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.
Borrowing it
Nothing to install: this file belongs to grafana/gcx. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/grafana/gcx/main/.agents/skills/propose-feature/SKILL.mdgit 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/propose-feature)<a href="https://agentmods.dev/skills/grafana/gcx/propose-feature"><img src="https://agentmods.dev/badge/skills/grafana/gcx/propose-feature/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/propose-feature"><img src="https://agentmods.dev/badge/skills/grafana/gcx/propose-feature.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00038 | $0.00954 |
| Opus 5.5 | $0.00015 | $0.00382 |
| Sonnet 5.5 | $0.00008 | $0.00191 |
| Haiku 4.5 | $0.00004 | $0.00095 |
Grade A, and why
propose-feature 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 8d 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Propose Feature
Produce one proposal that a reader with no access to the conversation can understand and assess. Use the common Feature proposal form for requests, improvements and proposed commands. This skill ends with a reviewable draft or an authorized, filed issue.
Read the contribution workflow for when a proposal is needed and what follows acceptance. Use contribute when selecting the contribution route or preparing implementation. Follow that policy; a user may also request a proposal draft for work whose route does not require one.
The repository is public. Follow the anonymization rules in the GitHub Issues section of CLAUDE.md. Use public evidence or independently developed material suitable for public review. Keep restricted research outside the checkout; removing links or attribution does not make internal findings publishable.
Ground the idea
Establish the concrete user problem and investigate enough context to make the direction credible:
- Check VISION.md for product fit and CONSTITUTION.md for invariants.
- Read relevant code and existing command help to understand the current behavior and nearby capabilities.
- Search existing issues and PRs for duplicates or decisions to reuse.
- Investigate relevant public APIs or dependencies where they affect feasibility. Scale research to the proposal; state unknown API readiness, permissions, data availability or other feasibility gaps explicitly.
Separate observed facts, user-reported needs and proposed behavior. When evidence contradicts the idea or an invariant prevents it, explain the conflict and reshape the proposal before drafting. A proposal can carry unresolved feasibility questions for maintainer review.
Interview briefly
Reuse decisions and evidence already available. Ask only what changes the proposal: who has the problem, the scenario that shows it, where scope stops and what completion looks like. Use a small batch of questions with recommended answers and tradeoffs where useful; continue independent research while answers are pending.
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.
- 8d ago First seen · 69 lines · 38 tokens per session scan A 2795cc3defe9
propose-feature is a skill published in the GitHub repository grafana/gcx (805 stars, last pushed yesterday), licensed Apache-2.0. It adds 38 tokens to every session and 954 once invoked, about $0.0002 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 2026-10-21.
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