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 agentmods add skills/grafana/gcx/slo-optimizenpx skills add grafana/gcx --skill slo-optimizegit 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/slo-optimize)<a href="https://agentmods.dev/skills/grafana/gcx/slo-optimize"><img src="https://agentmods.dev/badge/skills/grafana/gcx/slo-optimize.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 | $0.00127 | $0.02867 |
| Opus 5 | $0.00063 | $0.01434 |
| Sonnet 5 | $0.00025 | $0.00573 |
| Haiku 4.5 | $0.00013 | $0.00287 |
Grade A, and why
slo-optimize 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 — 291 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SLO Optimizer
Analyze SLO timeline trends, compute statistics over the past 28 days, and generate advisory recommendations backed by real metric values. Never modify SLO definitions directly — route to slo-manage when the user wants to apply a recommendation.
Core Principles
- Use gcx commands exclusively — do not call Grafana APIs directly.
- Trust the user's expertise — skip explanations of what SLOs or burn rates are.
- Use
-o jsonfor agent processing of structured output; default format for user display. - Show graph output for timeline data so the user can see the trend visually.
- Every recommendation MUST include supporting data (current values, projected values, or historical comparisons). No generic advice without numbers.
- This skill is advisory only. Route to slo-manage for any changes the user wants to apply.
Prerequisites
gcx configured with a context pointing to the target Grafana instance.
If the user does not supply a UUID, list available SLOs first:
gcx slo definitions list
Ask the user which SLO to analyze if the target is ambiguous.
Optimization Workflow
Step 1: Retrieve SLO Definition
gcx slo definitions get <UUID> -o json
Extract and note:
spec.name— display namespec.objectives[0].value— current objective (e.g., 0.999)spec.objectives[0].window— compliance window (e.g., 28d)spec.query.type— ratio | freeform | thresholdspec.query.ratio.groupByLabels— dimensional labels (may be empty)spec.alerting— fastBurn / slowBurn configuration (may be absent)spec.destinationDatasource.uid— datasource UID for metric queries
Step 2: Fetch 28-Day Timeline
# Default graph output for user display
gcx slo definitions timeline <UUID> --from now-28d --to now
# JSON output for statistical analysis
gcx slo definitions timeline <UUID> --from now-28d --to now -o json
Parse the JSON output to extract SLI values across the time series. Compute:
mean_sli— average SLI over the 28-day windowmin_sli— lowest observed SLI pointmax_sli— highest observed SLI pointstd_dev— variability indicator
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 First seen · 291 lines · 127 tokens per session scan A 4593e61a2ee2
slo-optimize is a skill published in the GitHub repository grafana/gcx (586 stars, last pushed yesterday), licensed Apache-2.0. It adds 127 tokens to every session and 2,867 once invoked, about $0.0006 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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