mirror of https://github.com/NousResearch/hermes-agent.git synced 2026-04-25 00:51:20 +00:00

docs(website): dedicated page per bundled + optional skill (#14929 )

Generates a full dedicated Docusaurus page for every one of the 132 skills
(73 bundled + 59 optional) under website/docs/user-guide/skills/{bundled,optional}/<category>/.
Each page carries the skill's description, metadata (version, author, license,
dependencies, platform gating, tags, related skills cross-linked to their own
pages), and the complete SKILL.md body that Hermes loads at runtime.

Previously the two catalog pages just listed skills with a one-line blurb and
no way to see what the skill actually did — users had to go read the source
repo. Now every skill has a browsable, searchable, cross-linked reference in
the docs.

- website/scripts/generate-skill-docs.py — generator that reads skills/ and
optional-skills/, writes per-skill pages, regenerates both catalog indexes,
and rewrites the Skills section of sidebars.ts. Handles MDX escaping
(outside fenced code blocks: curly braces, unsafe HTML-ish tags) and
rewrites relative references/*.md links to point at the GitHub source.
- website/docs/reference/skills-catalog.md — regenerated; each row links to
the new dedicated page.
- website/docs/reference/optional-skills-catalog.md — same.
- website/sidebars.ts — Skills section now has Bundled / Optional subtrees
with one nested category per skill folder.
- .github/workflows/{docs-site-checks,deploy-site}.yml — run the generator
before docusaurus build so CI stays in sync with the source SKILL.md files.

Build verified locally with `npx docusaurus build`. Only remaining warnings
are pre-existing broken link/anchor issues in unrelated pages.

2026-04-23 22:22:11 -07:00

3 KiB

Raw Blame History

title	sidebar_label	description
Songsee — Generate spectrograms and audio feature visualizations (mel, chroma, MFCC, tempogram, etc	Songsee	Generate spectrograms and audio feature visualizations (mel, chroma, MFCC, tempogram, etc

{/* This page is auto-generated from the skill's SKILL.md by website/scripts/generate-skill-docs.py. Edit the source SKILL.md, not this page. */}

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.

Skill metadata


Source	Bundled (installed by default)
Path	`skills/media/songsee`
Version	`1.0.0`
Author	community
License	MIT
Tags	`Audio`, `Visualization`, `Spectrogram`, `Music`, `Analysis`

Reference: full SKILL.md

:::info The following is the complete skill definition that Hermes loads when this skill is triggered. This is what the agent sees as instructions when the skill is active. :::

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

3 KiB Raw Blame History