video-to-context
Skill1 starsChanged 8 months agoApache-2.0View on GitHub
Use when the user shares a screen recording, demo video, or bug reproduction video and you need to understand what happens in it. Extracts speech transcript (via Whisper GPU) and key frames (via ffmpeg) into a markdown document you can read directly. Symptoms: user mentions video, screen recording, .mov/.mp4 file, or asks you to watch/analyze a recording.
- Installs packages
In this repository11 other files it gives its agents.
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The file
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 1 is distinctive, and 3 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 36
Run this to install the transcription stack:
- Very specific: line 64
Ensure ~/.local/bin is in PATH, or create a symlink:
- Very specific: line 115
Read the .txt transcript first for the full narrative
- Very specific: line 116
Read specific frames (.jpg) when you need to see what was on screen at a given moment
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Video-to-Context: Extract full context from videos
Turn screen recordings into readable context (transcript + key frames) so you can understand what happened without watching the video.
When to use
- User shares a screen recording from a Jira card, Slack, or bug report
- You need to understand both what's shown on screen AND what's being said
- Video is in
.mov,.mp4,.webm, or similar format
Requirements
- ffmpeg (frame extraction)
- whisper-ctranslate2 with CUDA (speech-to-text) -- see setup below
- NVIDIA GPU with CUDA drivers (CPU fallback possible but slow)
- uv package manager
Setup (one-time)
Run this to install the transcription stack:
#!/bin/bash
set -euo pipefail
VENV="$HOME/.local/share/video-tools"
# Create isolated venv
uv venv "$VENV" --python 3.12
# Install whisper + CUDA libs
VIRTUAL_ENV="$VENV" uv pip install whisper-ctranslate2 nvidia-cublas-cu12 nvidia-cudnn-cu12
# Create wrapper that sets CUDA library paths
mkdir -p "$HOME/.local/bin"
cat > "$HOME/.local/bin/whisper-gpu" << 'SCRIPT'
#!/bin/bash
VENV="$HOME/.local/share/video-tools"
export LD_LIBRARY_PATH="$VENV/lib/python3.12/site-packages/nvidia/cublas/lib:$VENV/lib/python3.12/site-packages/nvidia/cudnn/lib:${LD_LIBRARY_PATH:-}"
exec "$VENV/bin/whisper-ctranslate2" "$@"
SCRIPT
chmod +x "$HOME/.local/bin/whisper-gpu"
# Verify
whisper-gpu --help &>/dev/null && echo "Ready." || echo "Setup failed."Ensure ~/.local/bin is in PATH, or create a symlink:
sudo ln -sf ~/.local/bin/whisper-gpu /usr/local/bin/whisper-gpuExtraction process
Step 1: Transcribe audio
whisper-gpu "$VIDEO" \
--model large-v3 \
--language en \
--device cuda \
--compute_type int8_float16 \
--output_format all \
--output_dir "$OUTDIR" \
--word_timestamps True \
--vad_filter True \
--batched True \
--batch_size 8This produces .txt (plain transcript), .srt (timestamped), and .json (word-level timestamps).
For non-English videos, change --language or remove it for auto-detection.
Step 2: Extract scene-change frames
ffmpeg -y -i "$VIDEO" \
-vf "select='gt(scene,0.3)',scale=1280:-1,drawtext=text='%{pts\:hms}':fontsize=24:fontcolor=white:box=1:boxcolor=black@0.7:x=w-tw-10:y=h-th-10" \
-vsync vfr -q:v 3 \
"$OUTDIR/frames/scene_%04d.jpg"The scene>0.3 threshold catches major visual changes (page navigations, modal opens, screen switches). Timestamps are burned into each frame's bottom-right corner.
Step 3: Extract interval frames (every 5s)
ffmpeg -y -i "$VIDEO" \
-vf "fps=1/5,scale=1280:-1,drawtext=text='%{pts\:hms}':fontsize=24:fontcolor=white:box=1:boxcolor=black@0.7:x=w-tw-10:y=h-th-10" \
-q:v 3 \
"$OUTDIR/frames/interval_%04d.jpg"Interval frames fill gaps where no scene change was detected but something relevant might still be happening.
Step 4: Read the output
- Read the
.txttranscript first for the full narrative - Read specific frames (
.jpg) when you need to see what was on screen at a given moment - Cross-reference
.srttimestamps with frame timestamps to match speech to visuals
Gotcha: macOS filenames with Unicode spaces
macOS screen recordings use U+202F (NARROW NO-BREAK SPACE) instead of regular spaces in filenames. If your file isn't found, try:
# Rename to regular spaces
find "$DIR" -maxdepth 1 -name "Screen*Recording*" -exec bash -c '
NEW=$(echo "$1" | sed "s/\xe2\x80\xaf/ /g")
[ "$1" != "$NEW" ] && mv "$1" "$NEW"
' _ {} \;Or add a glob fallback in your script:
if [ ! -f "$VIDEO" ]; then
RESOLVED=$(find "$(dirname "$VIDEO")" -maxdepth 1 \
-name "$(basename "$VIDEO" | sed 's/ /*/g')" -print -quit 2>/dev/null)
[ -n "$RESOLVED" ] && VIDEO="$RESOLVED"
fiOutput structure
video_context/
context.md # Transcript + frame list (read this first)
recording.txt # Plain transcript
recording.srt # Timestamped transcript
recording.json # Word-level timestamps
frames/
scene_0001.jpg # Scene-change frames (timestamps burned in)
scene_0002.jpg
interval_0001.jpg # Every-5s frames (timestamps burned in)
interval_0002.jpgQuick reference
| What | Command/Flag | Notes |
| Whisper model | large-v3 | Best accuracy. Use medium for speed. |
| Quantization | int8_float16 | Good for 8GB VRAM GPUs (e.g. RTX 4060) |
| Scene threshold | gt(scene,0.3) | Lower = more frames. 0.3 is a good default. |
| Frame interval | fps=1/5 | Every 5 seconds. Change denominator as needed. |
| CPU fallback | --device cpu | Works but 10-20x slower than CUDA |
| Output formats | --output_format all | Gets txt, srt, json, vtt, tsv at once |
| VAD filter | --vad_filter True | Skips silence, improves accuracy |
| Batched mode | --batched True | Faster on GPU, uses more VRAM |
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