Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add skeletorflet/opencode-supreme-setup --skill audio-jinglegit clone --depth 1 https://github.com/skeletorflet/opencode-supreme-setupWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/skeletorflet/opencode-supreme-setup/audio-jingle)<a href="https://agentmods.dev/skills/skeletorflet/opencode-supreme-setup/audio-jingle"><img src="https://agentmods.dev/badge/skills/skeletorflet/opencode-supreme-setup/audio-jingle.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00078 | $0.01159 |
| Opus 5 | $0.00039 | $0.00580 |
| Sonnet 5 | $0.00016 | $0.00232 |
| Haiku 4.5 | $0.00008 | $0.00116 |
Grade A, and why
audio-jingle scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
94% identical to audio-jingle — 6 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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>"
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 133 lines · 78 tokens per session scan A ed595b2b8039
audio-jingle is a skill published in the GitHub repository skeletorflet/opencode-supreme-setup (47 stars, last pushed 3mo ago), licensed MIT. It adds 78 tokens to every session and 1,159 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to audio-jingle, differing in 6 lines, and is treated as a copy.
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