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-nextjs-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-nextjs-style)<a href="https://agentmods.dev/skills/pareelamre/analyzing-llm-rationale/otel-nextjs-style"><img src="https://agentmods.dev/badge/skills/pareelamre/analyzing-llm-rationale/otel-nextjs-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-nextjs-style"><img src="https://agentmods.dev/badge/skills/pareelamre/analyzing-llm-rationale/otel-nextjs-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 | $0.00046 | $0.01383 |
| Opus 5 | $0.00023 | $0.00691 |
| Sonnet 5 | $0.00009 | $0.00277 |
| Haiku 4.5 | $0.00005 | $0.00138 |
Grade A, and why
otel-nextjs-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-nextjs-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 — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OTel Next.js Style
For Next.js apps, prefer the framework entrypoint.
// instrumentation.ts
import { registerOTel } from "@vercel/otel";
export function register() {
registerOTel({
serviceName: "mugline-web",
});
}
Do not replace this with a custom NodeSDK bootstrap unless the repo is not a
normal Next/Vercel app or already has a custom provider that must be extended.
For JavaScript/TypeScript LLM providers, prefer provider instrumentation over
manual child spans. For Anthropic, add OpenInference in the same bootstrap and
keep call sites native. This example uses @vercel/[email protected]; if the installed
types are v1, use logRecordProcessor singular instead.
import Anthropic from "@anthropic-ai/sdk";
import { AnthropicInstrumentation } from "@arizeai/openinference-instrumentation-anthropic";
import { OTLPLogExporter } from "@opentelemetry/exporter-logs-otlp-http";
import { BatchLogRecordProcessor } from "@opentelemetry/sdk-logs";
import { registerOTel } from "@vercel/otel";
const anthropicInstrumentation = new AnthropicInstrumentation({
traceConfig: {
hideInputs: true,
hideOutputs: true,
},
});
anthropicInstrumentation.manuallyInstrument(Anthropic);
export function register() {
registerOTel({
serviceName: "mugline-web",
instrumentations: [anthropicInstrumentation],
logRecordProcessors: [new BatchLogRecordProcessor(new OTLPLogExporter())],
});
}
Route Handlers
Use native OTel APIs where auto-instrumentation is blind.
import { withSpan } from "@superlog/otel-helpers";
const tracer = trace.getTracer("mugline.web");
const meter = metrics.getMeter("mugline.web");
const requests = meter.createCounter("mug.copy.generated");
export async function POST(request: Request) {
const tenantId = request.headers.get("x-tenant-id") ?? "tenant_demo";
return await withSpan("mug.copy.generate", async (span) => {
span.setAttribute("tenant.id", tenantId);
requests.add(1, { "tenant.id": tenantId, outcome: "success" });
return Response.json({ ok: true });
}, { tracer });
}
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 · 152 lines · 46 tokens per session scan A 7319d31fd5cb
otel-nextjs-style is a skill published in the GitHub repository pareelamre/analyzing-llm-rationale (0 stars, last pushed yesterday), licensed MIT. It adds 46 tokens to every session and 1,383 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-nextjs-style, differing in 0 lines, and is treated as a copy.
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