Podcast logo

Podcast

Organization
marswaveai
podcast

Create podcasts from topics, URLs, or text. Triggers on: "做播客", "podcast", "播客", "录一期节目", "chat about", "discuss", "debate", "dialogue", "make a podcast about".

Overview

Publishermarswaveai
Repositoryskills
Skill namepodcast
Stars
82
Forks
3
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Podcast 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/marswaveai/skills/tree/main/podcast
  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/marswaveai/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/podcast .claude/skills/podcast
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Podcast 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 Podcast 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 Podcast 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.

When to Use

  • User wants to create a podcast episode on any topic
  • User provides a URL or text and wants it turned into a podcast discussion
  • User asks for a "debate", "dialogue", or "discussion" format
  • User says "podcast", "播客", or "录一期节目"

When NOT to Use

  • User wants text-to-speech reading (use /speech)
  • User wants an explainer video with visuals (use /explainer)
  • User wants to generate an image (use /image-gen)
  • User only wants to extract content from a URL without generating audio (use /content-parser)

Purpose

Generate podcast episodes with 1-2 AI speakers discussing a topic. Supports quick overviews, deep analysis, and debate formats. Input can be a topic description, URL(s), or text. Output is a full audio episode with transcript.

Hard Constraints

  • Always check CLI auth following shared/cli-authentication.md
  • Follow shared/cli-patterns.md for command execution and error handling
  • Never hardcode speaker IDs in API calls — use built-in defaults from shared/speaker-selection.md as fallback only; fetch from the speakers API when the user wants to change voice
  • Never fabricate CLI commands or parameters
  • Always read config following shared/config-pattern.md before any interaction
  • Always follow shared/speaker-selection.md for speaker selection (text table + free-text input)
  • Never save files to ~/Downloads/ or .listenhub/ — save artifacts to the current working directory with friendly topic-based names (see shared/config-pattern.md § Artifact Naming)

Step -1: CLI Auth Check

Follow shared/cli-authentication.md § Auth Check. If the CLI is not installed or the user is not logged in, auto-install and auto-login — never ask the user to run commands manually.

Then follow shared/cli-authentication.md § Auth Mode Detection to determine AUTH_MODE and set:

bash
if [ "$AUTH_MODE" = "openapi" ]; then
  CMD_PREFIX="listenhub openapi podcast"
else
  CMD_PREFIX="listenhub podcast"
fi

All subsequent CLI calls use $CMD_PREFIX instead of hardcoded listenhub podcast.

Step 0: Config Setup

Follow shared/config-pattern.md Step 0 (Zero-Question Boot).

If file doesn't exist — silently create with defaults and proceed:

bash
mkdir -p ".listenhub/podcast"
echo '{"outputMode":"inline","language":null,"defaultMode":"quick","defaultSpeakers":{}}' > ".listenhub/podcast/config.json"
CONFIG_PATH=".listenhub/podcast/config.json"
CONFIG=$(cat "$CONFIG_PATH")

Do NOT ask any setup questions. Proceed directly to the Interaction Flow.

If file exists — read config silently and proceed:

bash
CONFIG_PATH=".listenhub/podcast/config.json"
[ ! -f "$CONFIG_PATH" ] && CONFIG_PATH="$HOME/.listenhub/podcast/config.json"
CONFIG=$(cat "$CONFIG_PATH")

Setup Flow (user-initiated reconfigure only)

Only run when the user explicitly asks to reconfigure. Display current settings:

当前配置 (podcast):
  输出方式:{inline / download / both}
  语言偏好:{zh / en / 未设置}
  默认模式:{quick / deep / debate / 未设置}
  默认主播:{speakerName(s) / 使用内置默认}

Then ask these questions in order and save:

  1. outputMode: Follow shared/output-mode.md § Setup Flow Question.

  2. Language (optional): "默认语言?"

    • "中文 (zh)"
    • "English (en)"
    • "每次手动选择" → keep null
  3. Mode (optional): "默认播客模式?"

    • "Quick — 简短概述"
    • "Deep — 深度分析"
    • "Debate — 辩论对话"
    • "每次手动选择" → keep null

After collecting answers, save immediately:

bash
NEW_CONFIG=$(echo "$CONFIG" | jq --arg m "$OUTPUT_MODE" '. + {"outputMode": $m}')
# Save language if user chose one (not "每次手动选择")
if [ "$LANGUAGE" != "null" ]; then
  NEW_CONFIG=$(echo "$NEW_CONFIG" | jq --arg lang "$LANGUAGE" '. + {"language": $lang}')
fi
# Save mode if user chose one
if [ "$MODE" != "null" ]; then
  NEW_CONFIG=$(echo "$NEW_CONFIG" | jq --arg mode "$MODE" '. + {"defaultMode": $mode}')
fi
echo "$NEW_CONFIG" > "$CONFIG_PATH"
CONFIG=$(cat "$CONFIG_PATH")

Interaction Flow

Step 1: Topic + Reference Materials

Ask topic and optional reference materials together in a single question using AskUserQuestion with two sub-questions, or a single free-text prompt:

What topic would you like to turn into a podcast? If you have reference materials (URLs or text), include them here too.

Accept: topic description, URL(s), pasted text, or any combination.

Examples of valid input:

Step 2: Mode

Default: "quick" — skip this question unless:

  • config.defaultMode is set to something else → use that value silently
  • User explicitly mentioned a mode keyword in Step 1 (e.g. "deep dive", "debate", "in depth") → infer mode from intent

Only ask this question if the user's intent is ambiguous AND no default is configured. In most cases, just use "quick".

Step 3: Language

Default: match the user's interaction language. Detect from the language the user used in Step 1:

  • If the user wrote in Chinese → zh
  • If the user wrote in English → en
  • If config.language is set → use that value

Never ask this question. Always infer silently. Show in the confirmation summary so the user can override if needed.

Step 4: Speaker Count

Default: 2 speakers (dialogue) — the most common and engaging format.

Skip this question. Debate mode requires 2 speakers. For quick/deep, default to 2 speakers as well.

Only use 1 speaker if the user explicitly requests a monologue or solo format.

Step 5: Speaker Selection

Follow shared/speaker-selection.md:

  • If config.defaultSpeakers.{language} is set → use saved speakers silently
  • If not set → use built-in defaults from shared/speaker-selection.md (no question asked)
  • Show the speaker(s) in the confirmation summary — user can change from there if desired
  • Only show the full speaker list if the user explicitly asks to change voices

For 2-speaker mode (dialogue/debate): use Primary + Secondary defaults for the language.

Step 5.5: Generation Speed

Default: 1.0x (original speed). Never ask this question.

Only set a speed when the user explicitly asks for one — "慢一点"、"快一点"、"1.25 倍速"、 "read it faster"、"slow it down". Otherwise omit --speed entirely so the request keeps its historical behaviour.

  • Range: any value from 0.5 to 2.0, at most two decimals — it is a continuous range, not a fixed set of steps.
  • Common values: 0.5, 0.75, 1 (default), 1.25, 1.5, 2. Values in between such as 0.85 or 1.35 are equally valid.
  • Meaning: the speaking rate of the generated audio, not a player playback rate. All speakers in one episode share the same task-level speed.
  • Map vague wording conservatively: "慢一点" → 0.85, "快一点" → 1.25, "慢很多" → 0.5, "快很多" → 1.75. When the user names a number, pass that number through unchanged.

Show the speed in the confirmation summary only when it is not 1.

Step 6: Confirm & Generate

Summarize all choices:

Ready to generate podcast:

  Topic: {topic}
  Mode: {mode}
  Language: {language}
  Speakers: {speaker name(s)}
  Speed: {speed}x        # omit this line when speed is 1
  References: {yes/no + brief description}

  Proceed?

Wait for explicit confirmation before calling any CLI command. The user can adjust any parameter here before confirming.

Workflow

Generation

  1. Submit (background): Run the CLI command with run_in_background: true and timeout: 360000:

    bash
    $CMD_PREFIX create \
      --query "{topic}" \
      --source-url "{url}" \
      --source-text "{text}" \
      --mode {quick|deep|debate} \
      --lang {en|zh|ja} \
      --speaker "{name}" \
      --speaker "{name2}" \
      --speed {0.5-2.0} \
      --json

    Flag notes:

    • --query — the topic or question to discuss
    • --source-url — repeatable, one per URL reference
    • --source-text — repeatable, one per text block reference
    • --mode — one of quick, deep, debate
    • --lang — language code
    • --speaker — repeatable (max 2); use speaker display names
    • --speaker-id — alternative to --speaker; use speaker IDs instead of names
    • --speed — generation speed multiplier 0.5–2.0 (max two decimals, default 1); omit it unless the user asked for a different speed
    • Omit --source-url / --source-text if the user provided no references

    The CLI handles polling internally and returns the final result when generation completes.

  2. Tell the user the task is submitted and that they will be notified when it finishes.

  3. When notified of completion, Present result:

    Parse the CLI JSON output to extract fields: audioUrl, subtitlesUrl, audioDuration, credits.

    Read OUTPUT_MODE from config. Follow shared/output-mode.md for behavior.

    inline or both: Display audioUrl as a clickable link.

    Present:

    播客已生成!
    
    在线收听:{audioUrl}
    字幕:{subtitlesUrl}(如有)
    时长:{audioDuration / 1000}s
    消耗积分:{credits}

    download or both: Also download the file. Generate a topic slug following shared/config-pattern.md § Artifact Naming.

    bash
    SLUG="{topic-slug}"  # e.g. "ai-developments"
    NAME="${SLUG}-podcast.mp3"
    # Dedup: if file exists, append -2, -3, etc.
    BASE="${NAME%.*}"; EXT="${NAME##*.}"; i=2
    while [ -e "$NAME" ]; do NAME="${BASE}-${i}.${EXT}"; i=$((i+1)); done
    curl -sS -o "$NAME" "{audioUrl}"

    Present:

    已保存到当前目录:
      {NAME}
  4. Offer to show transcript or provide download URL on request

After Successful Generation

Update config with the choices made this session:

bash
NEW_CONFIG=$(echo "$CONFIG" | jq \
  --arg lang "{language}" \
  --arg mode "{mode}" \
  --argjson speakers '{"{language}": ["{speakerId}"]}' \
  '. + {"language": $lang, "defaultMode": $mode, "defaultSpeakers": (.defaultSpeakers + $speakers)}')
echo "$NEW_CONFIG" > "$CONFIG_PATH"

API Reference

  • Speaker list: shared/cli-speakers.md
  • Speaker selection guide: shared/speaker-selection.md
  • CLI patterns: shared/cli-patterns.md
  • CLI authentication: shared/cli-authentication.md
  • Config pattern: shared/config-pattern.md

Composability

  • Invokes: speakers API (for speaker selection)
  • Invoked by: content-planner (Phase 3)

Example

User: "Make a podcast about the latest AI developments"

Agent workflow:

  1. Detect: podcast request, topic = "latest AI developments", no references
  2. Infer: mode = "quick" (default), language = "en" (user wrote in English), 2 speakers (default)
  3. Show confirmation summary → user confirms
bash
$CMD_PREFIX create \
  --query "The latest AI developments" \
  --mode deep \
  --lang en \
  --speaker "Mars" \
  --speaker "Mia" \
  --json

Wait for CLI to return result, then present with title and listen link.

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 Podcast AI skill do?

Create podcasts from topics, URLs, or text. Triggers on: "做播客", "podcast", "播客", "录一期节目", "chat about", "discuss", "debate", "dialogue", "make a podcast about".

Why use Podcast on TypingMind?

Because you install it once and use it with any model. Podcast 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 Podcast in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/marswaveai/skills/tree/main/podcast. 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 Podcast?

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 Podcast?

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

Is the Podcast AI skill free?

Yes. It is published on GitHub by marswaveai 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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