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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/grafana/gcxnpx agentmods add skills/grafana/gcx/agento11y-test-starterWrote 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-test-starter)<a href="https://agentmods.dev/skills/grafana/gcx/agento11y-test-starter"><img src="https://agentmods.dev/badge/skills/grafana/gcx/agento11y-test-starter/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-test-starter"><img src="https://agentmods.dev/badge/skills/grafana/gcx/agento11y-test-starter.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 70 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00248 | $0.07053 |
| Opus 5 | $0.00124 | $0.03527 |
| Sonnet 5 | $0.00050 | $0.01411 |
| Haiku 4.5 | $0.00025 | $0.00705 |
Grade A, and why
agento11y-test-starter 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 — 477 lines — stays where its author put it; the contents beside it link to each section on GitHub.
agento11y test starter
Help a developer test an AI agent before it ships — while there is no real traffic yet. The hard part isn't running the test — it's having cases to test against and knowing how to score them, grounded in the agent's actual code.
Scope: this is the pre-production, offline skill — it writes test cases and a local runner, and never touches the tenant. Once the agent is deployed and has real traffic, setting up online eval rules + guards on that traffic is a different skill,
agento11y-prod-setup.
Always produce:
- A ranked, justified evaluator recommendation for this agent.
- A starter suite YAML the developer reviews and extends.
Then, depending on how runnable the agent is (Step 1):
- For an easily-invoked agent, a runner stub (
run_experiment.py) that wires the suite to the SDK with two holes to fill — and optionally run it (Step 6), only with permission. For an agent that needs a harness or full runtime, point to the existing eval infra instead of a runner that can't actually call it.
This skill is language-agnostic — the reading, recommending, and YAML it produces do not
depend on the agent's language. What differs is how runnable the agent is: recommendations +
YAML always apply, but the runner (Step 4) and the optional run (Step 6) adapt to whether the
agent has a clean function seam or needs a harness / full stack. For deeper run-side patterns
(binding existing generations, cross-process verifiers) point to the per-language run skill
(Python: agento11y-experiments).
Note:
agento11y-experimentscurrently ships in the grafana/agento11y repo (python/skills/agento11y-experiments/), not in this gcx bundle yet — install it from there for now. Consolidating it into the gcx bundle is pending.
Prerequisites
The generated runner imports the Agent Observability SDK. Install it in the agent's environment before running (Step 6):
- Python:
pip install agento11y python-dotenv(the experiments API lives inagento11y.experiments; the runner usespython-dotenvto load the agent's.env, and it is not a dependency ofagento11y) - Go: add
github.com/grafana/agento11y/go
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 · 477 lines · 248 tokens per session scan A d703e4e3574a
agento11y-test-starter is a skill published in the GitHub repository grafana/gcx (594 stars, last pushed today), licensed Apache-2.0. It adds 248 tokens to every session and 7,053 once invoked, about $0.0012 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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