gcx is a command-line tool that lets people and AI coding agents manage and inspect Grafana Cloud, Enterprise, and open-source instances. It provides access to dashboards, alerts, SLOs, metrics, logs, and traces, with workflows for alert investigation, dashboard management, GitOps, and observability setup. Its catalogue entries provide agent instructions and extensions for using gcx.
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 skills add grafana/gcx --skill agento11y-prod-setupgit clone --depth 1 https://github.com/grafana/gcxWrote 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/grafana/gcx/agento11y-prod-setup)<a href="https://agentmods.dev/skills/grafana/gcx/agento11y-prod-setup"><img src="https://agentmods.dev/badge/skills/grafana/gcx/agento11y-prod-setup/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/grafana/gcx/agento11y-prod-setup"><img src="https://agentmods.dev/badge/skills/grafana/gcx/agento11y-prod-setup.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00259 | $0.07950 |
| Opus 5 | $0.00130 | $0.03975 |
| Sonnet 5 | $0.00052 | $0.01590 |
| Haiku 4.5 | $0.00026 | $0.00795 |
Grade A, and why
agento11y-prod-setup 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 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.
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 — 473 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Observability — production evals & guards setup
The production counterpart to agento11y-test-starter (which runs pre-ship, on code alone,
producing an offline test suite). This skill runs after ship, when the agent has real
traffic, and sets up the two production surfaces the starter deliberately leaves out:
- Online eval rules — evaluators that score ingested live conversations, so regressions surface without hand-reviewing every conversation.
- Guards (hook-rules) — policies on the request path that
warn(and can later be promoted todeny) in real time. A guard decides via one of three shapes:evaluator_ids(an evaluator judges),redact(regex redaction), ortool_filter(block tool calls). See Step 4.
What this skill does that agento11y doesn't
The sibling agento11y skill is the mechanics layer: exact CLI flags, evaluator/rule YAML
shapes, create-or-update semantics, the online-eval setup steps. It assumes you already know
what to create.
This skill is the judgment layer. It answers which rules and guards this specific agent needs, by grounding in two evidence sources a generic checklist can't use:
- The agent's code — its system prompt, tools, and how it handles user data. Half the value
is here; read and cite it (
file:line). - The agent's real traffic — because it's deployed, you can see what it actually does in prod, not just what the code says it might.
Two gaps this skill fills beyond agento11y:
- Recommendation from evidence —
agento11ystarts once you know what to create; this decides. - Guards —
agento11ydocuments evaluators and rules but not guards (hook-rules), even thoughgcx agento11y guardsexists. This skill carries the guard shapes (evaluator_ids/redact/tool_filter, plusaction_on_fail) itself.
For any mechanical detail — exact flags, evaluator/rule YAML fields, the setup flow — defer to
the agento11y skill and to gcx agento11y <sub> --help rather than restating it here.
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 · 473 lines · 259 tokens per session scan A 56e6d8e5142a
agento11y-prod-setup is a skill published in the GitHub repository grafana/gcx (594 stars, last pushed today), licensed Apache-2.0. It adds 259 tokens to every session and 7,950 once invoked, about $0.0013 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 2026-08-30.
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