analyzing-llm-rationale: Skill for Claude Code

.agents/skills/otel-onboarding-style/SKILL.md

otel-onboarding-style is a skill for Claude Code, Codex from pareelamre/analyzing-llm-rationale. It costs 33 tokens per session (2,734 once invoked), scanned A, a copy of otel-onboarding-style, MIT.

A general coding guide for adding OpenTelemetry monitoring to an application. OpenTelemetry is a common way to record traces, logs, and measurements about software behavior.

In plain words
What is it for?
Use it when adding Superlog monitoring to services that need business-operation traces, counters, error details, or language-model usage data.
Why use it?
It helps developers collect useful monitoring data without inventing custom interfaces or wrapping code unnecessarily. It also gives guidance for language-model measurements and basic checks after setup.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is pareelamre/analyzing-llm-rationale's own configuration. It tells Claude Code and Codex how to work on analyzing-llm-rationale itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything analyzing-llm-rationale configures →

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/pareelamre/analyzing-llm-rationale/main/.agents/skills/otel-onboarding-style/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/pareelamre/analyzing-llm-rationale

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for otel-onboarding-style

README.md
[![agentmods](https://agentmods.dev/badge/skills/pareelamre/analyzing-llm-rationale/otel-onboarding-style/github.svg)](https://agentmods.dev/skills/pareelamre/analyzing-llm-rationale/otel-onboarding-style)
Your own site
<a href="https://agentmods.dev/skills/pareelamre/analyzing-llm-rationale/otel-onboarding-style"><img src="https://agentmods.dev/badge/skills/pareelamre/analyzing-llm-rationale/otel-onboarding-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.

agentmods 80×15 button for otel-onboarding-style

Your own site · 80×15
<a href="https://agentmods.dev/skills/pareelamre/analyzing-llm-rationale/otel-onboarding-style"><img src="https://agentmods.dev/badge/skills/pareelamre/analyzing-llm-rationale/otel-onboarding-style.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 33 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,734 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00033 $0.02734
Opus 5 $0.00016 $0.01367
Sonnet 5 $0.00007 $0.00547
Haiku 4.5 $0.00003 $0.00273

Measured 11d ago against content hash 0c7ee7da0f8d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

otel-onboarding-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.

Origin

This is a copy

100% identical to otel-onboarding-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.

.agents/skills/otel-onboarding-style/SKILL.md · 269 lines

How it starts

The opening of the file, as written. The whole thing — 269 lines — stays where its author put it; the contents beside it link to each section on GitHub.

OTel Onboarding Style

Use native OpenTelemetry APIs. Do not invent helper APIs.

In TypeScript/JavaScript, use the published @superlog/otel-helpers withSpan helper for bounded business spans and add @superlog/otel-helpers to package.json when it is not already present. This is required when the package can be installed. withSpan is the intended replacement for expanding a whole function into tracer.startActiveSpan(...) plus try / catch / finally. Do not use helpers to wrap provider SDK calls that OpenInference/provider instrumentation can observe directly.

Do:

const tracer = trace.getTracer("mugline.api");
const meter = metrics.getMeter("mugline.api");
const ordersSubmitted = meter.createCounter("orders.submitted");

await withSpan("order.submit", async (span) => {
  span.setAttributes({
    "tenant.id": tenantId,
    "order.id": orderId,
    outcome: "success",
  });
  ordersSubmitted.add(1, { "tenant.id": tenantId, outcome: "success" });
}, { tracer });

Do not:

await sendSuperlogSpan(...);
recordCounter(...);
withTelemetry(...);

Naming

  • Files/functions are provider-neutral: telemetry.ts, observability.ts, initTelemetry(), initObservability().
  • The word Superlog belongs only in endpoint/key setup comments or PR instructions.
  • Span names are conventional and low-cardinality: checkout.process, voice.session, llm.generate_copy.
  • Prefer semantic product-operation span names over provider transport names. llm.generate_copy or llm.voice_response is usually more useful than llm.anthropic.messages.create.

Endpoint and public token

Inline the endpoint and the Superlog public ingest token directly in the bootstrap source. The token starts with sl_public_, is project-scoped and write-only, and is intended to be safe in browser/mobile/server source code like a PostHog project token or Sentry DSN.

SUPERLOG_ENDPOINT = "https://intake.superlog.sh"
SUPERLOG_PUBLIC_TOKEN = "sl_public_…"

Read the full file on GitHub · 269 lines

Changes

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.

  1. 11d ago First seen · 269 lines · 33 tokens per session scan A 0c7ee7da0f8d

Subscribe to this mod's changes

otel-onboarding-style is a skill published in the GitHub repository pareelamre/analyzing-llm-rationale (0 stars, last pushed today), licensed MIT. It adds 33 tokens to every session and 2,734 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-onboarding-style, differing in 0 lines, and is treated as a copy.

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