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/agento11yWrote 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)<a href="https://agentmods.dev/skills/grafana/gcx/agento11y"><img src="https://agentmods.dev/badge/skills/grafana/gcx/agento11y/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"><img src="https://agentmods.dev/badge/skills/grafana/gcx/agento11y.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 38 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 | $0.00066 | $0.02548 |
| Opus 5.5 | $0.00026 | $0.01019 |
| Sonnet 5.5 | $0.00013 | $0.00510 |
| Haiku 4.5 | $0.00007 | $0.00255 |
Grade A, and why
agento11y 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 6d 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 — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Observability
Agent Observability records what LLM-powered applications do in production and scores the quality of their output.
Applications send generations (individual LLM API calls — request, response, model, tokens, tool calls) to Agent Observability. Generations belonging to the same user session are grouped into a conversation.
Evaluators are scoring functions (LLM judge, regex, heuristic, JSON schema, etc.) that assess generation quality. Rules bind evaluators to production traffic, they select which generations to evaluate (e.g. only user-visible turns), filter by agent/model, and control sampling rate. When a rule matches a generation, Agent Observability runs the bound evaluators and writes scores.
All commands live under gcx agento11y. Use gcx agento11y <subcommand> --help for flags and usage.
Command Groups
| Group | Purpose |
|---|---|
conversations |
List, get, search conversations |
generations |
Get a single generation, list its scores |
agents |
List agents, get details, list version history (list-versions) |
evaluators |
List, get, upsert, delete, test evaluators |
rules |
List, get, create, update, delete evaluation rules; list-scores for online score rows |
templates |
List, get built-in evaluator templates |
judge |
List judge providers and models |
experiments |
List, get, create, update, cancel runs; inspect scores/reports/trials; pull experiment source bundles |
Delete commands (evaluators delete, rules delete) require --force to skip confirmation in agent mode (there is no -f shorthand on delete). List first to confirm the target ID:
gcx agento11y evaluators list
gcx agento11y evaluators delete <id> --force
gcx agento11y rules list
gcx agento11y rules delete <id> --force
Deleting an evaluator referenced by a rule may leave the rule pointing at a missing evaluator — check gcx agento11y rules list after.
Conversation Search
Defaults to last 24 hours. Filter syntax: key operator "value", space-separated.
What ships with it
2 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.
- 6d ago Changed · +2 lines a037bac9500a
- 14d ago Changed · -6 lines · -79 tokens per session 76160636704f
- 1mo ago Changed · +45 lines · +19 tokens per session 9a774ea3da9a
- 2mo ago First seen · 161 lines · 126 tokens per session scan A 35a37c52fe04
agento11y is a skill published in the GitHub repository grafana/gcx (817 stars, last pushed today), licensed Apache-2.0. It adds 66 tokens to every session and 2,548 once invoked, about $0.0003 per session on Opus 5.5. 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.
agento11y compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| agento11ygrafana/gcx | Skill | 817 | today | Apache-2.0 | A | 66 | Claude Code |
| imagegenopenai/codex | Skill | 133k | today | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| chat-pet-sprite-creationmicrosoft/vscode | Skill | 195k | today | MIT | A | 53 | Claude Code, Codex |