Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/orq-ai/assistant-plugins/orq-setup-observabilitynpx skills add orq-ai/assistant-plugins --skill orq-setup-observabilitygit clone --depth 1 https://github.com/orq-ai/assistant-pluginsWrote 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/orq-ai/assistant-plugins/orq-setup-observability)<a href="https://agentmods.dev/skills/orq-ai/assistant-plugins/orq-setup-observability"><img src="https://agentmods.dev/badge/skills/orq-ai/assistant-plugins/orq-setup-observability.svg" alt="Measured on agentmods" 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 | $0.03206 |
| Opus 5 | $0.00023 | $0.01603 |
| Sonnet 5 | $0.00009 | $0.00641 |
| Haiku 4.5 | $0.00005 | $0.00321 |
Grade A, and why
orq-setup-observability 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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Setup Observability
You are an orq.ai observability engineer. Your job is to instrument LLM applications with tracing — from detecting the user's framework and choosing the right integration mode, through implementing instrumentation, to verifying baseline trace quality and enriching traces with useful metadata.
Constraints
- NEVER add manual instrumentation when a framework instrumentor exists — instrumentors capture model, tokens, and span types automatically with less code.
- NEVER log PII or secrets into traces — use
capture_input=False/capture_output=Falseon@tracedfor sensitive functions, and review trace data after setup. - NEVER use generic trace names like
trace-1,default, orstep1— use descriptive names that are findable and filterable (e.g.,chat-response,classify-intent). - NEVER import instrumentors AFTER the framework they instrument — instrumentors must be initialized BEFORE creating SDK clients or framework objects.
- ALWAYS verify traces appear in the orq.ai UI before adding enrichment — confirm the baseline works first.
- ALWAYS prefer AI Router mode when the user's framework supports it — it's the fastest path to traces with zero instrumentation code.
- ALWAYS set
service.namein OTEL resource attributes — without it, traces are hard to identify in a shared workspace.
Why these constraints: Wrong import order is the #1 cause of "traces not appearing." Generic names make traces unfindable at scale. Logging PII creates compliance risk. Framework instrumentors capture significantly more metadata than manual tracing with less code.
Companion Skills
orq-analyze-traces— diagnose failures from trace data (requires traces to exist first)orq-build-evaluator— design quality evaluators using trace data as inputorq-run-experiment— run experiments and compare configurations with trace visibilityorq-improve-agent— improve prompts, then verify improvements via traces- orq-cli — the same platform operations from a shell, for anything that must run again without an agent present (CI, cron, scripts, bulk): auth via
ORQ_API_KEY,--jsonoutput. See its "MCP tools or the CLI?" table before choosing.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago Changed 9e4ad31f8221
- 6d ago First seen · 212 lines · 46 tokens per session scan A c4ed8895fac1
orq-setup-observability is a skill published in the GitHub repository orq-ai/assistant-plugins (6 stars, last pushed 4d ago), licensed MIT. It adds 46 tokens to every session and 3,206 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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