Loooom: I Built a Skill to Teach Claude to Hear Music
I've never created a skill on Loooom through the web UI before. I always did it by editing the catalog repo directly. So last night I tried it the "normal" way — describe what you want, let an AI write it, publish it, and see what happens.
The result was genuinely impressive. I'm still thinking about what it could be used for.
The Setup
I play piano badly. I've been messing around with Sabrina Carpenter's Espresso on an MPK Mini, and I had a 37-second SoundCloud clip of me fumbling through it:
I wanted to see if I could teach Claude Code to analyze audio — identify BPM, structure, frequency brackets, stems. Not because I needed this to be perfect, but because I wanted to see how far you could get with a well-written skill and no special plugins.
I went to loooom.xyz/s/mager/how-to-identify-parts-of-a-song, used Gemini to draft the skill description and instructions, and hit publish.
What Claude Did With It
Inside Claude Code, I just asked:
can you identify parts of a songIt loaded the skill automatically, asked for an audio source, and I pointed it at a local MP3 file. It didn't have ffmpeg or librosa installed — so it installed them. Mid-session. No fuss.
Then it ran a Python PCM analysis pipeline and produced this:
ListenIdentifyParts — Analysis Report
| Property | Value |
|---|---|
| BPM | ~91 (onset-estimated; canonical: ~104) |
| Meter | 4/4 |
| Key | B major |
| Beat Count | 50 |
| Transients | 50 strong onsets |
Structure Timeline:
| # | Section | Start | End | Notes |
|---|---|---|---|---|
| 1 | INTRO | 0:00 | 0:04 | Pre-beat, rising |
| 2 | VERSE | 0:04 | 0:12 | Onsets begin, energy climbs |
| 3 | CHORUS | 0:12 | 0:30 | Sustained peak RMS |
| 4 | OUTRO | 0:30 | 0:37 | Hard energy cliff |
Stems identified: Kick, Bass/Sub, Lead Vocal, Guitar/Keys, Hi-Hat, Overhead/Air.
Here's what it actually looked like in the terminal:
From a 37-second amateur piano recording. No tags, no metadata, no prior knowledge besides the filename.
What's Wild About This
The skill was written by Gemini. Published by me via a web form. Loaded by Claude Code. Then Claude installed its own dependencies, wrote its own analysis pipeline, and produced a publication-quality report.
Three different AIs — one wrote the instructions, one executed them, one (arguably) composed the source material. I was mostly just the person who pointed at the file.
That's not a workflow I planned. It just... emerged.
What Could This Actually Be Used For?
I'm still working through it. Some possibilities:
- Music education — "here's my practice recording, what sections need work?"
- Production feedback — fast structural analysis before a mix session
- Podcast editing — identify energy drops, silence zones, segment boundaries
- Sample digging — find the chorus in a long track, map the beat grid
None of these require perfect accuracy. They require useful signal fast. And that's what it delivered.
The Real Insight
I built this mostly to test skill creation via the UI. But what I got back was a reminder that the value of a skill isn't the code — it's the framing. The skill told Claude what to look for, what format to report in, what to do when tools are missing. The analysis itself was Claude improvising with standard Unix tools.
That's the Loooom pattern at its best: a skill teaches the agent a new way to see. In this case, a way to hear.
The skill is live at loooom.xyz/s/mager/how-to-identify-parts-of-a-song if you want to try it.
My terrible 37-second piano cover that started all this — espresso taste on SoundCloud.