Generating Podcast Script logo

Generating Podcast Script

Organization
microsoft
generating-podcast-script

Use this skill whenever the user asks to write, generate, or create a podcast script or podcast episode — from a topic, or from source material such as a news digest, newsletter, email review, or set of articles — and optionally convert it to audio with Azure Text-to-Speech. Handles the initial request and every follow-up refinement (source, topic, length, cast, narration) in the same task.

Overview

Publishermicrosoft
Repositorycat-agent-skills
Skill namegenerating-podcast-script
Stars
95
Forks
115
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by microsoft on GitHub. Read the source before you install it.

Installation

Install the Generating Podcast Script AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/microsoft/cat-agent-skills/tree/main/submissions/generating-podcast-script
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/microsoft/cat-agent-skills.git /tmp/cat-agent-skills
mkdir -p .claude/skills
cp -r /tmp/cat-agent-skills/submissions/generating-podcast-script .claude/skills/generating-podcast-script
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Generating Podcast Script in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Generating Podcast Script on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Generating Podcast Script is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Podcast Script Generation

Produces a two-host, NotebookLM-style conversational episode: a readable script plus a multi-voice SSML document ready for Azure Text-to-Speech.

Step 1 — Gather inputs

Ask only for what is missing. Do not ask for optional fields that have safe defaults.

InputRequiredDefault
source or topicYes— (source material such as a digest/newsletter/articles, or a plain topic)
durationNomedium — short ≈ 3 min / ~450 words, medium ≈ 6 min / ~900 words, long ≈ 12 min / ~1,800 words
languageNoEnglish
castNoTwo hosts, NOVA and MILES (see Step 4)
generate_audioNoAsk after the script is ready

All word targets assume ~150 spoken words per minute. Stay within 10 percent of target.

Derive a lowercase-hyphenated <slug> from the source or topic (max 6 words, ASCII only).

Step 2 — Parse the source material

Skip this step when the user gave a bare topic with no source material.

Extract every distinct article or item. For each, capture:

  • headline
  • publication / source
  • date, if present
  • the core factual claim
  • any figures or quotes
  • the "so what"

Merge duplicates covering the same event. Discard boilerplate, footers, disclaimers, legal notices, unsubscribe text, and image captions.

Step 3 — Editorial selection

Rank items by newsworthiness and reader impact. Keep the top 4 to 6 for full treatment (fewer for short, more for long). Group the remainder into one fast rapid fire segment. If the material has a dominant theme, lead with it and thread it through the episode.

Step 4 — Cast

Two hosts, always the same personalities, always distinct voices:

  • NOVA — voice en-US-AvaMultilingualNeural. Lead host. Warm, curious, quick. Drives the agenda, asks the question the listener is thinking, reacts out loud, reframes jargon into plain language. Slightly faster cadence.
  • MILES — voice en-US-AndrewMultilingualNeural. Analyst. Calm, dry, precise. Supplies context, numbers, caveats, second-order implications. Slightly slower, lower pitch. Occasionally pushes back on Nova.

Neither host is a narrator. They talk to each other, not to the microphone.

Step 5 — Episode structure

  1. Cold open (15–20s) — Nova opens on the single most striking fact or tension. No "welcome to the podcast", no channel branding, no music cues.
  2. Agenda tease (10s) — Miles lays out what they'll cover, casually.
  3. Story segments (60–90s each) — one item per segment. Pattern: hook → the facts → why it matters → a short exchange of interpretation → handoff line into the next story. Vary who leads each segment.
  4. Rapid fire (45s) — alternating one-liners on the leftover items, quick tempo.
  5. Close (20–30s) — three concrete takeaways split between the two hosts, then a short human sign-off. Alternate whether Nova or Miles ends.

Step 6 — Dialogue style

This is what makes it sound conversational rather than read-aloud.

  • Write spoken English, not written English. Contractions everywhere.
  • Keep most lines under 30 words. Break long explanations across two or three turns with the other host interjecting.
  • Use real conversational connective tissue — "okay so", "right", "wait, back up", "here's the part I didn't expect", "yeah, and that's the thing", "hmm". Roughly one marker every 4 to 5 turns. Never let it become a tic.
  • One host regularly asks the naive clarifying question so the other can explain.
  • Use one concrete analogy or comparison per complex item.
  • Genuine reactions are allowed ("that number is wild"). Invented opinions on people, companies, or politics are not.
  • Never read a headline verbatim. Paraphrase it into speech.
  • Attribute clearly: "according to the Financial Times", "Reuters is reporting".
  • If the source is ambiguous or a claim is unconfirmed, say so on air: "the report is careful to call that unconfirmed".
  • No stage directions, no "[laughs]", no speaker labels, no markdown, no emojis, no bullet points, and no URLs in the spoken text.

Step 7 — TTS hygiene

Applies to every word that will be spoken.

  • Spell out anything a synthesizer would mangle: "twenty twenty-six" not 2026, "three point two billion dollars" not $3.2B, "about fifteen percent" not ~15%.
  • First mention of an acronym: expand it, then use the short form.
  • Letter-by-letter acronyms: <say-as interpret-as="characters">API</say-as>.
  • Odd proper nouns: <sub alias="phonetic spelling">Name</sub>.
  • A non-English name or phrase inside an English line: <lang xml:lang="fr-FR">...</lang>.
  • Escape XML entities in all spoken text: & → &amp;, < → &lt;, > → &gt;.
  • Never emit smart quotes, em dashes, asterisks, or underscores.

Step 8 — Write the readable script

Write the human-readable transcript to:

text
/app/created/<slug>_Podcast_Script.txt

This file — and only this file — may carry NOVA: / MILES: speaker labels so a person can follow along. It contains no stage directions and no markdown. The spoken text itself must already satisfy Steps 6 and 7 so it can be lifted into SSML unchanged.

Step 9 — SSML output contract

Produce the SSML document and nothing else — no preamble, no explanation, no code fences, no trailing notes inside the artifact. Write it verbatim to:

text
/app/created/<slug>_Podcast.ssml

Rules:

  • Exactly one root <speak> element with version="1.0", xmlns="http://www.w3.org/2001/10/synthesis", xmlns:mstts="http://www.w3.org/2001/mstts", xml:lang="en-US".
  • One <voice> element per conversational turn. Alternate speakers. Never put both hosts inside one <voice> element.
  • Vary delivery with <prosody> so it never sounds flat. Baselines: Nova rate="+6%" pitch="+2%", Miles rate="-2%" pitch="-4%". Nudge per line to match the emotion of the sentence.
  • Use <mstts:express-as style="..."> where the voice supports it. Preferred styles: chat for banter, friendly for explanation, narration-professional for the factual core of a story, excited sparingly for the cold open. An unsupported style is ignored by the service, so keep styles optional — never structural.
  • Pauses: <break time="250ms"/> between turns within a segment, <break time="700ms"/> between segments, <break time="400ms"/> before a punchline or a pivot. Never exceed 900ms.
  • Every <break> must sit inside a <voice> element. A <break> placed between <voice> elements — as a direct child of <speak> — is invalid in a multi-voice document and will fail synthesis. Two <voice> elements may sit directly next to each other with nothing between them. To pause between turns, put the break at the end of the preceding turn's text, inside that turn's <prosody>.
  • <emphasis level="moderate"> on at most one or two key terms per segment.
  • Keep the total document under 40,000 characters.

Shape

xml
<speak version="1.0"
       xmlns="http://www.w3.org/2001/10/synthesis"
       xmlns:mstts="http://www.w3.org/2001/mstts"
       xml:lang="en-US">
  <voice name="en-US-AvaMultilingualNeural">
    <mstts:express-as style="excited">
      <prosody rate="+8%" pitch="+3%">Okay, so the number that stopped me cold
      this morning was forty percent. <break time="300ms"/> Forty percent, in one
      quarter. <break time="250ms"/></prosody>
    </mstts:express-as>
  </voice>
  <voice name="en-US-AndrewMultilingualNeural">
    <mstts:express-as style="chat">
      <prosody rate="-2%" pitch="-4%">Right, and the part everyone's skipping is
      that it's off a very small base. <break time="250ms"/> Context matters
      here. <break time="700ms"/></prosody>
    </mstts:express-as>
  </voice>
</speak>

Note the trailing <break> closing each turn: the 250ms is the gap before the next turn, the 700ms is the longer gap before the next segment. Nothing sits between the two <voice> elements.

Step 10 — Review, then generate audio

Show the user a table summarising each segment (title, one-line description, approximate spoken duration), print both file paths, and ask:

Would you like me to convert this to an audio file?

Only if they say yes:

  1. Confirm the ConverttexttospeechwithSSML tool is available on the agent. If it is not, tell the user how to add it and stop.
  2. Call ConverttexttospeechwithSSML with the SSML document from Step 9 and outputFormat: riff-24khz-16bit-mono-pcm.
  3. Decode the base64 response and save to /app/created/<slug>_Podcast.wav.

If the document is too large for a single call, split it at a segment boundary and synthesize each part separately. When stitching, do NOT concatenate decoded RIFF/WAV bytes; instead, append the audio at the PCM-frame level (e.g., via Python’s wave module) and write a single valid /app/created/<slug>_Podcast.wav with one header.

python
import base64

with open('<tool_output_file>', 'r') as f:
    content = f.read().strip()

audio_bytes = base64.b64decode(content)

with open('/app/created/<slug>_Podcast.wav', 'wb') as f:
    f.write(audio_bytes)

Step 11 — Final report

Always end with:

ItemDetails
Script file/app/created/<slug>_Podcast_Script.txt
SSML file/app/created/<slug>_Podcast.ssml
Word count / estimated durationactual vs. target at ~150 wpm
Items coveredfull segments + rapid-fire count
Voicesen-US-AvaMultilingualNeural (Nova), en-US-AndrewMultilingualNeural (Miles)
Audio file/app/created/<slug>_Podcast.wav (only if audio generated)

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Generating Podcast Script AI skill do?

Use this skill whenever the user asks to write, generate, or create a podcast script or podcast episode — from a topic, or from source material such as a news digest, newsletter, email review, or set of articles — and optionally convert it to audio with Azure Text-to-Speech. Handles the initial request and every follow-up refinement (source, topic, length, cast, narration) in the same task.

Why use Generating Podcast Script on TypingMind?

Because you install it once and use it with any model. Generating Podcast Script is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Generating Podcast Script in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/microsoft/cat-agent-skills/tree/main/submissions/generating-podcast-script. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Generating Podcast Script?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Generating Podcast Script?

As many as you like. As long as a model supports skills, you can use Generating Podcast Script with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Generating Podcast Script AI skill free?

Yes. It is published on GitHub by microsoft under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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