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Explainer

Organization
marswaveai
explainer

Create explainer videos with narration and AI-generated visuals. Triggers on: "解说视频", "explainer video", "explain this as a video", "tutorial video", "introduce X (video)", "解释一下XX(视频形式)".

Overview

Publishermarswaveai
Repositoryskills
Skill nameexplainer
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 Explainer 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/explainer
  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/explainer .claude/skills/explainer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Explainer 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 Explainer 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 Explainer 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 an explainer or tutorial video
  • User asks to "explain" something in video form
  • User wants narrated content with AI-generated visuals
  • User says "explainer video", "解说视频", "tutorial video"

When NOT to Use

  • User wants audio-only content without visuals (use /speech or /podcast)
  • User wants a podcast-style discussion (use /podcast)
  • User wants to generate a standalone image (use /image-gen)
  • User wants to read text aloud without video (use /speech)

Purpose

Generate explainer videos that combine a single narrator's voiceover with AI-generated visuals. Ideal for product introductions, concept explanations, and tutorials. Supports text-only script generation or full text + video output.

Hard Constraints

  • Always read config following shared/config-pattern.md before any interaction
  • Follow shared/cli-patterns.md for execution modes, error handling, and interaction patterns
  • Always follow shared/cli-authentication.md for auth checks
  • Never hardcode speaker IDs — always fetch from the speakers CLI when the user wants to change voice
  • 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)
  • Explainer uses exactly 1 speaker
  • Mode must be info (for Info style) or story (for Story style) — never slides (use /slides skill instead)

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 storybook"
else
  CMD_PREFIX="listenhub explainer"
fi

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

Note: The OpenAPI command is storybook (not explainer) — same backend, different naming.

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/explainer"
echo '{"outputMode":"inline","language":null,"defaultStyle":null,"defaultSpeakers":{}}' > ".listenhub/explainer/config.json"
CONFIG_PATH=".listenhub/explainer/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/explainer/config.json"
[ ! -f "$CONFIG_PATH" ] && CONFIG_PATH="$HOME/.listenhub/explainer/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 (explainer):
  输出方式:{inline / download / both}
  语言偏好:{zh / en / 未设置}
  默认风格:{info / story / 未设置}
  默认主播:{speakerName / 使用内置默认}

Then ask:

  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. Style (optional): "默认风格?"

    • "Info — 信息展示型"
    • "Story — 故事叙述型"
    • "每次手动选择" → keep null

After collecting answers, save immediately:

bash
NEW_CONFIG=$(echo "$CONFIG" | jq --arg m "$OUTPUT_MODE" '. + {"outputMode": $m}')
echo "$NEW_CONFIG" > "$CONFIG_PATH"
CONFIG=$(cat "$CONFIG_PATH")

Interaction Flow

Step 1: Topic / Content

Free text input. Ask the user:

What would you like to explain or introduce?

Accept: topic description, text content, or concept to explain.

Step 2: Language

If config.language is set, pre-fill and show in summary — skip this question. Otherwise ask:

Question: "What language?"
Options:
  - "Chinese (zh)" — Content in Mandarin Chinese
  - "English (en)" — Content in English
  - "Japanese (ja)" — Content in Japanese

Step 3: Style

If config.defaultStyle is set, pre-fill and show in summary — skip this question. Otherwise ask:

Question: "What style of explainer?"
Options:
  - "Info" — Informational, factual presentation style
  - "Story" — Narrative, storytelling approach

Step 4: Speaker Selection

Follow shared/speaker-selection.md:

  • If config.defaultSpeakers.{language} is set → use saved speaker silently
  • If not set → use built-in default from shared/speaker-selection.md for the language
  • Show the speaker in the confirmation summary (Step 6) — user can change from there if desired
  • Only show the full speaker list if the user explicitly asks to change voice

Speaker query: see shared/cli-speakers.md for listing and filtering speakers.

Only 1 speaker is supported for explainer videos.

Step 5: Output Type

Question: "What output do you want?"
Options:
  - "Text script only" — Generate narration script, no video
  - "Text + Video" — Generate full explainer video with AI visuals

Step 6: Confirm & Generate

Summarize all choices:

Ready to generate explainer:

  Topic: {topic}
  Language: {language}
  Style: {info/story}
  Speaker: {speaker name}
  Output: {text only / text + video}

  Proceed?

Wait for explicit confirmation before running any CLI command.

Workflow

Run the CLI command with run_in_background: true and timeout: 660000. The CLI blocks until generation completes and returns the final result as JSON:

bash
$CMD_PREFIX create \
  --query "{topic}" \
  --mode {info|story} \
  --lang {en|zh|ja} \
  --speaker "{name}" \
  --speaker-id "{id}" \
  --timeout 600 \
  --json

If the command fails (non-zero exit), check stderr for error details. See shared/cli-patterns.md § Error Handling for exit codes and common errors.

Optional flags (add when applicable):

  • --source-url "{url}" — if the user provided a reference URL
  • --skip-audio — if text-only output (no video)
  • --image-size {2K|4K} — image resolution (default: 2K)
  • --aspect-ratio {16:9|9:16|1:1} — video aspect ratio (default: 16:9)
  • --style "{style}" — visual style for AI-generated images

Tell the user the task is submitted. When notified of completion, parse and present result:

Parse the CLI JSON output for key fields:

bash
EPISODE_ID=$(echo "$RESULT" | jq -r '.episodeId')
AUDIO_URL=$(echo "$RESULT" | jq -r '.audioUrl // empty')
VIDEO_URL=$(echo "$RESULT" | jq -r '.videoUrl // empty')
CREDITS=$(echo "$RESULT" | jq -r '.credits // empty')

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

If text-only output:

inline or both: Present the script inline.

Present:

解说脚本已生成!

「{title}」

在线查看:https://listenhub.ai/app/explainer/{episodeId}

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

  • Save as {slug}-explainer.md in cwd (dedup if exists)
  • Present the save path in addition to the above summary.

If text + video output:

inline or both: Display video URL and audio URL as clickable links.

Present:

解说视频已生成!

视频链接:{videoUrl}
音频链接:{audioUrl}
消耗积分:{credits}

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

  • Create {slug}-explainer/ folder (dedup if exists)
  • Write script.md inside
  • Download audio:
    bash
    listenhub download "{audioUrl}" -o "{slug}-explainer/audio.mp3"
  • Present:
    已保存到当前目录:
      {slug}-explainer/
        script.md
        audio.mp3

After Successful Generation

Update config with the choices made this session:

bash
NEW_CONFIG=$(echo "$CONFIG" | jq \
  --arg lang "{language}" \
  --arg style "{info/story}" \
  --arg speakerId "{speakerId}" \
  '. + {"language": $lang, "defaultStyle": $style, "defaultSpeakers": (.defaultSpeakers + {($lang): [$speakerId]})}')
echo "$NEW_CONFIG" > "$CONFIG_PATH"

Estimated times:

  • Text script only: 2-3 minutes
  • Text + Video: 5-10 minutes

Resources

  • CLI authentication: shared/cli-authentication.md
  • CLI patterns: shared/cli-patterns.md
  • Speaker query: shared/cli-speakers.md
  • Speaker selection guide: shared/speaker-selection.md
  • Config pattern: shared/config-pattern.md
  • Output mode: shared/output-mode.md

Composability

  • Invokes: speakers CLI (for speaker selection); may invoke /speech for voiceover
  • Invoked by: content-planner (Phase 3)

Example

User: "Create an explainer video introducing Claude Code"

Agent workflow:

  1. Topic: "Claude Code introduction"
  2. Ask language → "English"
  3. Ask style → "Info"
  4. Use default speaker "Mars" (cozy-man-english)
  5. Ask output → "Text + Video"
bash
# Run with run_in_background: true, timeout: 660000
$CMD_PREFIX create \
  --query "Introduce Claude Code: what it is, key features, and how to get started" \
  --mode info \
  --lang en \
  --speaker "Mars" \
  --speaker-id "cozy-man-english" \
  --timeout 600 \
  --json

Parse result for episodeId, audioUrl, videoUrl, credits, and present to user.

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 Explainer AI skill do?

Create explainer videos with narration and AI-generated visuals. Triggers on: "解说视频", "explainer video", "explain this as a video", "tutorial video", "introduce X (video)", "解释一下XX(视频形式)".

Why use Explainer on TypingMind?

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

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

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 Explainer?

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

Is the Explainer 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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