Borrowing it
Nothing to install: this file belongs to pareelamre/analyzing-llm-rationale. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/pareelamre/analyzing-llm-rationale/main/.agents/skills/otel-livekit-style/SKILL.mdgit clone --depth 1 https://github.com/pareelamre/analyzing-llm-rationaleWrote 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/pareelamre/analyzing-llm-rationale/otel-livekit-style)<a href="https://agentmods.dev/skills/pareelamre/analyzing-llm-rationale/otel-livekit-style"><img src="https://agentmods.dev/badge/skills/pareelamre/analyzing-llm-rationale/otel-livekit-style/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/pareelamre/analyzing-llm-rationale/otel-livekit-style"><img src="https://agentmods.dev/badge/skills/pareelamre/analyzing-llm-rationale/otel-livekit-style.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 | $0.00031 | $0.00715 |
| Opus 5 | $0.00015 | $0.00358 |
| Sonnet 5 | $0.00006 | $0.00143 |
| Haiku 4.5 | $0.00003 | $0.00072 |
Grade A, and why
otel-livekit-style 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 11d 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
100% identical to otel-livekit-style — 0 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OTel LiveKit Style
LiveKit session lifecycle begins in entrypoint(ctx) and cleanup ends in
ctx.add_shutdown_callback(on_shutdown).
Whole-Session Span
It is okay for a voice.session span to end in on_shutdown, because the
operation crosses a framework callback boundary. Make it active while child
work is registered and started.
async def entrypoint(ctx: agents.JobContext) -> None:
await ctx.connect()
started_at = asyncio.get_running_loop().time()
session_span = tracer.start_span(
"voice.session",
attributes={
"tenant.id": tenant_id,
"user.id": user_id,
"voice.room_name": room_name,
},
)
with trace.use_span(session_span, end_on_exit=False):
voice_sessions_started.add(1, {
"tenant.id": tenant_id,
"voice.room_name.present": room_name != "unknown",
})
async def on_shutdown() -> None:
duration_ms = int((asyncio.get_running_loop().time() - started_at) * 1000)
session_span.set_attribute("voice.duration_ms", duration_ms)
voice_session_duration.record(duration_ms, {
"tenant.id": tenant_id,
"voice.disconnect_reason": disconnect_reason,
"outcome": "success",
})
session_span.end()
ctx.add_shutdown_callback(on_shutdown)
await _deliver_initial_greeting(...)
Do not replace this with a short voice.session.end span inside shutdown.
Bounded Session Work
Bounded operations still get their own decorators.
@tracer.start_as_current_span("voice.deliver_initial_greeting")
async def _deliver_initial_greeting(...):
...
Session Metrics
Use both:
voice.sessions.startedcounter at entrypoint startvoice.session.duration_mshistogram in shutdown
Add product metrics for important voice events, e.g.
voice.greetings.delivered.
Smoke Checks
For a minimal LiveKit fixture, a good smoke path imports the agent and executes one tiny instrumented function or starts a span/log record with a local OTLP endpoint. Checking that tracer/meter objects are non-None is not enough.
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.
- 11d ago First seen · 105 lines · 31 tokens per session scan A 3111882f7b2d
otel-livekit-style is a skill published in the GitHub repository pareelamre/analyzing-llm-rationale (0 stars, last pushed today), licensed MIT. It adds 31 tokens to every session and 715 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to otel-livekit-style, differing in 0 lines, and is treated as a copy.
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