Runs full diagnostic analysis on multiple files in parallel. Processes up to 50 files at once and returns per-file results plus a summary comparison. Sample rate mismatches between files are flagged as dealbreaker severity.
Parameters
| Parameter | Type | Default | Description |
|---|---|---|---|
| file_paths | list[string] | required | List of paths to audio files (max 50) |
Example Output
$ batch_diagnostic [vocals.wav, drums.wav, bass.wav, keys.wav]
Batch Diagnostic: 4 files
vocals.wav Loudness: -18.4 LUFS | Peak: -3.2 dB | Centroid: 2,847 Hz Problems: SIG sibilance at 6.8 kHz
drums.wav Loudness: -12.1 LUFS | Peak: -0.4 dB | Centroid: 1,423 Hz Problems: None
bass.wav Loudness: -16.7 LUFS | Peak: -2.8 dB | Centroid: 185 Hz Problems: MOD mud 200-350 Hz
keys.wav Loudness: -20.3 LUFS | Peak: -6.1 dB | Centroid: 3,102 Hz Problems: None
Summary: Loudest: drums.wav (-12.1 LUFS) Quietest: keys.wav (-20.3 LUFS) Range: 8.2 LUFS between stems Issues: 2 files have detected problems
What the Numbers Mean
Batch diagnostic returns a condensed version of each file’s full diagnostic (key metrics only), plus a cross-file summary:
- Range between stems — The LUFS difference between loudest and quietest. A large range (>10 LU) might mean some stems need gain staging before mixing.
- Per-file problems — Quick identification of which stems need treatment before mixing begins.
- Sample rate mismatch — If stems don’t share a sample rate, batch_diagnostic flags this as a dealbreaker. Mismatched rates will cause timing issues in your DAW.
See full_diagnostic for complete metric descriptions.
Example Prompts
Session overview
Analyze all the stems in my session: vocals.wav, drums.wav, bass.wav, guitar.wav, keys.wav, strings.wav
Quick scan
Run a batch diagnostic on everything in my recordings folder
Related Tools
- full_diagnostic — Deep analysis on a single file (more detail per file)
- multi_stem_masking — After batch diagnostic, check for frequency conflicts between stems
Pro tip
Run batch diagnostic before starting a mix session. It gives you a bird’s-eye view of all your material: relative loudness, which tracks have issues, and where the potential trouble spots are. Your AI assistant can then prioritize what to fix first.