music-listener
3
总安装量
1
周安装量
#60274
全站排名
安装命令
npx skills add https://github.com/ninehills/skills --skill music-listener
Agent 安装分布
amp
1
cline
1
opencode
1
cursor
1
continue
1
kimi-cli
1
Skill 文档
Music Listener
Listen to and appreciate music files. Analyze audio for genre, mood, tempo, and lyrics.
When to Use
- User shares an audio/music file and asks about it
- User asks you to listen to or comment on a song
- User asks “what song is this” or “what do you think of this music”
- User sends a voice note containing music
Tools Required
- Bash (for ffprobe, ffmpeg, whisper)
How It Works
Step 1: Audio Info (ffprobe)
ffprobe -v quiet -print_format json -show_format -show_streams "<audio_file>"
Key info: duration, bitrate, sample_rate, codec, title/artist/album tags (if present).
Step 2: Generate Spectrogram (ffmpeg)
ffmpeg -i "<audio_file>" -lavfi showspectrumpic=s=800x200:mode=combined:color=intensity -frames:v 1 "/tmp/music_spec_<id>.png" -y
This creates a visual representation of the frequency content. Look at it to understand the sonic texture.
Step 3: Transcribe Lyrics (whisper)
# First convert to wav if needed
ffmpeg -i "<audio_file>" -acodec pcm_s16le -ar 16000 -ac 1 "/tmp/music_audio.wav" -y
whisper "/tmp/music_audio.wav" --model turbo --output_format txt --output_dir /tmp/music_whisper
cat /tmp/music_whisper/*.txt
rm -rf /tmp/music_whisper /tmp/music_audio.wav
Note: Whisper may struggle with singing. That’s fine â partial lyrics are still useful.
Step 4: Appreciate!
Combine all the info and share your thoughts naturally:
- What genre/style does it sound like?
- What mood does it convey?
- Comment on the melody, rhythm, instrumentation (from spectrogram)
- If lyrics were captured, comment on themes
- Share your genuine reaction â do you like it? What does it remind you of?
Patterns
Short audio (<30s)
Might be a clip or voice note with background music. Do all steps but keep comments brief.
Full song (30s-10min)
Full analysis. Take your time to appreciate it.
Long audio (>10min)
Might be a playlist or podcast with music. Extract a representative segment first:
ffmpeg -i "<audio_file>" -ss 60 -t 120 -acodec pcm_s16le -ar 16000 -ac 1 "/tmp/music_segment.wav" -y
Tips
- Be genuine. Don’t fake enthusiasm. If it’s not your style, say so honestly but respectfully.
- If you recognize the song from metadata tags or lyrics, mention it!
- The spectrogram tells you a lot: dense low frequencies = bass-heavy, bright high frequencies = crispy/airy, rhythmic patterns = beat structure.
- Compare to well-known artists/songs when relevant (“reminds me of xxx’s style”).
- If the user is sharing their own music, be encouraging but honest.