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/superlog-onboard/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/superlog-onboard)<a href="https://agentmods.dev/skills/pareelamre/analyzing-llm-rationale/superlog-onboard"><img src="https://agentmods.dev/badge/skills/pareelamre/analyzing-llm-rationale/superlog-onboard/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/superlog-onboard"><img src="https://agentmods.dev/badge/skills/pareelamre/analyzing-llm-rationale/superlog-onboard.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 | $0.00071 | $0.05251 |
| Opus 5 | $0.00036 | $0.02625 |
| Sonnet 5 | $0.00014 | $0.01050 |
| Haiku 4.5 | $0.00007 | $0.00525 |
Grade A, and why
superlog-onboard 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 10d 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.
1. **Run the project's own dev or build command** (whatever its `package.json` / `pyproject` / `Makefile` already wires up). Confirm it starts cleanly with no errors that trace back to your OTel install. Also run a telem This is a copy
100% identical to superlog-onboard — 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 — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Superlog onboarding
Wire OpenTelemetry traces, logs, and metrics into the user's project so telemetry streams to Superlog. Cover every app and service in the repo — not just the one the user is currently sitting in.
Prefer native OpenTelemetry APIs and the framework's documented bootstrap over custom helper layers. If a specific stack stumps you, search the OTel docs for that language; don't guess.
Before editing, read the applicable companion skills:
otel-onboarding-stylefor general OTel taste.otel-python-stylefor Python services.otel-fastapi-stylefor FastAPI services.otel-livekit-stylefor LiveKit agents.otel-nextjs-stylefor Next.js/Vercel apps.otel-expo-stylefor Expo / React Native apps.otel-supabase-edge-stylefor Supabase Edge Functions.otel-generic-stylefor any other language (Go, Java/Kotlin, Ruby, Rust, .NET/C#, PHP, Elixir, plain Node, …) — use this as the fallback when none of the above match.
Step 0 — Endpoint and public token handling
The OTLP endpoint is always https://intake.superlog.sh and goes directly
in the bootstrap source.
The Superlog public ingest token starts with sl_public_. It is
project-scoped, write-only, and intentionally safe to include in application
source, including browser and mobile bundles. Treat it like a PostHog project
token, Sentry DSN, or Datadog RUM client token: it can send telemetry to one
project, but cannot read data, change settings, or access the Superlog account.
Inline the public token as a constant next to the endpoint. Do not put it in
.env files, deploy settings, or generated shell commands; source-level config
is the intended onboarding path and avoids broken deploys from missing env vars.
When an OTLP exporter requires headers, pass the public token through the
exporter constructor as the x-api-key header. This is the single most
common onboarding failure: the token is valid, but the exporter sends it under
the wrong header name (or no header), so ingest returns 401 on every request and
the install looks broken when the key is fine. Ingest reads the token from
exactly two places — x-api-key: <token>, or Authorization: Bearer <token>
(the literal Bearer prefix is required). Use x-api-key; it avoids the
Bearer -prefix footgun and survives proxies/SDKs that strip Authorization.
Do not invent other header names (api-key, x-superlog-token,
Authorization: <token> without Bearer ) — all of them 401.
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.
- 10d ago First seen · 251 lines · 71 tokens per session scan A 81df991bd07b
superlog-onboard is a skill published in the GitHub repository pareelamre/analyzing-llm-rationale (0 stars, last pushed yesterday), licensed MIT. It adds 71 tokens to every session and 5,251 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to superlog-onboard, differing in 0 lines, and is treated as a copy.
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