anything2explainer
English | 简体中文
Topic in, narrated explainer video out. anything2explainer is a Claude Code / Codex skill that turns any topic into a black-canvas motion-graphics explainer video with TTS voiceover, subtitles and a chapter progress bar, in Chinese or English. Every frame is drawn in code with Remotion (React + TypeScript). No stock footage, no generative video model, no frames lifted from anyone else's work.
It is not a CLI. What ships here is the whole method an AI coding agent needs to finish the film: a compilable Remotion template, a primitives and lighting library, tooling for voiceover / storyboard / rendering / quantitative QC, written style and motion specs, a multi-agent division-of-labour protocol, and one complete reference film as the quality bar.
English cut — RAG & Knowledge Bases, 5′02″, 44 lines / 785 words, voiced by kokoro-82m am_liam at natural speed:
https://github.com/user-attachments/assets/e2771c68-a28c-4459-ac5a-a5b685181eeb
Chinese cut — RAG 与知识库 v2, 4′54″, 44 lines / 1490 characters, dot-field backdrop (bg: 'dots'), voiced through the bring-your-own-TTS path (Volcengine TTS 2.0 + forced alignment):
https://github.com/user-attachments/assets/5c213990-cbba-439e-8371-fbb3aa348e05
Both cuts share one storyboard and 44 shots; the English cut re-times every shot to the English voiceover. The full paper trail of the original Chinese cut (4′35″, star-field backdrop, 8 build agents in parallel for 40 minutes, two QC rounds) lives in examples/rag/ (research → narration → storyboard → shot source → QC reports → delivery notes); rendered frames are in examples/rag/frames/.
What it does
- Input: a topic ("explain vector databases"), or an article / document you want turned into a video. You also pick the length and the language.
- Output: a 1280×720 H.264 MP4 with synchronized voiceover, word-boundary-aligned subtitles, chapter cards, a top HUD and a bottom chapter progress bar, plus the full paper trail (research doc with sources, narration, storyboard, per-shot source code, QC reports).
- How: the agent researches the topic with sources, writes the narration, generates the voiceover and frame-accurate timeline, storyboards every shot, then dispatches parallel build agents that write one Remotion component per shot. QC agents review the rendered frames against written criteria before delivery.
- Time: roughly 1 to 3 hours of wall clock depending on length, most of it agents building shots in parallel. You are consulted at exactly four checkpoints.
Output spec
| Frame / rate | 1280×720 @ 30fps, H.264 |
| Length | your call (see table below); 2–8 minutes all work |
| Language | Chinese or English (lang in src/config.ts); typography, subtitle budgets and TTS switch with it |
| Look | black canvas with one of two backdrops, star field + fog gradient or dot-field wave (bg in src/config.ts; the dot-field wave is ported from video-talkcraft); white line art + purple accents; ultra-bold headline type |
| Persistent layers | 44px white-on-black-stroke subtitles, bottom chapter progress bar, top capsule HUD, optional pipeline rail, built by Anything2Explainer skill end credit (builtBy, set to '' to drop) |
| Voiceover | Chinese: edge-tts zh-CN-YunxiNeural (Yunxi, male, unmodified rate ≈5.5 chars/s). English: kokoro-82m am_liam (Liam, male). Or bring your own TTS / finished audio |
Length drives how much ground the film covers, and the size of the whole pipeline:
| Length | Chinese chars | English words | Lines / shots | Build agents | Wall clock | Disk |
|---|---|---|---|---|---|---|
| 2–3 min | 650–880 | 280–420 | 24–32 | 4–6 | ≈1 h | ≈2 GB |
| 3–5 min (reference tier) | 1100–1400 | 420–700 | 40–50 | 8 | ≈2 h | ≈2 GB |
| 5–8 min | 1650–2200 | 700–1150 | 60–80 | 10–14 | ≈2–3 h | ≈3 GB |
Chapter count follows the content, within limits set by length: under 3 minutes use a single chapter (no chapter cards), 3–5 minutes 3–4 chapters of at least 60 s each, 5–8 minutes 4–6. The progress bar splits evenly across however many chapters the narration declares. A blank line in the narration marks a paragraph, which is also one shot: sentences inside a paragraph are separated by 10 frames, paragraph ends by 30, so the pause lands where the picture changes and every shot holds 1–1.5 s after its last element lands. The finished video runs 5–8% longer than the raw speech by design.
Install
git clone https://github.com/Vincentwei1021/anything2explainer.git
ln -s "$PWD/anything2explainer" ~/.claude/skills/anything2explainer # Claude Code
ln -s "$PWD/anything2explainer" ~/.codex/skills/anything2explainer # Codex
Dependencies:
# Node ≥18 (the template's npm install pulls remotion 4.0.507 / react 19)
brew install ffmpeg # frame extraction / transcoding, required
python3 -m venv ~/.venvs/a2e && source ~/.venvs/a2e/bin/activate
pip install 'edge-tts==7.2.8' numpy pillow scipy # pin edge-tts: it tracks a Microsoft endpoint and breaks across upgrades (7.2.0+ needs word boundaries requested explicitly; the script does)
# only needed for English narration (kokoro-82m runs locally)
pip install kokoro soundfile && brew install espeak-ng
scipy is only used by the QC script frame_metrics.py. The shell scripts are zsh + Python 3, developed and verified on macOS; Linux should work, Windows is untested.
Linux / Raspberry Pi (ARM)
Verified on a Raspberry Pi 5 (ARM64, Python 3.13). Three things differ from macOS:
sudo apt install zsh espeak-ng # scripts are #!/bin/zsh; espeak-ng for kokoro/piper G2P
# Remotion has no linux-arm64 headless browser → point it at system Chromium:
sudo apt install chromium # or chromium-browser
export REMOTION_BROWSER_EXECUTABLE=/usr/bin/chromium # read by template/remotion.config.ts (no-op on macOS)
TTS on Linux/ARM. kokoro (the default English engine) is hard to install on ARM/Python 3.13 (it pins an old numpy and pulls spaCy → blis, which lack aarch64 wheels). Two local engines that install cleanly instead — pass one via TTS_ENGINE:
# kokoro_onnx — natural voice, onnxruntime (no torch/spaCy). Download model + voices from
# github.com/thewh1teagle/kokoro-onnx releases (kokoro-v1.0.onnx, voices-v1.0.bin)
pip install kokoro-onnx
TTS_ENGINE=kokoro_onnx KOKORO_ONNX_MODEL=…/kokoro-v1.0.onnx KOKORO_ONNX_VOICES=…/voices-v1.0.bin \
KOKORO_ONNX_VOICE=am_michael python3 scripts/tts_build.py
# piper — fastest local, robotic; a Pi-native fallback. Voice .onnx from github.com/rhasspy/piper
pip install piper-tts
TTS_ENGINE=piper PIPER_MODEL=…/en_US-ryan-medium.onnx python3 scripts/tts_build.py
The edge engine (natural, free, word-boundary timing) also works on Linux and needs no local model — it's a cloud call to Microsoft: TTS_ENGINE=edge VOICE=en-US-AndrewNeural python3 scripts/tts_build.py.
Usage
In Claude Code or Codex, just say what you want. The skill triggers itself:
Make me an explainer video about vector databases.
讲一下向量数据库，做成一条讲解视频
It then walks the 9 stages in SKILL.md:
- Scaffold the Remotion project from the template.
- Research (1 agent): a sourced research doc with a list of numbers and analogies, every item with a URL.
- Narration & timeline: the script, then TTS voiceover with per-word boundaries turned into a frame-accurate timeline and subtitle table.
- Storyboard: one line per shot with frame range, beat, visuals, motion, hero element and lighting.
- Overlays & primitives: title, chapter cards, HUD, pipeline rail, plus 2–5 topic-specific icons.
- Pilot (1 agent): the first shot group, then a 30-second cut for you to judge the look.
- Parallel build: the remaining groups, 5–7 shots per agent, each writing pure-function Remotion components.
- Render the full film and run quantitative frame metrics.
- QC & fixes: one QC agent per chapter, fix agents per group, re-verification, then delivery notes.
You can also drive the template by hand:
template/scripts/new_project.sh ~/work/my-video myslug
cd ~/work/my-video
# 1. research/调研.md 2. script/narration.txt → python3 scripts/tts_build.py
# 3. script/storyboard_src.md → python3 scripts/render_storyboard.py 4. edit src/config.ts
# 5. src/shots/G1..Gn 6. scripts/preview.sh 30 (first 30 seconds)
# 7. VER=v1 scripts/render.sh + python3 scripts/frame_metrics.py 8. QC → fix → v2/v3
Four checkpoints
The run stops and waits for you at exactly four points instead of ploughing through (details in SKILL.md):
- Length and language: before the script is written. Length decides the line count, shot count and how many agents run in parallel, i.e. how much the film can actually cover; language flips
langinsrc/config.ts, which drives typography, subtitle budgets and the default voice. - Narration sign-off: before voiceover. Once locked, frame numbers are hard-coded into every shot; changing one word re-times the whole film. This is the cheapest place to intervene.
- Voiceover: before TTS runs you get asked whether you have a preferred engine. If not, defaults apply (edge-tts Yunxi for Chinese, kokoro-82m Liam for English). You can also hand over finished audio and fill the per-line timeline yourself.
- First 30 seconds: only the first bui