Bpm

Skill chienchuanw/chuan-skills/plugins/gma2/skills/bpm

Personal plugin marketplace of Claude Code skills (slash commands)

Install

npx -y skills add chienchuanw/chuan-skills --skill bpm

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What its author says it does

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Estimate the BPM of a show's audio files and write each tempo into its song macro's `SetVar $songbpm` line on grandMA2, so the speed master tracks per song. Use whenever the user points at a folder of performance audio and wants BPM detected and pushed into the song macros — "analyze the BPM", "calculate tempos and update the macros", "fill in $songbpm from these tracks", "get the BPM for the set". Requires the gma2 MCP connected and the song macros already built (see the connect and setlist skills). Needs ffmpeg; pulls librosa via uv.

SKILL.md

3.2 KB, as published. Nobody here has run it

gma2 bpm

Detect each song's tempo from its audio and write it into the matching song macro's line 2 (SetVar $songbpm=<n>), which the engine feeds to the BPM speed master on song change. Detection lives in scripts/analyze_bpm.py; all console writes go through the gma2 MCP.

Inputs (ask the user)

Audio folder/files — the user provides the path each time (shows differ). Typically one subfolder per artist with files named S1_…, S2_… in song order. Mixed .wav/.mp3 is fine.
Song macros already exist (built by the setlist skill); the console is connected via the gma2 MCP.

Workflow

1. Analyze

ffmpeg must be on PATH. Run the analyzer with uv so librosa loads into an ephemeral environment (nothing gets installed permanently):

uv run --with librosa --with soundfile --with numpy python \
  plugins/gma2/skills/bpm/scripts/analyze_bpm.py "<audio folder>"

It decodes each file to mono 22.05 kHz via ffmpeg, runs librosa beat tracking, and prints JSON sorted by folder then by the S<n> track number — one entry per file with bpm, raw, duration_s, and a suspect flag (with half/double alternatives) for likely octave errors.

2. Map files → song IDs

The analyzer sorts by folder + S<n>, which mirrors the rundown's artist/song order. Map each file to its song ID the same way the set list was numbered (artist-offset blocks: 101/111/121…, +1 per song). Sanity-check the mapping against the song names — if the audio folders or counts don't match the built macros (e.g. an extra track that isn't in the rundown), surface the discrepancy and don't invent a macro for it.

3. Flag the suspects (don't silently auto-correct)

Automatic trackers often lock onto half or double the musical tempo. Report the suspect entries (bpm < 75 or > 170) with their half/double alternatives and let the user confirm — the musically-correct octave needs ears. Write the detected value by default; offer to halve/double on request. Missing BPM defaults to 60.

4. Write into the macros

For each mapped song, set line 2 via the MCP send_raw_command:

Assign Macro 1.<id>.2 /cmd="SetVar $songbpm=<bpm>"

5. Verify and report

Read back a few: List Macro 1.<id>.2 should show SetVar $songbpm=<bpm>. Report the full table (id · name · BPM), mark the suspects you flagged, and remind the user the show isn't saved.

Notes

Tempo is rounded to an integer (the speed master takes a BPM number).
The analyzer never touches the console — it only prints numbers; the agent does all writes through MCP tools (keeps the skill's console path uniform).