hermes-agent/skills/media/songsee/SKILL.md at 59fbcd5ccb4d080f9a00d8a862f6998aa04a1ed7

mirror of https://github.com/NousResearch/hermes-agent.git synced 2026-05-15 04:12:25 +00:00

Teknium 98db898c0b feat(skills): declare platforms frontmatter for all 79 undeclared built-in skills

Completes the Windows-gating coverage for the built-in skills/ tree. Every
bundled SKILL.md now carries an explicit platforms: declaration so the
loader (agent.skill_utils.skill_matches_platform) can skip-load skills
that don't fit the current OS.

74 skills declared cross-platform (platforms: [linux, macos, windows]):
  Creative (16): ascii-art, ascii-video, architecture-diagram, baoyu-comic,
    baoyu-infographic, claude-design, creative-ideation, design-md,
    excalidraw, humanizer, manim-video, p5js, pixel-art,
    popular-web-designs, pretext, sketch, songwriting-and-ai-music,
    touchdesigner-mcp
  Autonomous agents: claude-code, codex, hermes-agent, opencode
  Data/devops: jupyter-live-kernel, kanban-orchestrator, kanban-worker,
    webhook-subscriptions, dogfood, codebase-inspection
  GitHub: github-auth, github-code-review, github-issues,
    github-pr-workflow, github-repo-management
  Media: gif-search, heartmula, songsee, spotify, youtube-content
  MCP / email / gaming / notes / smart-home: native-mcp, himalaya,
    pokemon-player, obsidian, openhue
  mlops (non-broken): weights-and-biases, huggingface-hub, llama-cpp,
    outlines, segment-anything-model, dspy, trl-fine-tuning
  Productivity: airtable, google-workspace, linear, maps, nano-pdf,
    notion, ocr-and-documents, powerpoint
  Red-teaming / research: godmode, arxiv, blogwatcher, llm-wiki,
    polymarket
  Software-dev: debugging-hermes-tui-commands, hermes-agent-skill-authoring,
    node-inspect-debugger, plan, requesting-code-review, spike,
    subagent-driven-development, systematic-debugging,
    test-driven-development, writing-plans
  Misc: yuanbao

5 skills gated from Windows (platforms: [linux, macos]):
  mlops/inference/vllm (serving-llms-vllm)
    vLLM is officially Linux-only; Windows requires WSL.
  mlops/training/axolotl
    Axolotl's flash-attn + deepspeed + bitsandbytes stack is Linux-first.
  mlops/training/unsloth
    Requires Triton + xformers + flash-attn — Linux only in practice.
  mlops/models/audiocraft (audiocraft-audio-generation)
    torchaudio ffmpeg backend + encodec dependencies are Linux-first.
  mlops/inference/obliteratus
    Research abliteration workflow; relies on Linux-focused pytorch
    kernels and MLX — no first-class Windows path.

Same strict-over-lenient policy as the optional-skills sweep: when the
underlying tool's Windows support is rough, missing, or WSL-only, gate the
skill. Easier to un-gate after verified Windows support lands than to leak
partial support that manifests as mid-task failures.

Combined with prior commits in this branch, every bundled SKILL.md
(skills/ + optional-skills/) now has a platforms: declaration.

2026-05-08 14:27:40 -07:00

2.3 KiB

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name

description

version

author

license

platforms

metadata

prerequisites

songsee

Audio spectrograms/features (mel, chroma, MFCC) via CLI.

1.0.0

community

MIT

linux

macos

windows

hermes

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

2.3 KiB Raw Blame History