Audio jingle

Skill onfire7777/universal-ai-skills-library/skills/audio-jingle

Router-first AI skill system for Codex, Claude, Cursor, Hermes, Paperclip, OpenCode, and local AI stacks: search, preflight-route, and load 1,812 skills on demand without duplicating the corpus.

Install

npx -y skills add onfire7777/universal-ai-skills-library --skill audio-jingle

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

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What its author says it does

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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.

SKILL.md

4.2 KB, 933 tokens by cl100k_base, as published. Nobody here has run it

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.

Gives 0 of the 12 instructions most video audio skills give in 933 tokens

Counted across 621 of the 795 authors here whose files we hold, read 2026-08-06

read individual rule files for detailed explanationsin 21 of 621, across 9 files
Use WAV PCM 16kHz mono audio formatin 13 of 621, across 4 files
render final videoin 13 of 621, across 6 files
use this skill when dealing with Remotion codein 11 of 621, across 4 files
save generated audio to a WAV filein 11 of 621, across 4 files
handle conversion errors gracefullyin 10 of 621, across 6 files
add captions to videos alwaysin 10 of 621, across 4 files
generate music from text descriptions using MusicGenin 9 of 621, across 2 files
do not skip pipeline layersin 9 of 621, across 3 files
do not make one tool do everythingin 9 of 621, across 3 files
never ask the user to paste their full API keyin 9 of 621, across 3 files
use azure document intelligence for complex pdfsin 9 of 621, across 4 files

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.