Book Video Generator En

Generates a 3-minute English video summarizing any book by creating script, storyboard, AI illustrations, TTS narration, subtitles, and final MP4 automatically.

3-Minute Book Digest — English Version

Overview

Automatically turn any book into a 3-minute explainer video: from writing the review script, splitting it into storyboards, AI illustration, English TTS narration, to subtitle compositing — fully automated.

Derived from the Coze workflow "Pipadushu_video_1". This Skill follows the Agent Skills open standard and is cross-platform compatible with WorkBuddy, OpenClaw, Codex CLI, and TRAE Work.

It replaces Coze plugins with local open-source tools: 剪映小助手 (Jianying assistant) → ffmpeg, Coze image generation → multi-model image generation (default ImageGen + alternatives Volcano Seedream / Gemini / Agnes), Coze TTS → Volcano Engine TTS (default) / edge-tts (fallback). All script, narration, on-screen text, and prompts are fully in English.

Platform Tool Mapping

This Skill's workflow involves 3 platform-related tools. Pick the matching tool for the platform you are running on.

Web Search (Stage 1 — search for book info)

Platform	Tool	Notes
WorkBuddy	WebSearch	Built-in tool, call directly
OpenClaw	built-in web search	Auto-available
Codex CLI	shell: curl or search MCP	Use shell commands or install a search MCP
TRAE Work	built-in web search	Auto-available

Image Generation (Stage 4a — storyboard illustrations)

Provides 1 default + 3 alternatives. Switch via the IMAGE_API env var:

Plan	Tool	Model	Env var	Notes
🏠 Default	ImageGen	Tencent Hunyuan (WorkBuddy built-in)	none	Call DeferExecuteTool directly in WorkBuddy
🏔️ Alt	volcengine	Volcano Seedream 5.0 lite (ByteDance)	ARK_API_KEY (recommended) or VOLCENGINE_AK + VOLCENGINE_SK	Original Coze image model; best for flat Chinese/English style, supports watermark removal
🤖 Alt	gemini	Google Gemini 3 Pro Image	GEMINI_API_KEY	Rich detail, strong semantic understanding
✨ Alt	agnes	Agnes AI (completely free)	AGNES_API_KEY	Free registration, OpenAI-compatible API

Switching:

• WorkBuddy: uses ImageGen by default. To switch, set IMAGE_API=volcengine|gemini|agnes, the script auto-calls scripts/generate_image.py.
• CLI platforms (Codex CLI / OpenClaw): run scripts/generate_image.py --api <plan> directly.

On every platform, the image prompt uses the desc_promopt field from the storyboard.

LLM Calls (Stages 1-3 — script & storyboard)

Every platform has built-in LLM chat. Simply send the System Prompts from references/prompts.md to the current platform's LLM.

Input

Param	Description	Required
book_name	Book title	Yes
author_name	Author name	Yes
ip_name	Account name (bottom watermark on cover)	No, hidden by default

Environment Setup

Ensure these Python dependencies are installed before running:

bash

pip install edge-tts imageio-ffmpeg pillow

Scripts auto-install missing dependencies on first run, but pre-installing avoids interruptions.

ffmpeg is provided automatically by the imageio-ffmpeg package — no separate system ffmpeg needed.

TTS Engine Config (optional)

Engine	Credential	Timestamps	Notes
Volcano Engine TTS (default)	VOLC_TTS_API_KEY	Estimated from audio duration	Doubao Speech 2.0, best English naturalness, commercial use, get API Key from Volcano console
edge-tts (fallback)	none	Native WordBoundary	Microsoft free TTS, works out of the box, auto-fallback when no credential

Set the Volcano credential:

bash

export VOLC_TTS_API_KEY="your-api-key"

When VOLC_TTS_API_KEY is not set, the script auto-uses edge-tts — fully functional. Default English voice: en-US-AriaNeural (edge-tts) / en_us_amy (Volcano). See scripts/generate_audio.py --list-voices.

Full Workflow (5 stages)

Stage 1: Generate the review script

Goal: From book title + author, use the LLM to produce a ~1000-word, 3-minute video script.

Steps:

1. Use the platform's web search tool to find real book info (summary, interpretations, publication year).
2. Use the system prompt (see references/prompts.md §1) and ask the LLM to output JSON:

json

{
  "book_name": "...",
  "author_name": "...",
  "year": "yyyy-MM",
  "content": "1000+ word book review script (hook intro + core content + key insights)",
  "category": "Book category"
}

Notes:

• Script should fit ~3 minutes of spoken narration (~450-650 words).
• The opening hook must be highly compelling.
• Source info via search to ensure accuracy.

Stage 2: Generate the storyboard

Goal: Split the review script into 8-50 storyboard shots, each with subtitle text, visual description, and an AI image prompt.

Steps: Use the system prompt (references/prompts.md §2), feed in Stage 1's content, output:

json

{
  "list": [
    {
      "story_name": "Shot name",
      "desc": "Visual description",
      "cap": "Subtitle text (one sentence)",
      "desc_promopt": "Image generation prompt (English)"
    }
  ],
  "keywords": ["keyword1", "keyword2"]
}

Then insert the intro shot at the beginning of list (mirrors the original workflow node 150774):

python

list.insert(0, {
    "story_name": "Intro",
    "desc": "Read one book every day",
    "cap": f"Read a book in 3 minutes. Today we're reading {book_name} by {author_name}.",
    "desc_promopt": "A person reading a book, flat illustration style"
})

Style constraints: flat illustration, cartoon character with clean lines, flat symbolic background, soft bright low-saturation palette.

Stage 3: Generate the title progress bar

Goal: Split the script into 4 sections, each with a title of ≤6 words, shown as a progress bar at the top of the video.

Steps: Use the system prompt (references/prompts.md §3), output 4 titles (title1-title4).

Usage: The 4 titles are passed to compose_video.py via the chapter_titles field in segments.json, rendered as a top progress bar (current section highlighted in orange + bottom progress line). Also need segment_chapters specifying each shot's section index (0-3); auto-distributed evenly if omitted.

Example: ["Introduction", "Core Ideas", "Practical Tips", "Conclusion"]

Stage 4: Generate assets (in parallel)

This stage generates all assets the video needs:

4a. AI illustration generation

For each shot's desc_promopt, call the image generation tool. Defaults to WorkBuddy's built-in ImageGen (Tencent Hunyuan); switch via IMAGE_API env var.

Unified style params (from the original workflow's image node):

• Size: 1024x768
• Style: flat illustration
• Protagonist top #FF7F72, pants #243139
• 30% transparent glass effect background
• Negative prompt: none

Plan selection & invocation:

Plan	IMAGE_API value	Invocation	Config needed
🏠 Tencent Hunyuan (default)	unset or imagegen	WorkBuddy built-in ImageGen tool	none
🏔️ Volcano Seedream	volcengine	python3 scripts/generate_image.py --api volcengine	ARK_API_KEY (recommended) or VOLCENGINE_AK + VOLCENGINE_SK
🤖 Google Gemini	gemini	python3 scripts/generate_image.py --api gemini	GEMINI_API_KEY
✨ Agnes AI	agnes	python3 scripts/generate_image.py --api agnes	AGNES_API_KEY

Execution logic:

1. If running in WorkBuddy and IMAGE_API is unset: call the ImageGen tool (DeferExecuteTool) directly, prompt = desc_promopt, size = "1024x768".
2. If IMAGE_API=volcengine|gemini|agnes: call scripts/generate_image.py, auto-install deps and generate via API.
3. If running on a CLI platform (Codex CLI, etc.): default scripts/generate_image.py --api gemini.

Generated images are named scene_000.png ~ scene_NNN.png, stored in output/{book_name}/images/.

4b. TTS voice synthesis

For each shot's cap (subtitle text), use the TTS engine to generate MP3 audio plus word-level timestamps (saved as a sibling .words.json for precise per-line subtitle sync in Stage 5).

Dual-engine architecture:

• Volcano Engine TTS (default): V1 API + X-Api-Key auth, Doubao Speech 2.0 voice, best English naturalness, commercial use, requires VOLC_TTS_API_KEY.
• edge-tts (fallback): free, no config, native WordBoundary word timestamps, auto-fallback when no Volcano credential.

Default voices:

• Volcano: en_us_amy (English female, warm — good for book narration)
• edge-tts: en-US-AriaNeural (Aria, female)

List all available voices: python3 scripts/generate_audio.py --list-voices

Run (all platforms, auto engine selection):

bash

python3 scripts/generate_audio.py --text "<subtitle>" --output "audio_001.mp3"

Specify engine or voice:

bash

# Force Volcano
python3 scripts/generate_audio.py --text "<subtitle>" --output "audio_001.mp3" --engine volcano --voice en_us_amy

# Force edge-tts
python3 scripts/generate_audio.py --text "<subtitle>" --output "audio_001.mp3" --engine edge --voice en-US-AriaNeural

Batch mode:

bash

python3 scripts/generate_audio.py --batch captions.json --output-dir audio/ --voice "en-US-AriaNeural"

4c. Opening cover image

Generate a dedicated cover image (1920x1080) for the first frame, with book title, author, and brand text.

Steps: run python3 scripts/generate_cover.py

bash

# Use first storyboard image as blurred background (recommended, consistent style)
python3 scripts/generate_cover.py \
  --book-name "Atomic Habits" \
  --author "James Clear" \
  --output output/Atomic_Habits/images/cover.png \
  --bg output/Atomic_Habits/images/scene_000.png

# No background image, use deep-blue gradient
python3 scripts/generate_cover.py \
  --book-name "Atomic Habits" \
  --author "James Clear" \
  --output output/Atomic_Habits/images/cover.png

Cover layout:

• Top: "3-MINUTE BOOK DIGEST" brand text + orange divider line (#FF7F72, matches protagonist's top)
• Middle: book title (large, auto-wrapped, centered, 80pt)
• Lower-middle: author name (42pt)
• Bottom: account-name watermark (optional, via --ip-name; hidden if omitted)

Fonts: auto-detects a system English font (Windows: Arial / macOS: Helvetica / Linux: DejaVu Sans) — no manual change needed.

The cover image is specified via the cover field in segments.json at Stage 5; it shows for cover_duration seconds (default 5) before the first shot, then switches back to the original shot image (narration continues uninterrupted).

json

{
  "output": "output/BookName_3min_digest.mp4",
  "cover": "output/BookName/images/cover.png",
  "chapter_titles": ["Introduction", "Core Ideas", "Practical Tips", "Conclusion"],
  "segment_chapters": [0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3],
  "keywords": ["Atomic Habits", "tiny changes", "compound effect"],
  "bgm": "assets/bgm_reading.mp3",
  "bgm_volume": 0.15,
  "transition_sound": "assets/transition_page_flip.mp3",
  "transition_interval": 3,
  "transition_volume": 0.3,
  "segments": [
    {"image": "images/scene_000.png", "audio": "audio/audio_000.mp3", "caption": "Subtitle text"},
    ...
  ]
}

• cover: optional, cover image path, shown for cover_duration seconds before the first shot
• cover_duration: optional, cover display time (seconds), default 5.0
• chapter_titles: optional, section titles for the top progress bar
• segment_chapters: optional, each shot's section index (0-based); auto-distributed evenly if omitted
• keywords: optional, keyword list; matched keywords in subtitles show in orange
• bgm: optional, background music path; auto-looks for assets/bgm_reading.mp3; pass empty string to disable
• bgm_volume: optional, BGM volume (0.0-1.0), default 0.15
• transition_sound: optional, transition sound path; auto-looks for assets/transition_page_flip.mp3; pass empty string to disable
• transition_interval: optional, add a transition sound every N shots, default 3
• transition_volume: optional, transition sound volume (0.0-1.0), default 0.3

Stage 5: Video composition

Goal: Compose all assets into the final MP4 video.

Steps: run python3 scripts/compose_video.py, which:

1. Computes each shot's duration (based on TTS audio duration + gap interval)
2. Scales/crops images to 1920x1080 (16:9)
3. Adds a 0.3s fade in/out transition (dip-to-black) per shot
4. Uses ffmpeg to compose image + audio into video clips
5. Progress bar overlay: top shows section titles (current section orange highlight + filled dot, others gray + hollow dot), bottom progress line shows overall playback progress
6. Generates per-line single-line ASS subtitles (precise sync via TTS word timestamps, split into short single lines by punctuation + word count, white bold text + black outline, orange keyword highlight, fade-in + slide-up entrance / fade-out exit animation)
7. Concatenates all clips into the full video
8. Audio mix: blends background music (BGM) at low volume throughout + a page-flip transition sound every 3 shots at the boundary

Subtitle params (corresponds to original workflow add_captions_1 node):

• Font color: white (#FFFFFF)
• Keyword highlight: orange (#FF7F72), via ASS inline color tag {\c&H727FFF&}
• Outline color: black (#000000)
• Font size: 64pt (bold)
• Position: bottom center (MarginV=30)
• Font: auto-detected system English font (Windows: Arial / macOS: Helvetica / Linux: DejaVu Sans), no manual change
• Subtitle format: ASS (Advanced SubStation Alpha), supports inline color + animation tags
• Per-line single-line display: uses TTS word timestamps to split long subtitles into short single lines synced to speech
• Entrance animation: fade in 200ms + slide up 20px from below (ASS \fad + \move)
• Exit animation: fade out 150ms (ASS \fad)

Progress bar params:

• Position: top of frame (80px height)
• Background: semi-transparent dark (alpha=130)
• Current section: orange (#FF7F72) text + filled dot
• Other sections: gray (#AAAAAA) text + hollow dot
• Bottom progress line: orange fill + dark-gray base, shows overall progress
• Font: auto-detected system English font, 44pt

Dynamic effect params:

• Transition fade duration: 0.3s (auto-halved for shots shorter than 0.6s)

Audio mix params:

• Background music: assets/bgm_reading.mp3 (3:56 reading BGM, auto-loop), volume 0.15
• Transition sound: assets/transition_page_flip.mp3 (0.71s page-flip), triggered 0.5s before the end of every 3rd shot, volume 0.3
• Mix method: ffmpeg amix filter, BGM loop + transition sounds overlaid at time points via adelay

📌 Audio assets are optional: Because SkillHub does not support uploading .mp3 files, audio assets are not bundled. If these files are missing from assets/, the video still generates fine (just without BGM and transition sounds). To add them, see assets/README.md.

Output

Final output: output/{book_name}_3min_digest.mp4

Cross-Platform Installation

WorkBuddy

The skill is installed at ~/.workbuddy/skills/book-video-generator-en/, ready to use.

OpenClaw

bash

# Copy to OpenClaw skills dir
cp -r ~/.workbuddy/skills/book-video-generator-en ~/.openclaw/skills/

# Or install via ClawHub (if published)
openclaw skills install book-video-generator-en

Codex CLI

bash

# 1. Enable Skills (if not yet)
echo '[features]\nskills = true' >> ~/.codex/config.toml

# 2. Copy skill dir
cp -r ~/.workbuddy/skills/book-video-generator-en ~/.codex/skills/

# 3. Restart Codex CLI
# 4. Type /skills to confirm the skill loaded

TRAE Work

text

1. Open TRAE Work → Rules & Skills → Skills → Create → Import files
2. Upload SKILL.md
3. Copy scripts/ and references/ into the skill dir

Original Workflow Reference

The original Coze workflow file is at references/workflow-original.yaml, a full chain of 30+ nodes:

text

Start → LLM (DeepSeek V3.2) generate review → Title summary → Storyboard visual description
→ Code concat intro → Batch image gen + cutout → TTS voice synthesis
→ Create Jianying draft → Batch add images/subtitles/audio → Save draft → End

The original flow relied on the Jianying assistant plugin (video composition core) and Coze's built-in image generation + cutout and TTS plugins. This Skill replaces them with ffmpeg, edge-tts, and the platform's image generation tools.

Quick Usage Example

User says: "Generate a 3-minute digest video of Atomic Habits by James Clear"

Execution flow:

1. Search "Atomic Habits James Clear summary review"
2. Generate review script with Stage 1 prompt
3. Generate storyboard with Stage 2 prompt
4. Generate title progress bar with Stage 3 prompt
5. For each shot: image generation tool creates illustration + TTS generates English narration (Volcano default / edge-tts fallback)
6. compose_video.py composes the final video
7. Output output/Atomic_Habits_3min_digest.mp4

Resource Files

• references/prompts.md — all LLM prompt sources (platform-independent, reusable)
• references/workflow-original.yaml — original Coze workflow (full YAML backup)
• scripts/compose_video.py — video composition script (pure Python + ffmpeg, cross-platform, with fade transitions + auto font detection + cover support + per-line single-line ASS subtitles with precise sync + keyword highlight + entrance/exit animations + top progress bar + BGM mix + transition sounds)
• scripts/generate_audio.py — TTS voice script (pure Python, dual engine: Volcano TTS V1 API + X-Api-Key default / edge-tts fallback, with word timestamps, auto engine switch by credential)
• scripts/generate_cover.py — cover image script (pure Python + Pillow, auto-wrap + blurred background + auto font detection)
• scripts/generate_image.py — image generation script (Volcano / Gemini / Agnes / OpenAI / Stability / local SD)