clip-hand-skill

name	clip-hand-skill
version	2.0.0
description	Expert knowledge for AI video clipping — yt-dlp downloading, whisper transcription, SRT generation, and ffmpeg processing
runtime	prompt_only

Video Clipping Expert Knowledge

Cross-Platform Notes

All tools (ffmpeg, ffprobe, yt-dlp, whisper) use identical CLI flags on Windows, macOS, and Linux. The differences are only in shell syntax:

Feature	macOS / Linux	Windows (cmd.exe)
Suppress stderr	2>/dev/null	2>NUL
Filter output	| grep pattern	| findstr pattern
Delete files	rm file1 file2	del file1 file2
Null output device	-f null -	-f null - (same)
ffmpeg subtitle paths	subtitles=clip.srt	subtitles=clip.srt (relative OK, absolute needs C\\:/path)

IMPORTANT: ffmpeg filter paths (-vf "subtitles=...") always need forward slashes. On Windows with absolute paths, escape the colon: subtitles=C\\:/Users/me/clip.srt

Prefer using file_write tool for creating SRT/text files instead of shell echo/heredoc.

---

yt-dlp Reference

Download with Format Selection

# Best video up to 1080p + best audio, merged
yt-dlp -f "bv[height<=1080]+ba/b[height<=1080]" --restrict-filenames -o "source.%(ext)s" "URL"

# 720p max (smaller, faster)
yt-dlp -f "bv[height<=720]+ba/b[height<=720]" --restrict-filenames -o "source.%(ext)s" "URL"

# Audio only (for transcription-only workflows)
yt-dlp -x --audio-format wav --restrict-filenames -o "audio.%(ext)s" "URL"

Metadata Inspection

# Get full metadata as JSON (duration, title, chapters, available subs)
yt-dlp --dump-json "URL"

# Key fields: duration, title, description, chapters, subtitles, automatic_captions

YouTube Auto-Subtitles

# Download auto-generated subtitles in json3 format (word-level timing)
yt-dlp --write-auto-subs --sub-lang en --sub-format json3 --skip-download --restrict-filenames -o "source" "URL"

# Download manual subtitles if available
yt-dlp --write-subs --sub-lang en --sub-format srt --skip-download --restrict-filenames -o "source" "URL"

# List available subtitle languages
yt-dlp --list-subs "URL"

Useful Flags

• --restrict-filenames — safe ASCII filenames (no spaces/special chars) — important on all platforms
• --no-playlist — download single video even if URL is in a playlist
• -o "template.%(ext)s" — output template (%(ext)s auto-detects format)
• --cookies-from-browser chrome — use browser cookies for age-restricted content
• --extract-audio / -x — extract audio only
• --audio-format wav — convert audio to wav (for whisper)

---

Whisper Transcription Reference

Audio Extraction for Whisper

# Extract mono 16kHz WAV (whisper's preferred input format)
ffmpeg -i source.mp4 -vn -ar 16000 -ac 1 -y audio.wav

Basic Transcription

# Standard transcription with word-level timestamps
whisper audio.wav --model small --output_format json --word_timestamps true --language en

# Faster alternative (same flags, 4x speed)
whisper-ctranslate2 audio.wav --model small --output_format json --word_timestamps true --language en

Model Sizes

Model	VRAM	Speed	Quality	Use When
tiny	~1GB	Fastest	Rough	Quick previews, testing pipeline
base	~1GB	Fast	OK	Short clips, clear speech
small	~2GB	Good	Good	Default — best balance
medium	~5GB	Slow	Better	Important content, accented speech
large-v3	~10GB	Slowest	Best	Final production, multiple languages

Note: On macOS Apple Silicon, consider mlx-whisper as a faster native alternative.

JSON Output Structure

{
  "text": "full transcript text...",
  "segments": [
    {
      "id": 0,
      "start": 0.0,
      "end": 4.52,
      "text": " Hello everyone, welcome back.",
      "words": [
        {"word": " Hello", "start": 0.0, "end": 0.32, "probability": 0.95},
        {"word": " everyone,", "start": 0.32, "end": 0.78, "probability": 0.91},
        {"word": " welcome", "start": 0.78, "end": 1.14, "probability": 0.98},
        {"word": " back.", "start": 1.14, "end": 1.52, "probability": 0.97}
      ]
    }
  ]
}

• segments[].words[] gives word-level timing when --word_timestamps true
• probability indicates confidence (< 0.5 = likely wrong)

---

YouTube json3 Subtitle Parsing

Format Structure

{
  "events": [
    {
      "tStartMs": 1230,
      "dDurationMs": 5000,
      "segs": [
        {"utf8": "hello ", "tOffsetMs": 0},
        {"utf8": "world ", "tOffsetMs": 200},
        {"utf8": "how ", "tOffsetMs": 450},
        {"utf8": "are you", "tOffsetMs": 700}
      ]
    }
  ]
}

Extracting Word Timing

For each event and each segment within it:

• word_start_ms = event.tStartMs + seg.tOffsetMs
• word_start_secs = word_start_ms / 1000.0
• word_text = seg.utf8.trim()

Events without segs are line breaks or formatting — skip them. Events with segs containing only "\n" are newlines — skip them.

---

SRT Generation from Transcript

SRT Format

1
00:00:00,000 --> 00:00:02,500
First line of caption text

2
00:00:02,500 --> 00:00:05,100
Second line of caption text

Rules for Building Good SRT

• Group words into subtitle lines of ~8-12 words (2-3 seconds per line)
• Break at natural pause points (periods, commas, clause boundaries)
• Keep lines under 42 characters for readability on mobile
• Adjust timestamps relative to clip start (subtract clip start time from all timestamps)
• Timestamp format: HH:MM:SS,mmm (comma separator, not dot)
• Each entry: index line, timestamp line, text line(s), blank line
• Use file_write tool to create the SRT file — works identically on all platforms

Styled Captions with ASS Format

For animated/styled captions, use ASS subtitle format instead of SRT:

ffmpeg -i clip.mp4 -vf "subtitles=clip.ass:force_style='FontSize=22,FontName=Arial,Bold=1,PrimaryColour=&H00FFFFFF,OutlineColour=&H00000000,Outline=2,Shadow=1,Alignment=2,MarginV=40'" -c:a copy output.mp4

Key ASS style properties:

• PrimaryColour=&H00FFFFFF — white text (AABBGGRR format)
• OutlineColour=&H00000000 — black outline
• Outline=2 — outline thickness
• Alignment=2 — bottom center
• MarginV=40 — margin from bottom edge
• FontSize=22 — good size for 1080x1920 vertical

---

FFmpeg Video Processing

Scene Detection

ffmpeg -i input.mp4 -filter:v "select='gt(scene,0.3)',showinfo" -f null - 2>&1

• Threshold 0.1 = very sensitive, 0.5 = only major cuts
• Parse pts_time: from showinfo output for timestamps
• On macOS/Linux pipe through grep showinfo, on Windows pipe through findstr showinfo

Silence Detection

ffmpeg -i input.mp4 -af "silencedetect=noise=-30dB:d=1.5" -f null - 2>&1

• d=1.5 = minimum 1.5 seconds of silence
• Look for silence_start and silence_end in output

Clip Extraction

# Re-encoded (accurate cuts)
ffmpeg -ss 00:01:30 -to 00:02:15 -i input.mp4 -c:v libx264 -c:a aac -preset fast -crf 23 -movflags +faststart -y clip.mp4

# Lossless copy (fast but may have keyframe alignment issues)
ffmpeg -ss 00:01:30 -to 00:02:15 -i input.mp4 -c copy -y clip.mp4

• -ss before -i = fast seek (recommended for extraction)
• -to = end timestamp, -t = duration

Vertical Video (9:16 for Shorts/Reels/TikTok)

# Center crop (when source is 16:9)
ffmpeg -i input.mp4 -vf "crop=ih*9/16:ih:(iw-ih*9/16)/2:0,scale=1080:1920" -c:a copy output.mp4

# Scale with letterbox padding (preserves full frame)
ffmpeg -i input.mp4 -vf "scale=1080:1920:force_original_aspect_ratio=decrease,pad=1080:1920:(ow-iw)/2:(oh-ih)/2:black" -c:a copy output.mp4

Caption Burn-in

# SRT subtitles with styling (use relative path or forward-slash absolute path)
ffmpeg -i input.mp4 -vf "subtitles=subs.srt:force_style='FontSize=22,FontName=Arial,PrimaryColour=&H00FFFFFF,OutlineColour=&H00000000,Outline=2,Alignment=2,MarginV=40'" -c:a copy output.mp4

# Simple text overlay
ffmpeg -i input.mp4 -vf "drawtext=text='Caption':fontsize=48:fontcolor=white:borderw=3:bordercolor=black:x=(w-text_w)/2:y=h-th-40" output.mp4

Windows path escaping: subtitles=C\\:/Users/me/subs.srt (double-backslash before colon)

Thumbnail Generation

# At specific time (2 seconds in)
ffmpeg -i input.mp4 -ss 2 -frames:v 1 -q:v 2 -y thumb.jpg

# Best keyframe
ffmpeg -i input.mp4 -vf "select='eq(pict_type,I)',scale=1280:720" -frames:v 1 thumb.jpg

# Contact sheet
ffmpeg -i input.mp4 -vf "fps=1/10,scale=320:-1,tile=4x4" contact.jpg

Video Analysis

# Full metadata (JSON)
ffprobe -v quiet -print_format json -show_format -show_streams input.mp4

# Duration only
ffprobe -v error -show_entries format=duration -of csv=p=0 input.mp4

# Resolution
ffprobe -v error -select_streams v:0 -show_entries stream=width,height -of csv=p=0 input.mp4

API-Based STT Reference

Groq Whisper API

Fastest cloud STT — uses whisper-large-v3 on Groq hardware. Free tier available.

curl -s -X POST "https://api.groq.com/openai/v1/audio/transcriptions" \
  -H "Authorization: Bearer $GROQ_API_KEY" \
  -H "Content-Type: multipart/form-data" \
  -F "file=@audio.wav" \
  -F "model=whisper-large-v3" \
  -F "response_format=verbose_json" \
  -F "timestamp_granularities[]=word" \
  -o transcript_raw.json

Response: {"text": "...", "words": [{"word": "hello", "start": 0.0, "end": 0.32}]}

• Max file size: 25MB. For longer audio, split with ffmpeg first.
• timestamp_granularities[]=word is required for word-level timing.

OpenAI Whisper API

curl -s -X POST "https://api.openai.com/v1/audio/transcriptions" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: multipart/form-data" \
  -F "file=@audio.wav" \
  -F "model=whisper-1" \
  -F "response_format=verbose_json" \
  -F "timestamp_granularities[]=word" \
  -o transcript_raw.json

Response format same as Groq. Max 25MB.

Deepgram Nova-2

curl -s -X POST "https://api.deepgram.com/v1/listen?model=nova-2&smart_format=true&utterances=true&punctuate=true" \
  -H "Authorization: Token $DEEPGRAM_API_KEY" \
  -H "Content-Type: audio/wav" \
  --data-binary @audio.wav \
  -o transcript_raw.json

Response: {"results": {"channels": [{"alternatives": [{"words": [{"word": "hello", "start": 0.0, "end": 0.32, "confidence": 0.99}]}]}]}}

• Supports streaming, but for clips use batch mode.
• smart_format=true adds punctuation and casing.

---

TTS Reference

Edge TTS (free, no API key needed)

# List available voices
edge-tts --list-voices

# Generate speech
edge-tts --text "Your caption text here" --voice en-US-AriaNeural --write-media tts_output.mp3

# Other good voices: en-US-GuyNeural, en-GB-SoniaNeural, en-AU-NatashaNeural

Install: pip install edge-tts

OpenAI TTS

curl -s -X POST "https://api.openai.com/v1/audio/speech" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"tts-1","input":"Your text here","voice":"alloy"}' \
  --output tts_output.mp3

Voices: alloy, echo, fable, onyx, nova, shimmer Models: tts-1 (fast), tts-1-hd (quality)

ElevenLabs

curl -s -X POST "https://api.elevenlabs.io/v1/text-to-speech/21m00Tcm4TlvDq8ikWAM" \
  -H "xi-api-key: $ELEVENLABS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"text":"Your text here","model_id":"eleven_monolingual_v1"}' \
  --output tts_output.mp3

Voice ID 21m00Tcm4TlvDq8ikWAM = Rachel (default). List voices: GET /v1/voices

Audio Merging (TTS + Original)

# Mix TTS over original audio (original at 30% volume, TTS at 100%)
ffmpeg -i clip.mp4 -i tts.mp3 \
  -filter_complex "[0:a]volume=0.3[orig];[1:a]volume=1.0[tts];[orig][tts]amix=inputs=2:duration=first[out]" \
  -map 0:v -map "[out]" -c:v copy -c:a aac -y clip_voiced.mp4

# Replace audio entirely (no original audio)
ffmpeg -i clip.mp4 -i tts.mp3 -map 0:v -map 1:a -c:v copy -c:a aac -shortest -y clip_voiced.mp4

---

Quality & Performance Tips

• Use -preset ultrafast for quick previews, -preset slow for final output
• Use -crf 23 for good quality (18=high, 28=low, lower=bigger files)
• Add -movflags +faststart for web-friendly MP4
• Use -threads 0 to auto-detect CPU cores
• Always use -y to overwrite without asking

---

Telegram Bot API Reference

sendVideo — Upload and send a video to a chat/channel

curl -s -X POST "https://api.telegram.org/bot<BOT_TOKEN>/sendVideo" \
  -F "chat_id=<CHAT_ID>" \
  -F "video=@clip_N_final.mp4" \
  -F "caption=Clip title here" \
  -F "parse_mode=HTML" \
  -F "supports_streaming=true"

Parameters

Parameter	Required	Description
chat_id	Yes	Channel (-100XXXXXXXXXX or @channelname), group, or user numeric ID
video	Yes	@filepath for upload (max 50MB) or a Telegram file_id for re-send
caption	No	Text caption, up to 1024 characters
parse_mode	No	HTML or MarkdownV2 for styled captions
supports_streaming	No	true enables progressive playback

Success Response

{"ok": true, "result": {"message_id": 1234, "video": {"file_id": "BAACAgI...", "file_size": 5242880}}}

Error Response

{"ok": false, "error_code": 400, "description": "Bad Request: chat not found"}

Common Errors

Error Code	Description	Fix
400	Chat not found	Verify chat_id; bot must be added to the channel/group
401	Unauthorized	Bot token is invalid or revoked — regenerate via @BotFather
413	Request entity too large	File exceeds 50MB — re-encode: ffmpeg -i input.mp4 -fs 49M -c:v libx264 -crf 28 -preset fast -c:a aac -y output.mp4
429	Too many requests	Rate limited — wait the retry_after seconds from the response

File Size Limit

Telegram allows up to 50MB for video uploads via Bot API. If a clip exceeds this:

ffmpeg -i clip_N_final.mp4 -fs 49M -c:v libx264 -crf 28 -preset fast -c:a aac -movflags +faststart -y clip_N_tg.mp4

---

WhatsApp Business Cloud API Reference

Two-Step Flow: Upload Media → Send Message

WhatsApp Cloud API requires uploading the video first to get a media_id, then sending a message referencing that ID.

Step 1 — Upload Media

curl -s -X POST "https://graph.facebook.com/v21.0/<PHONE_NUMBER_ID>/media" \
  -H "Authorization: Bearer <ACCESS_TOKEN>" \
  -F "file=@clip_N_final.mp4" \
  -F "type=video/mp4" \
  -F "messaging_product=whatsapp"

Success response:

{"id": "1234567890"}

Step 2 — Send Video Message

curl -s -X POST "https://graph.facebook.com/v21.0/<PHONE_NUMBER_ID>/messages" \
  -H "Authorization: Bearer <ACCESS_TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{
    "messaging_product": "whatsapp",
    "to": "<RECIPIENT_PHONE>",
    "type": "video",
    "video": {
      "id": "<MEDIA_ID>",
      "caption": "Clip title here"
    }
  }'

Success response:

{"messaging_product": "whatsapp", "contacts": [{"wa_id": "14155551234"}], "messages": [{"id": "wamid.HBgL..."}]}

File Size Limit

WhatsApp allows up to 16MB for video uploads. If a clip exceeds this:

ffmpeg -i clip_N_final.mp4 -fs 15M -c:v libx264 -crf 30 -preset fast -c:a aac -movflags +faststart -y clip_N_wa.mp4

24-Hour Messaging Window

WhatsApp requires the recipient to have messaged you within the last 24 hours (for non-template messages). If you get a "template required" error, either:

• Ask the recipient to send any message to the business number first
• Use a pre-approved message template instead of a free-form video message

Common Errors

Error Code	Description	Fix
100	Invalid parameter	Check phone_number_id and recipient format (no + prefix, no spaces)
190	Invalid/expired access token	Regenerate token in Meta Business Settings; temporary tokens expire in 24h
131030	Recipient not in allowed list	In test mode, add recipient to allowed numbers in Meta Developer Portal
131047	Re-engagement message / template required	Recipient hasn't messaged within 24h — use a template or ask them to message first
131053	Media upload failed	File too large or unsupported format — re-encode as MP4 under 16MB