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 agentmods add commands/michael-l-i/cadence-code/wrap-upgit clone --depth 1 https://github.com/michael-L-i/cadence-codeWrote 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/commands/michael-l-i/cadence-code/wrap-up)<a href="https://agentmods.dev/commands/michael-l-i/cadence-code/wrap-up"><img src="https://agentmods.dev/badge/commands/michael-l-i/cadence-code/wrap-up.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 | $0.00007 | $0.00250 |
| Opus 5 | $0.00003 | $0.00125 |
| Sonnet 5 | $0.00001 | $0.00050 |
| Haiku 4.5 | $0.00001 | $0.00025 |
Grade A, and why
wrap-up 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 3d 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.
What it actually says
End the current Cadence Code conversation cleanly with
mcp__cadence-code__voice_status, mcp__cadence-code__voice_speak, and
mcp__cadence-code__voice_stop.
- Call
mcp__cadence-code__voice_status. - If
readyis false, say there is no active Cadence Code conversation to wrap up. Do not callvoice_start,voice_speak,voice_listen, orvoice_stop. - If
readyis true, callmcp__cadence-code__voice_speakwith a brief, natural goodbye andlisten_after: false. - Call
mcp__cadence-code__voice_stopexactly once withwait_for_speech: true. Do not listen again. - Confirm briefly that the conversation ended and its local speech models were
released. If the goodbye fails, still call
voice_stoponce to clean up, then show the error.
Do not start a new conversation, ask for confirmation, or do additional task work from this command.
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.
- 3d ago First seen · 23 lines · 7 tokens per session scan A e9954a2629a1
wrap-up is a command published in the GitHub repository michael-L-i/cadence-code (2 stars, last pushed 6d ago), licensed MIT. It adds 7 tokens to every session and 250 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
summary-say
Summarize and speak the last response.
tts-init
Install MLX dependencies and download model (4GB).
tts-mute
Temporarily mute TTS notifications.
tts-start
Start TTS server to keep model warm.
tts-status
Check TTS server status and configuration.
tts-stop
Stop the TTS server to reclaim GPU memory.