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-instrument-feature/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-instrument-feature)<a href="https://agentmods.dev/skills/pareelamre/analyzing-llm-rationale/otel-instrument-feature"><img src="https://agentmods.dev/badge/skills/pareelamre/analyzing-llm-rationale/otel-instrument-feature/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-instrument-feature"><img src="https://agentmods.dev/badge/skills/pareelamre/analyzing-llm-rationale/otel-instrument-feature.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 | $0.00092 | $0.02090 |
| Opus 5 | $0.00046 | $0.01045 |
| Sonnet 5 | $0.00018 | $0.00418 |
| Haiku 4.5 | $0.00009 | $0.00209 |
Grade A, and why
otel-instrument-feature scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
Run the feature end-to-end through the project's normal entry point — hit the route with `curl`, invoke the CLI subcommand, enqueue the job, trigger the agent tool. A unit test that mocks the tracer does not count; the g This is a copy
100% identical to otel-instrument-feature — 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Instrument a new feature with OpenTelemetry
Use this skill whenever you are about to write — or have just finished writing — a new business operation in the user's project. A "new feature" is anything a human operator would want to find later when something looks wrong: a new HTTP route, RPC handler, GraphQL resolver, background job, queue consumer, cron task, CLI subcommand, agent tool, scheduled workflow, or any module-level function that represents a meaningful step in a user-facing flow.
Adding code that is just plumbing — a pure helper, a type, a config — does not trigger this skill. Adding code that takes a request in, does work, and produces a side effect or response does.
Step 0 — Confirm OTel is already wired
Before instrumenting, look for an existing bootstrap. Common signs: a telemetry.ts / observability.ts / instrumentation.ts / init_observability() module, @vercel/otel registerOTel(...), NodeSDK/sdk-node setup, Python LoggerProvider + OTLPLogExporter, superlogHeaders(...), or an inlined https://intake.superlog.sh endpoint with an sl_public_ token.
If you cannot find a bootstrap, do not invent one inside the new feature file. Tell the user the project still needs OTel wiring and that this is the job of the superlog-onboard skill — then continue with the feature implementation, leaving a single brief TODO at the bootstrap site (not on every span/metric/log call site). Do not block on this.
If a bootstrap exists, read the applicable companion skill before writing code:
otel-onboarding-style— general OTel taste and attribute conventions.otel-python-style— Python.otel-fastapi-style— FastAPI.otel-livekit-style— LiveKit agents.otel-nextjs-style— Next.js / Vercel.otel-expo-style— Expo / React Native.otel-supabase-edge-style— Supabase Edge Functions.otel-generic-style— anything else (Go, Java, Ruby, Rust, .NET, PHP, Elixir, plain Node, …).
Match the local convention. If the file you are editing already uses withSpan from @superlog/otel-helpers, keep using it; if it uses tracer.startActiveSpan directly, keep using that. Do not introduce a second style alongside the existing one.
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 · 104 lines · 92 tokens per session scan A 86fb19fdeef0
otel-instrument-feature is a skill published in the GitHub repository pareelamre/analyzing-llm-rationale (0 stars, last pushed today), licensed MIT. It adds 92 tokens to every session and 2,090 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to otel-instrument-feature, differing in 0 lines, and is treated as a copy.
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