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 howdeploy/deploychan_mcp --skill buffered-agent-voicegit clone --depth 1 https://github.com/howdeploy/deploychan_mcpWrote 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/howdeploy/deploychan_mcp/buffered-agent-voice)<a href="https://agentmods.dev/skills/howdeploy/deploychan_mcp/buffered-agent-voice"><img src="https://agentmods.dev/badge/skills/howdeploy/deploychan_mcp/buffered-agent-voice/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/howdeploy/deploychan_mcp/buffered-agent-voice"><img src="https://agentmods.dev/badge/skills/howdeploy/deploychan_mcp/buffered-agent-voice.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00051 | $0.00869 |
| Opus 5.5 | $0.00020 | $0.00348 |
| Sonnet 5.5 | $0.00010 | $0.00174 |
| Haiku 4.5 | $0.00005 | $0.00087 |
Grade A, and why
Speak through a buffered, interruptible agent voice service 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 today.
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.
How it starts
The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Buffered agent speech
Identify the user's actual voice service, output device, selected voice/model, proxy,
current volume, cancellation path and conversation binding. This skill contains no
personal profile, API key, device address or ready-made session attachment. For the
implementation details retrieve buffered-speech-and-cancellation through Deploychan
MCP, and voice-pipeline-latency-and-lessons for measurement/negative evidence.
Deliver an explicitly requested answer
- Reuse the existing private service interface and warm connection. Do not create a new script/session or restart a daemon just to speak one reply.
- Prepare a direct, natural first sentence and a short complete answer. Avoid Markdown, paths, URLs and tool logs in speech. A short conversation may be a few sentences; a work summary states the result and a useful next step. Do not pad the first sentence with filler to reach a provider text threshold.
- Use one bounded request or single-use permission for the identified conversation, then submit the final speech text promptly. Do not mix two submission methods for the same answer or enable automatic narration for subsequent unrelated tasks.
- Preserve current speaker volume and mute. Do not change computer volume or the chosen voice/model/proxy unless that is part of the user's task.
- Treat queue acceptance as acceptance only. State verified playback/listening evidence when available; do not retry an ambiguous paid request automatically.
Maintain the transport without regressing reliability
Keep input text incremental. Force generation after the first prepared phrase, continue text on the same provider socket and flush the remainder only if nonempty. For a ready text request use one input segment/flush. Handle intermediate audio-final boundaries without discarding later text or returning the connection to another job early. Keep frame alignment, length limits, timeouts and cleanup through cancellation.
Prepare and validate the full private WAV before starting the native device player. This deliberately avoids the proxy-sensitive live playback that failed in the case study. Do not remove buffering merely to improve the first-PCM number. Check the actual TTD protocol instead of copying fields from a different TTS WebSocket API.
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.
- today First seen · 75 lines · 51 tokens per session scan A fa8e8d2cd04e
Speak through a buffered, interruptible agent voice service is a skill published in the GitHub repository howdeploy/deploychan_mcp (19 stars, last pushed today), licensed MIT. It adds 51 tokens to every session and 869 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-28.
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