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Audio spectrograms/features (mel, chroma, MFCC) via CLI.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Use when a problem or request is underspecified and you need to decide WHAT to clarify before doing the work — the NEXT-BEST QUESTIONS to answer, ranked. Interrogates the prompt into candidate questions, projects plausible answers with probabilities, estimates each question's value of information (how much the answer would change the recommended plan, weighted by likelihood and stakes), discards low-value/redundant ones, and keeps generating until a diverse bucket of high-value questions is filled. Reports a ranked list with recommendations (pre-answer / assume-default) using role-specialized local Ollama models. Reports only — it does not ask the user or answer the questions itself. Triggers: "what should I clarify", "what questions matter here", "is this spec complete", "what am I missing before I start".
Use to run one improvement iteration on the next-best-questions skill: review its learnings journal, research opportunities, pre-register a cost-aware experiment, build off-default, evaluate Δresult AND Δcost, and journal the verdict either way. Triggers: 'improve nbq', 'run the nbq loop', 'next-best-questions iteration'. There are NO scripts; this protocol is agent-executed.
Prompt any model or alias via "ask <model> <question>". Resolves short names (deepseek, kimi, qwen, glm) to full model IDs. Captures session IDs for follow-up questions. Comparison mode: "ask deepseek kimi <question>" dispatches multiple models in parallel. Each call is a full Hermes agent with tools and multi-turn reasoning. Replies inline with a model badge.
Autonomous end-to-end build/debug engine. Use it when asked to build, implement, fix, or debug a module/feature: it runs the WHOLE loop unattended — drafts a checkable Definition of Done, fail-closed gates it (vague or ambiguous requests come back with the blocking questions instead of guesses), generates tests, then loops implement -> lint -> evidence until coverage + two distinct-model judges + real exit codes + a whole-suite regression gate all pass — and on COMPLETE AUTO-MERGES the work into the target repo's current branch (no repo named = a fresh workspace under the write-safe root is the deliverable). Prefer this over the `dev` skill for any multi-file task whose outcome should be verified, merged code rather than role-by-role assistance. NOT for trivial one-shot asks (a quick answer, a one-line edit, running a command) — just do those directly. Run: `devloop "<request>" [--repo PATH]` (or python3 ${HERMES_HOME}/skills/software-development/devloop/scripts/devloop_cli.py) — see "How to run".
An LLM authors a JSON/YAML workflow spec (states, prompts, routing, failure policy); a durable interpreter runs it, passing state from step to step, with suspend/resume, human gates, and replay that never re-runs completed steps. Use when an LLM should define-and-run a multi-step workflow that must pause for a human (approve, decide, fix something) and resume later, survive a crash and continue exactly once, or route declaratively on step errors. Rides a durable-execution-lite engine (append-only journal, deterministic replay). Triggers: LLM-authored workflow, workflow spec, prompt routing, durable resume, resumable script, pause and resume, checkpoint and continue, don't re-run completed steps.
Cron job that reviews Hermes conversation history and captures durable knowledge into the wiki. Handles triage (SKIP/NOTE/PAGE), Slack-session cleanup, and post-capture lint.
| name | songsee |
| description | Audio spectrograms/features (mel, chroma, MFCC) via CLI. |
| version | 1.0.0 |
| author | community |
| license | MIT |
| platforms | ["linux","macos","windows"] |
| metadata | {"hermes":{"tags":["Audio","Visualization","Spectrogram","Music","Analysis"],"homepage":"https://github.com/steipete/songsee","config":[{"key":"songsee.enabled","description":"Enable songsee skill behavior","default":true,"prompt":"Enable songsee skill?"}],"category":"creative"}} |
| prerequisites | {"commands":["songsee"]} |
Generate spectrograms and multi-panel audio feature visualizations from audio files.
Requires Go:
go install github.com/steipete/songsee/cmd/songsee@latest
Optional: ffmpeg for formats beyond WAV/MP3.
# 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
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.
| 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 |
ffmpegvision_analyze for automated audio analysis