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hermes-agent/skills/media/songsee/SKILL.md
teknium1 732c66b0f3 refactor: reorganize skills into sub-categories 
The skills directory was getting disorganized — mlops alone had 40
skills in a flat list, and 12 categories were singletons with just
one skill each.

Code change:
- prompt_builder.py: Support sub-categories in skill scanner.
  skills/mlops/training/axolotl/SKILL.md now shows as category
  'mlops/training' instead of just 'mlops'. Backwards-compatible
  with existing flat structure.

Split mlops (40 skills) into 7 sub-categories:
- mlops/training (12): accelerate, axolotl, flash-attention,
  grpo-rl-training, peft, pytorch-fsdp, pytorch-lightning,
  simpo, slime, torchtitan, trl-fine-tuning, unsloth
- mlops/inference (8): gguf, guidance, instructor, llama-cpp,
  obliteratus, outlines, tensorrt-llm, vllm
- mlops/models (6): audiocraft, clip, llava, segment-anything,
  stable-diffusion, whisper
- mlops/vector-databases (4): chroma, faiss, pinecone, qdrant
- mlops/evaluation (5): huggingface-tokenizers,
  lm-evaluation-harness, nemo-curator, saelens, weights-and-biases
- mlops/cloud (2): lambda-labs, modal
- mlops/research (1): dspy

Merged singleton categories:
- gifs → media (gif-search joins youtube-content)
- music-creation → media (heartmula, songsee)
- diagramming → creative (excalidraw joins ascii-art)
- ocr-and-documents → productivity
- domain → research (domain-intel)
- feeds → research (blogwatcher)
- market-data → research (polymarket)

Fixed misplaced skills:
- mlops/code-review → software-development (not ML-specific)
- mlops/ml-paper-writing → research (academic writing)

Added DESCRIPTION.md files for all new/updated categories.
2026-03-09 03:35:53 -07:00

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name description version author license metadata
songsee Generate spectrograms and audio feature visualizations (mel, chroma, MFCC, tempogram, etc.) from audio files via CLI. Useful for audio analysis, music production debugging, and visual documentation. 1.0.0 community MIT
hermes
tags homepage
Audio
Visualization
Spectrogram
Music
Analysis
https://github.com/steipete/songsee

songsee

Generate spectrograms and multi-panel audio feature visualizations from audio files.

Prerequisites

Requires Go:

go install github.com/steipete/songsee/cmd/songsee@latest

Optional: ffmpeg for formats beyond WAV/MP3.

Quick Start

# Basic spectrogram
songsee track.mp3

# Save to specific file
songsee track.mp3 -o spectrogram.png

# Multi-panel visualization grid
songsee track.mp3 --viz spectrogram,mel,chroma,hpss,selfsim,loudness,tempogram,mfcc,flux

# Time slice (start at 12.5s, 8s duration)
songsee track.mp3 --start 12.5 --duration 8 -o slice.jpg

# From stdin
cat track.mp3 | songsee - --format png -o out.png

Visualization Types

Use --viz with comma-separated values:

Type Description
spectrogram Standard frequency spectrogram
mel Mel-scaled spectrogram
chroma Pitch class distribution
hpss Harmonic/percussive separation
selfsim Self-similarity matrix
loudness Loudness over time
tempogram Tempo estimation
mfcc Mel-frequency cepstral coefficients
flux Spectral flux (onset detection)

Multiple --viz types render as a grid in a single image.

Common Flags

Flag Description
--viz Visualization types (comma-separated)
--style Color palette: classic, magma, inferno, viridis, gray
--width / --height Output image dimensions
--window / --hop FFT window and hop size
--min-freq / --max-freq Frequency range filter
--start / --duration Time slice of the audio
--format Output format: jpg or png
-o Output file path

Notes

  • WAV and MP3 are decoded natively; other formats require ffmpeg
  • Output images can be inspected with vision_analyze for automated audio analysis
  • Useful for comparing audio outputs, debugging synthesis, or documenting audio processing pipelines