Imported from speechlab0210/video-production-skill (SKILL.md). Install upstream with npx skills add speechlab0210/video-production-skill. Copyright stays with the author.

Video Production Skill

End-to-end pipeline for producing AI-narrated educational videos with slides. Distilled from producing 40+ real videos for an AI-run YouTube channel (蝦說 AI), including every mistake we only made once.

Who this is for: an AI agent (Claude, Codex, Gemini, or any coding agent) asked to produce a narrated slide video. A human can follow it too.

Quick Start

0. Read references/teaching-style.md + references/narration-style.md (content quality rules)
1. Create project directory and cd into it; copy config.json from references/config-example.json and fill it in
2. Write narration.json (the script — one string per slide)
3. Generate slides (Path A: gpt-image-2 hand-drawn / Path B: HTML + Playwright screenshot)
4. Visually inspect every slide PNG (wrong characters / clipping / unreadable fonts)
5. TTS narration → MP3 (with built-in ASR verification)   → node scripts/tts_with_asr.js
6. FFmpeg assemble → video.mp4                            → node scripts/assemble.js
7. Quality check (bitrate + frame extraction + visual)
8. Subtitles → SRT (+ optional burn-in)                   → node scripts/gen_subtitles.js
9. Cover image → thumbnail                                → python scripts/cover_gen.py
10. Upload wherever you publish; verify the thumbnail and visibility after upload

Requirements

• Node.js ≥ 18 (scripts use only built-ins + playwright for HTML screenshots)
• Python ≥ 3.9 (only for gpt-image-2 slide/cover generation and optional rescore)
• FFmpeg + FFprobe on PATH (or set explicit paths in config.json)
• API keys (environment variables, or a .env file in the project directory):
  • ELEVENLABS_API_KEY — TTS
  • OPENAI_API_KEY — Whisper ASR verification + gpt-image-2 slides/cover
• A TTS voice: set tts.voiceId in config.json (any voice from your ElevenLabs voice library — a premade voice works out of the box; a cloned voice makes it yours).

⚠️ Never hardcode API keys in scripts and never commit them to git. One of our keys was auto-revoked from a public repo push and 32 videos rendered silent. Environment variables only.

Configuration

Create config.json in your project directory (start from references/config-example.json):

{
  "tts": {
    "provider": "elevenlabs",
    "voiceId": "YOUR_ELEVENLABS_VOICE_ID",
    "model": "eleven_multilingual_v2",
    "maxRetries": 5
  },
  "asr": {
    "provider": "openai",
    "model": "whisper-1",
    "language": "zh",
    "passThreshold": 0.85
  },
  "video": {
    "width": 1920,
    "height": 1080,
    "audioBitrate": "192k",
    "slidePadding": 1.0
  },
  "branding": {
    "watermark": "🎬 Your Channel Name"
  },
  "ffmpeg": "ffmpeg",
  "ffprobe": "ffprobe"
}

Checklist (mandatory, do not skip steps)

□ 0. Read references/teaching-style.md — content/teaching rules (pre-flight constitution)
□ 1. Write narration.json
□ 1.5 ⭐ ALIGNMENT CHECK: narration entries count == slide count. MUST be equal!
     If narration splits a topic across 2 entries, there must be 2 separate slides.
     Mismatch = audio/visual desync for every slide after the mismatch point.
□ 2. Generate slides (Path A or B below)
□ 3. ⭐ Visual check: open every slide PNG with an image tool
     (wrong/garbled characters? clipped edges? readable on a phone?)
□ 4. TTS synthesis + ⭐ ASR verification (similarity ≥ 0.85, never skip)
□ 5. FFmpeg assemble → video.mp4
□ 6. ⭐ Quality check: ffprobe audio bitrate + extract a frame + visual verify
□ 7. Subtitles → subtitles_aligned.srt (external track and/or burn-in)
□ 8. ⭐ Cover image → thumbnail (16:9; pad, don't crop)
□ 9. Upload (start unlisted/private, review, then publish)
□ 10. ⭐ After upload: verify the thumbnail actually shows YOUR cover, not an auto frame

The ⭐ steps are non-negotiable. Each one exists because skipping it once ruined an entire batch of videos.

🔴 The cover is part of the video, not an optional extra. We once shipped a video where every step was done except the cover — it went live with a default gray frame. "No cover = not shipped."

Step 1: Script (narration.json)

A JSON array of strings, one per slide (CRITICAL: array length MUST equal slide count):

[
  "開場白，吸引注意力...",
  "第一個重點，配合比喻...",
  "第二個重點...",
  "結尾，call to action..."
]

Writing guidelines

• 80–150 characters per slide (Chinese). Too short = rushed; too long = TTS breaks.
• Conversational tone: metaphors, rhetorical questions, humor.
• One concept per slide. Don't cram.
• Story > Information. Viewers remember stories, not bullet lists.
• Full teaching methodology (audience empathy, glossing jargon, honest attribution, first-person endings…): references/teaching-style.md. Voice & TTS-safe writing: references/narration-style.md.

TTS-friendly writing (important!)

TTS engines mispronounce things. Prevent it at the script stage:

Punctuation and pacing: our TTS script strips punctuation before synthesis (each punctuation mark becomes a pause in Chinese TTS — dense commas produce machine-gun narration). Write narration that flows in breath-groups of ~8–22 characters; put commas only at real breath points. Subtitles still use the original punctuated text.

Step 2: Slides

Two first-class paths. Pick ONE per video (Path A is our channel default; Path B has no image-API dependency).

Path A — gpt-image-2 hand-drawn teaching style (scripts/slides_gen.py)

Full-bleed AI-generated slides in a "professor's hand-drawn lecture notes" style: white background, bold black CJK title top-left with underline, thin black arrows, lots of whitespace, a small mascot in the corner, stick figures only (no real faces).

1. Write one Chinese prompt per slide into slides_prompts.json (array of strings). Wrap every string that must appear on the slide in 「」. Always end the shared style block with: 「所有中文字必須完全正確、清楚可讀、不可有亂碼或錯字。數字要正確。」
2. python scripts/slides_gen.py → generates slides_raw/slide_NN.png (1536×1024). Batch ≤4–5 concurrent lanes (~120s per image). Do not run Whisper at the same time — concurrent heavy API calls starve each other.
3. Visually inspect every image (garbled characters / wrong or invented numbers / typos → regenerate just that slide; HTTP 502 → just retry that slide). gpt-image-2 WILL invent numbers to fill tables unless your prompt explicitly says 「畫面只能出現 X 這幾個數字，其他留空」.
4. node scripts/pad_and_burn.js pad → 1536×1024 scaled to 1410×940, padded onto a 1920×1080 white canvas with a 140px bottom band reserved for subtitles.

Path B — HTML slides + Playwright screenshot (scripts/screenshot.js)

Create one HTML file per slide: slides/slide_01.html, slide_02.html, … (template: references/slide-template.html), then node scripts/screenshot.js.

Design rules (hard-won; mobile viewers are the majority):

• Resolution = video.width × video.height (default 1920×1080).
• Fill 80%+ of the canvas — padding: 60-80px, flexbox centering, no floating cards.
• Every element on the slide must be mentioned in the narration (and vice versa).
• 🔴 Never use <24px body text. Fewer points in bigger font > more points in tiny font.

Path C — Node Canvas fallback (scripts/generate_slides.js)

No browser, no image API: renders simple title+bullets slides from slides.json. Use only when Playwright and the image API are both unavailable.

Step 3: TTS + ASR Verification (scripts/tts_with_asr.js)

node scripts/tts_with_asr.js [project_dir]

Reads narration.json, synthesizes each entry via ElevenLabs, saves audio/slide_XX.mp3, then verifies every clip with Whisper ASR:

1. Transcribe the generated audio
2. Compute character-overlap similarity vs the original text
3. ≥ 0.85 = PASS (target ≥0.90 for important videos)
4. < 0.85 = FAIL → adjust wording and retry (up to tts.maxRetries)

Adjustment rules: swap synonyms / split long sentences / write numbers in Chinese — but never change the meaning, never drop information.

Don't chase 0.85 forever — verify the words, ship on redundancy. Whisper produces false alarms on Chinese: same-sound different-tone homophones, digits vs 中文數字, Simplified output vs Traditional script. If the ASR "errors" are homophones and every key word/number comes through, the audio is correct — keep the best attempt. The slide shows the number visually and the subtitle uses the original text, so the viewer has triple redundancy. For digit-heavy narration, run python scripts/rescore.py — it strips numerals and compares toneless-pinyin multisets (≥0.90 = pass), which kills most false failures. A real defect = ASR gets the SAME wrong word consistently across multiple synth attempts (that's the TTS mispronouncing, not Whisper mishearing → rewrite that word).

Step 4: FFmpeg Assembly (scripts/assemble.js)

node scripts/assemble.js [project_dir]

Pairs each slides/slide_XX.png with audio/slide_XX.mp3, creates per-slide clips, concatenates into video.mp4.

Key flags (all already in the script):

• -b:a 192k — without it audio can silently render at 2kbps (present but inaudible)
• -tune stillimage — much smaller files for slide video
• -pix_fmt yuv420p — plays everywhere
• -movflags +faststart — video streams/plays inline instead of "link won't open"

🔴 Mixing clips from different sources (e.g. your narrated slides + a real screen recording)? Do NOT use the concat demuxer with -c copy — strict players (Windows Media Player) refuse to play the result. Re-encode through the concat FILTER into one continuous stream, normalize every input (scale, fps=30, format=yuv420p, 44100 stereo audio), re-encode audio, add faststart, and align loudness (loudnorm=I=-16:TP=-1.5:LRA=11). Details in references/lessons-learned.md.

🔴 Mixing two TTS providers in one video? Their sample rates differ (ElevenLabs 44100 Hz vs OpenAI TTS 24000 Hz). Resample everything to 44100 BEFORE assembly or some players choke exactly at the voice-switch point.

Step 5: Quality Check (three checks, all required)

5a. Audio bitrate

ffprobe -v error -show_entries stream=codec_type,codec_name,bit_rate -of default video.mp4

✅ ~130–192 kbps AAC ❌ ~2 kbps or N/A = TTS failed silently

5b. Visual verification

ffmpeg -ss 3 -i video.mp4 -frames:v 1 -update 1 verify.png

Compare verify.png against slides/slide_01.png with an image tool. Content must match.

5c. Font size check

Inspect verify.png: is every text element readable at mobile size?

Step 6: Subtitles (scripts/gen_subtitles.js)

node scripts/gen_subtitles.js [project_dir]

Produces subtitles_aligned.srt: Whisper word timestamps for timing, original narration text for display (never use ASR output as subtitle text — it mishears). Line breaks are width-aware (CJK=1, Latin=0.5, ≤16 full-width per line) and never cut inside an English word.

⚠️ The #1 subtitle bug: drift from assuming clip duration = audio + padding. FFmpeg's -shortest truncates the padding, so real clip duration ≈ audio duration. The script therefore reads ACTUAL durations from temp/clip_XX.mp4 with ffprobe. If you ever hand-roll offsets: never offset += audioDur + padding — that drifts +1s per slide and by slide 16 subtitles are 15 seconds late.

Ship options:

• External SRT track (recommended when your platform supports it — viewers can toggle, nothing covers the slides)
• Burn-in: node scripts/pad_and_burn.js burn (Path A white-band layout: dark text FontSize 30 / MarginV 30 sits inside the reserved 140px band) For full-bleed dark HTML slides use FontSize=14–18, MarginV=6–12, BorderStyle=3 instead.

For rough/cloned voices where Whisper timestamps collapse, there is a geometry-based fallback (ffmpeg silencedetect + character-width proportional alignment) — see references/lessons-learned.md § subtitle alignment.

Step 7: Cover / Thumbnail (scripts/cover_gen.py)

python scripts/cover_gen.py "一張 YouTube 影片封面，橫式 16:9，白底手繪教學風…主標題用超大粗黑體繁體中文寫「你的標題」…"

• Generate at 1536×1024, then pad — do not crop — to 1280×720: crop cuts off the top of your title. scale=1080:720 + pad=1280:720:100:0:color=white (white side bars are invisible on a white cover).
• Target ≤2MB for YouTube.
• Same prompt hygiene as slides: quote exact text in 「」, forbid invented numbers, visually verify before shipping.

Step 8: Upload

Platform-specific; do it however you normally operate (browser automation, manual, CLI). Regardless of method, these rules survived contact with reality:

1. Upload as unlisted/private first, review the actual playback, then publish.
2. Verify the thumbnail after upload — the preview must show YOUR cover, not an auto-selected frame.
3. Re-check title/description/language settings after any dialog reopens (some upload wizards silently reset radio buttons).
4. If you used an external SRT: upload it as a caption track ("with timing"), publish the track, then verify captions actually appear on the watch page.
5. Disclose AI authorship in the description if the narration/production is AI-made.

Output Structure

my-video-project/
├── config.json              ← project config (from references/config-example.json)
├── narration.json           ← script text per slide
├── slides_prompts.json      ← (Path A) one gpt-image-2 prompt per slide
├── slides_raw/              ← (Path A) raw 1536×1024 generations
├── slides/                  ← final 1920×1080 PNGs (padded or screenshotted)
│   ├── slide_01.png
│   └── ...
├── audio/
│   ├── slide_01.mp3
│   └── ...
├── temp/                    ← per-slide clips + whisper word caches
├── video.mp4                ← assembled (no subtitles)
├── subtitles_aligned.srt    ← aligned subtitle track
├── video_sub.mp4            ← (optional) burned-in version
└── thumbnail.jpg            ← cover, 1280×720

Scripts Reference

All scripts take the project directory as an optional first argument (default: CWD).

Sub-agents

If you spawn a sub-agent to produce a video, the task prompt must explicitly say "read the video-production SKILL.md first". Sub-agents that aren't told skip the pipeline and reinvent (worse) wheels. Re-read the skill fresh each session — it evolves.

When things break

references/lessons-learned.md is the accident report archive: path bugs, silent audio, subtitle drift, moderation blocks on image generation, players that refuse concat-copied files, Whisper false alarms, batch API contention, and more. Read it before your first video; grep it when something looks weird — the odds are good we already hit it.