Skill Introduction

Overview

Audio generation skill — jingles, beds, voiceover, and sound effects. Routes music requests to Suno V5 / Udio / Lyria, speech to MiniMax TTS / FishAudio / ElevenLabs V3, and SFX to ElevenLabs SFX or AudioCraft. Output is one MP3/WAV file saved to the project folder.

Core value

Turns reusable Documentation know-how into an installable skill, helping users complete github, community work faster.

Target users

Developers, testers, and maintainers who handle Documentation tasks in Focus Code.
Teams that already trust workflows or content from nexu-io.
Users who want standardized prompts, steps, or conventions instead of repeating setup work.

Best practices

Read the skill content first to confirm required inputs, expected outputs, and dependencies.
Try it on a small task before relying on it for critical work.
Add project-specific constraints such as coding style, target platform, test expectations, and delivery format.
For external sources, verify the source link, version, and recent maintenance activity.

Best use cases

Tasks related to github, community that need a reusable execution flow.
Converting a community repo, team convention, or personal workflow into day-to-day assistance.
Starting from a proven skill instead of writing prompts or procedures from scratch.

Limits and boundaries

Results depend on the quality of the original skill content and may need human correction.
It does not replace code review, tests, security review, or professional judgment.
External tools, APIs, account permissions, and local dependencies still need separate setup.

Differentiation

Structured around Documentation, making it easier to discover and reuse than loose prompt snippets.
Marked as GitHub, which helps users judge trust and maintenance expectations.
Keeps the original source link available for repository, documentation, or discussion follow-up.
Tagged with github, community, so it can be filtered by concrete task intent.

Install and use

Install

Copy Install Command

focus install audio-jingle-aea4fe

View source

Detail Preview

SKILL.md

Primary filemarkdown5 KB

name: audio-jingle description: | Audio generation skill — jingles, beds, voiceover, and sound effects. Routes music requests to Suno V5 / Udio / Lyria, speech to MiniMax TTS / FishAudio / ElevenLabs V3, and SFX to ElevenLabs SFX or AudioCraft. Output is one MP3/WAV file saved to the project folder. triggers:

"music"
"jingle"
"bed"
"voiceover"
"tts"
"sound effect"
"音乐"
"配音"
"音效" od: mode: audio surface: audio scenario: marketing preview: type: html entry: example.html design_system: requires: false example_prompt: | A 30-second upbeat indie-pop jingle for a coffee shop launch — warm electric piano lead, brushed drums, gentle bass, a single sun-soaked "ahhh" choir on the chorus. No vocals. Loop-friendly tail.

Audio Jingle Skill

Three sub-modes. The active project's audioKind decides which one runs:

`audioKind`	Models we route to	Plan focus
`music`	Suno V5 (default), Udio, Lyria 2	genre + tempo + instrumentation
`speech`	MiniMax TTS (default), Fish, ElevenLabs V3	script + voice + pacing
`sfx`	ElevenLabs SFX (default), AudioCraft	texture + impact + duration

Resource map

audio-jingle/
├── SKILL.md
└── example.html

Workflow

Step 0 — Read the project metadata

audioKind, audioModel, audioDuration (seconds), and (for speech) voice. Branch by audioKind and use the values verbatim — no clarifying form unless something is marked (unknown — ask).

Important: voice is provider-specific. For minimax-tts, --voice must be a valid MiniMax voice_id (for example male-qn-qingse), not a natural-language description. If you only have a prose voice brief ("warm female narrator", "neutral Mandarin"), keep that in your plan but omit --voice so the daemon's default voice id applies, or ask the user to choose a specific id.

Step 1 — Plan

Music

Genre + reference artists (1-2)
Tempo (BPM) + key
Instrumentation (3-5 instruments max)
Vocals: yes / no / hummed / choir
Mood arc (intro → chorus → outro)

Speech

Script (final, not draft — TTS runs verbatim)
Voice target + pacing For MiniMax this means a real voice_id, not prose in --voice
Pronunciation hints for proper nouns / acronyms

SFX

Texture (impact / whoosh / ambience / foley)
Duration + envelope (sharp attack vs. gentle swell)
Layering note (single hit vs. stacked)

State the plan in 2-3 sentences before dispatching.

Step 2 — Compose the prompt

Use the format the upstream model prefers. Bind audioDuration to the API parameter directly; never put "make it 30 seconds" in prose.

Step 3 — Dispatch via the media contract

Use the unified dispatcher — do not call provider APIs by hand:

"$OD_NODE_BIN" "$OD_BIN" media generate \
  --project "$OD_PROJECT_ID" \
  --surface audio \
  --audio-kind "<music|speech|sfx>" \
  --model "<audioModel from metadata>" \
  --duration <audioDuration seconds> \
  [--voice "<provider voice id (speech only)>"] \
  --output "<short-slug>-<duration>s.mp3" \
  --prompt "<assembled prompt from Step 2 — for speech, the literal script>"

The command prints one line of JSON: {"file": {"name": "...", ...}}. The bytes land in the project; the FileViewer renders the audio transport controls automatically.

Step 4 — Hand off

Reply with: plan summary, the filename returned by the dispatcher, and one sentence on what to try if the user wants a variation (e.g. "swap tempo from 92 to 108 BPM" rather than "make it different").

Hard rules

TTS runs your script literally. Proof it before dispatching — even one stray comma changes the cadence.
MiniMax TTS rejects free-form voice prose in --voice. Use a real MiniMax voice_id (for example male-qn-qingse) or omit the flag and let the daemon's default voice apply.
Music: under 30s = single section; 30–90s = intro + body; 90s+ = full arc. Don't try to fit a 3-act song into 15 seconds.
SFX: prefer one well-described layer over a paragraph of "make it cool" — generators reward specific texture words.
Save the file every turn. The audio viewer shows transport controls the moment the file lands.

audio jingle