How do I install the Bpm skill?

Run npx skillmds@latest add chienchuanw/bpm in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Bpm?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

chienchuanw (@chienchuanw) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Bpm by chienchuanw — Agent Skill

Name: Bpm
Author: chienchuanw

Bpm

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.

chienchuanw 4355aaf 2 files · 6.2 KB Updated Sep 17, 2026

File contents

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).

---name: bpmdescription: 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.---# gma2 bpmDetect each song's tempo from its audio and write it into the matching songmacro's line 2 (`SetVar $songbpm=<n>`), which the engine feeds to the BPM speedmaster on song change. Detection lives in `scripts/analyze_bpm.py`; all consolewrites 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 anephemeral environment (nothing gets installed permanently):```bashuv 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, andprints JSON sorted by folder then by the `S<n>` track number — one entry per filewith `bpm`, `raw`, `duration_s`, and a `suspect` flag (with `half`/`double`alternatives) for likely octave errors.### 2. Map files → song IDsThe analyzer sorts by folder + `S<n>`, which mirrors the rundown's artist/songorder. 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 mappingagainst the song names — if the audio folders or counts don't match the builtmacros (e.g. an extra track that isn't in the rundown), surface the discrepancyand 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 andlet the user confirm — the musically-correct octave needs ears. Write the detectedvalue by default; offer to halve/double on request. Missing BPM defaults to 60.### 4. Write into the macrosFor each mapped song, set line 2 via the MCP `send_raw_command`:```Assign Macro 1.<id>.2 /cmd="SetVar $songbpm=<bpm>"```### 5. Verify and reportRead back a few: `List Macro 1.<id>.2` should show `SetVar $songbpm=<bpm>`. Reportthe full table (id · name · BPM), mark the suspects you flagged, and remind theuser 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).

Frequently asked questions

Bpm