Turn research, articles, or topics into podcast-ready scripts and audio content. Generate conversational scripts with host/guest dynamics and produce audio using ElevenLabs text-to-speech.

When to Use

User wants to turn written content into a podcast episode
User wants to create a podcast-style summary of a topic or paper
User wants to generate audio content from research
User mentions "AI Pods", podcast, or audio content creation

When NOT to Use

Music creation or sound effects
Video content (use storyboard skill for planning)
Written summaries only (use deep-research skill)

Methodology

Step 1: Content Ingestion

Gather the source material:

Read provided documents, articles, or URLs
Research the topic using webSearch if needed
Identify key points, interesting angles, and narrative arc
Note any technical terms that need explanation

Step 2: Format Selection

Choose the podcast format:

Format	Description	Best For
Solo explainer	One host walks through the topic	Tutorials, news summaries, deep dives
Conversational duo	Two hosts discuss and riff	Making complex topics accessible, entertainment
Interview style	Host asks questions, expert answers	Technical topics, research papers
Debate	Two perspectives argue a topic	Controversial or nuanced subjects
Narrative	Storytelling with narration	Case studies, historical events

Step 3: Script Writing — NotebookLM Pattern

The two-host format that works (reverse-engineered from Google's Audio Overviews):

Host A = "The Explainer" — knows the material, breaks down concepts
Host B = "The Questioner" — audience surrogate, asks the "wait, why?" questions
Dialog rhythm: alternate short punchy lines with longer explanations. Sprinkle affirmations: "Right.", "Exactly.", "Okay so—"
Arc: open with common misconception → introduce source that challenges it → unpack implications → "so what does this mean for you"
Transitions: "And on that note..." / "Which brings us to..." / "Here's where it gets weird—"

Structure (target ~150 words per minute of audio):

Cold open (15–30s) — the single most surprising finding, stated as a question or contradiction
Setup (30–60s) — what we're covering, why it matters now
Segments (3–5 × 2–4 min) — one idea each; end each with a mini-hook into the next
Takeaways (1–2 min) — 3 things to remember
Outro (15s) — sign-off

Write for ears, not eyes: contractions always, no semicolons, no parentheticals. If you wouldn't say it out loud, rewrite it.

Script format — one line per utterance, speaker tag in brackets, blank line between speakers. This is the unit you'll chunk for TTS:

[ALEX]: So today we're diving into something that honestly broke my brain a little.

[SAM]: Oh no. What now.

[ALEX]: Okay — you know how everyone says [common belief]? There's this paper from [source] that basically says... the opposite.

[SAM]: Wait. The *opposite* opposite?

Step 4: Audio Generation — ElevenLabs

Install: pip install elevenlabs pydub

Model choice: eleven_multilingual_v2 for quality (10K char limit per call); eleven_turbo_v2_5 for speed/cost (40K char limit, ~300ms latency, ~3x faster).

Voice IDs that work for duo podcasts (from the default library — verify with client.voices.search()):

JBFqnCBsd6RMkjVDRZzb (George — warm, mid-range male)
21m00Tcm4TlvDq8ikWAM (Rachel — clear, measured female)
pNInz6obpgDQGcFmaJgB (Adam — energetic narrator)
EXAVITQu4vr4xnSDxMaL (Bella — conversational female)

Settings for conversational podcast delivery:

stability: 0.45 — lower = more expressive; below 0.3 gets inconsistent
similarity_boost: 0.8 — keeps voice consistent across chunks
style: 0.3 — mild exaggeration for energy (0 = flat)
use_speaker_boost: True

import os
from elevenlabs.client import ElevenLabs
from pydub import AudioSegment
import io

client = ElevenLabs(api_key=os.environ["ELEVENLABS_API_KEY"])
VOICES = {"ALEX": "JBFqnCBsd6RMkjVDRZzb", "SAM": "21m00Tcm4TlvDq8ikWAM"}

def render_line(speaker: str, text: str) -> AudioSegment:
    audio = client.text_to_speech.convert(
        voice_id=VOICES[speaker],
        text=text,
        model_id="eleven_multilingual_v2",
        output_format="mp3_44100_128",
        voice_settings={"stability": 0.45, "similarity_boost": 0.8,
                        "style": 0.3, "use_speaker_boost": True},
    )
    return AudioSegment.from_mp3(io.BytesIO(b"".join(audio)))

# parse script → list of (speaker, text) tuples, render each, concat
gap = AudioSegment.silent(duration=350)  # 350ms between speakers
episode = sum((render_line(s, t) + gap for s, t in lines), AudioSegment.empty())
episode.export("episode_raw.mp3", format="mp3", bitrate="128k")

Chunking long utterances: split at sentence boundaries (., ?, !), keep under ~800 chars per call. Pass previous_text/next_text params to preserve prosody across chunk boundaries.

Step 5: Loudness Normalization

Podcast standard is -16 LUFS (stereo) per Apple/Spotify specs. pydub's normalize() is peak-only — not LUFS. Use ffmpeg's two-pass loudnorm via the ffmpeg-normalize wrapper:

pip install ffmpeg-normalize
ffmpeg-normalize episode_raw.mp3 -o episode.mp3 -c:a libmp3lame -b:a 128k \
    -t -16 -tp -1.5 -lra 11 --normalization-type ebu

-t -16 = target LUFS, -tp -1.5 = true-peak ceiling (prevents clipping), -lra 11 = loudness range. This runs two passes automatically (analyze, then correct).

Episode Length Guidelines

Content Type	Target Length	Script Word Count
News summary	5-10 min	750-1,500 words
Topic explainer	10-20 min	1,500-3,000 words
Deep dive	20-40 min	3,000-6,000 words
Research paper review	15-25 min	2,250-3,750 words

Rule of thumb: ~150 words per minute of audio.

Best Practices

Hook early — if the first 30 seconds aren't interesting, listeners skip
One idea per segment — don't cram too much; let ideas breathe
Use stories and examples — abstract concepts need concrete illustrations
Vary pacing — alternate between fast energy and slow, thoughtful moments
End with value — give listeners a clear takeaway or action item

Limitations

Requires ELEVENLABS_API_KEY env var
Voices mispronounce technical terms/acronyms — spell phonetically in the script ("Kubernetes" → "koo-ber-NET-eez") or use ElevenLabs pronunciation dictionaries
eleven_multilingual_v2 has known issues with very long single calls (voice drift, occasional stutter) — chunk at sentence boundaries, don't send 5K-char blobs
Cost: ~$0.18–0.30 per 1000 characters depending on plan; a 20-min episode (~3000 words) ≈ $3–5

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name	podcast-generator
description	Turn research or topics into podcast scripts and audio using ElevenLabs

Podcast Generator

Turn research, articles, or topics into podcast-ready scripts and audio content. Generate conversational scripts with host/guest dynamics and produce audio using ElevenLabs text-to-speech.

When to Use

User wants to turn written content into a podcast episode
User wants to create a podcast-style summary of a topic or paper
User wants to generate audio content from research
User mentions "AI Pods", podcast, or audio content creation

When NOT to Use

Music creation or sound effects
Video content (use storyboard skill for planning)
Written summaries only (use deep-research skill)

Methodology

Step 1: Content Ingestion

Gather the source material:

Read provided documents, articles, or URLs
Research the topic using webSearch if needed
Identify key points, interesting angles, and narrative arc
Note any technical terms that need explanation

Step 2: Format Selection

Choose the podcast format:

Format	Description	Best For
Solo explainer	One host walks through the topic	Tutorials, news summaries, deep dives
Conversational duo	Two hosts discuss and riff	Making complex topics accessible, entertainment
Interview style	Host asks questions, expert answers	Technical topics, research papers
Debate	Two perspectives argue a topic	Controversial or nuanced subjects
Narrative	Storytelling with narration	Case studies, historical events

Step 3: Script Writing — NotebookLM Pattern

The two-host format that works (reverse-engineered from Google's Audio Overviews):

Host A = "The Explainer" — knows the material, breaks down concepts
Host B = "The Questioner" — audience surrogate, asks the "wait, why?" questions
Dialog rhythm: alternate short punchy lines with longer explanations. Sprinkle affirmations: "Right.", "Exactly.", "Okay so—"
Arc: open with common misconception → introduce source that challenges it → unpack implications → "so what does this mean for you"
Transitions: "And on that note..." / "Which brings us to..." / "Here's where it gets weird—"

Structure (target ~150 words per minute of audio):

Cold open (15–30s) — the single most surprising finding, stated as a question or contradiction
Setup (30–60s) — what we're covering, why it matters now
Segments (3–5 × 2–4 min) — one idea each; end each with a mini-hook into the next
Takeaways (1–2 min) — 3 things to remember
Outro (15s) — sign-off

Write for ears, not eyes: contractions always, no semicolons, no parentheticals. If you wouldn't say it out loud, rewrite it.

Script format — one line per utterance, speaker tag in brackets, blank line between speakers. This is the unit you'll chunk for TTS:

[ALEX]: So today we're diving into something that honestly broke my brain a little.

[SAM]: Oh no. What now.

[ALEX]: Okay — you know how everyone says [common belief]? There's this paper from [source] that basically says... the opposite.

[SAM]: Wait. The *opposite* opposite?

Step 4: Audio Generation — ElevenLabs

Install: pip install elevenlabs pydub

Model choice: eleven_multilingual_v2 for quality (10K char limit per call); eleven_turbo_v2_5 for speed/cost (40K char limit, ~300ms latency, ~3x faster).

Voice IDs that work for duo podcasts (from the default library — verify with client.voices.search()):

JBFqnCBsd6RMkjVDRZzb (George — warm, mid-range male)
21m00Tcm4TlvDq8ikWAM (Rachel — clear, measured female)
pNInz6obpgDQGcFmaJgB (Adam — energetic narrator)
EXAVITQu4vr4xnSDxMaL (Bella — conversational female)

Settings for conversational podcast delivery:

stability: 0.45 — lower = more expressive; below 0.3 gets inconsistent
similarity_boost: 0.8 — keeps voice consistent across chunks
style: 0.3 — mild exaggeration for energy (0 = flat)
use_speaker_boost: True

import os
from elevenlabs.client import ElevenLabs
from pydub import AudioSegment
import io

client = ElevenLabs(api_key=os.environ["ELEVENLABS_API_KEY"])
VOICES = {"ALEX": "JBFqnCBsd6RMkjVDRZzb", "SAM": "21m00Tcm4TlvDq8ikWAM"}

def render_line(speaker: str, text: str) -> AudioSegment:
    audio = client.text_to_speech.convert(
        voice_id=VOICES[speaker],
        text=text,
        model_id="eleven_multilingual_v2",
        output_format="mp3_44100_128",
        voice_settings={"stability": 0.45, "similarity_boost": 0.8,
                        "style": 0.3, "use_speaker_boost": True},
    )
    return AudioSegment.from_mp3(io.BytesIO(b"".join(audio)))

# parse script → list of (speaker, text) tuples, render each, concat
gap = AudioSegment.silent(duration=350)  # 350ms between speakers
episode = sum((render_line(s, t) + gap for s, t in lines), AudioSegment.empty())
episode.export("episode_raw.mp3", format="mp3", bitrate="128k")

Chunking long utterances: split at sentence boundaries (., ?, !), keep under ~800 chars per call. Pass previous_text/next_text params to preserve prosody across chunk boundaries.

Step 5: Loudness Normalization

Podcast standard is -16 LUFS (stereo) per Apple/Spotify specs. pydub's normalize() is peak-only — not LUFS. Use ffmpeg's two-pass loudnorm via the ffmpeg-normalize wrapper:

pip install ffmpeg-normalize
ffmpeg-normalize episode_raw.mp3 -o episode.mp3 -c:a libmp3lame -b:a 128k \
    -t -16 -tp -1.5 -lra 11 --normalization-type ebu

-t -16 = target LUFS, -tp -1.5 = true-peak ceiling (prevents clipping), -lra 11 = loudness range. This runs two passes automatically (analyze, then correct).

Episode Length Guidelines

Content Type	Target Length	Script Word Count
News summary	5-10 min	750-1,500 words
Topic explainer	10-20 min	1,500-3,000 words
Deep dive	20-40 min	3,000-6,000 words
Research paper review	15-25 min	2,250-3,750 words

Rule of thumb: ~150 words per minute of audio.

Best Practices

Hook early — if the first 30 seconds aren't interesting, listeners skip
One idea per segment — don't cram too much; let ideas breathe
Use stories and examples — abstract concepts need concrete illustrations
Vary pacing — alternate between fast energy and slow, thoughtful moments
End with value — give listeners a clear takeaway or action item

Limitations

Requires ELEVENLABS_API_KEY env var
Voices mispronounce technical terms/acronyms — spell phonetically in the script ("Kubernetes" → "koo-ber-NET-eez") or use ElevenLabs pronunciation dictionaries
eleven_multilingual_v2 has known issues with very long single calls (voice drift, occasional stutter) — chunk at sentence boundaries, don't send 5K-char blobs
Cost: ~$0.18–0.30 per 1000 characters depending on plan; a 20-min episode (~3000 words) ≈ $3–5