagentes.com/skills/bytedance/deer-flow/skill-reviewer.md): Revisa paquetes de skills de DeerFlow: preparación para publicar, triggers, límites de seguridad, recursos y evidencia. Úsala cuando pidan auditar, calificar o validar para producción una skill.
- [Skill Creator](https://skillsagentes.com/skills/bytedance/deer-flow/skill-creator.md): Crea skills nuevas, modifica y mejora skills existentes, y mide su rendimiento. Úsala para crear una skill, editarla, correr evals, hacer benchmark con análisis de varianza, u optimizar su descripción.
- [Smoke Test](https://skillsagentes.com/skills/bytedance/deer-flow/smoke-test.md): Skill de smoke test de extremo a extremo para DeerFlow: actualiza el código, despliega en local o Docker, verifica disponibilidad de servicios, hace health check y genera el reporte final.
- [Deerflow Maintainer Orchestrator](https://skillsagentes.com/skills/bytedance/deer-flow/deerflow-maintainer-orchestrator.md): Manejo de issues y PRs de GitHub solo por comentarios para mantenedores de DeerFlow: resuelve alcance con gh, analiza, publica o redacta comentarios de issues y revisiones de PR, y compara PRs en competencia.

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