gcx: Skill for Claude Code

.claude/skills/review-pr/SKILL.md

review-pr is a skill for Claude Code from grafana/gcx. It costs 95 tokens per session (3,325 once invoked), scanned A, original, Apache-2.0.

A pull-request review workflow for checking code written by someone else and producing a ranked report with a verdict. It can also turn findings into inline comments on GitHub.

In plain words
What is it for?
Use it to review a gcx pull request, combine automated and repository-specific checks, rank issues, and optionally post comments anchored to changed lines on GitHub.
Why use it?
It gives reviewers a defined order for checking correctness, compliance, duplication, and unnecessary complexity. This keeps findings consistent and avoids sending incomplete or duplicate feedback.

Skill for Claude Code ✓ vendor

Written for Claude Code: installed under .claude/. Also seen: reads .claude/ paths; mentions AGENTS.md.

This is grafana/gcx's own configuration. It tells Claude Code how to work on gcx itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything gcx configures →

About the project

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.

grafana/gcx · 594 stars · on GitHub · grafana.com

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/grafana/gcx/main/.claude/skills/review-pr/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/grafana/gcx

Made for: Claude Code.

Wrote 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.

agentmods badge for review-pr

README.md
[![agentmods](https://agentmods.dev/badge/skills/grafana/gcx/review-pr/github.svg)](https://agentmods.dev/skills/grafana/gcx/review-pr)
Your own site
<a href="https://agentmods.dev/skills/grafana/gcx/review-pr"><img src="https://agentmods.dev/badge/skills/grafana/gcx/review-pr/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.

agentmods 80×15 button for review-pr

Your own site · 80×15
<a href="https://agentmods.dev/skills/grafana/gcx/review-pr"><img src="https://agentmods.dev/badge/skills/grafana/gcx/review-pr.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 95 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,325 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
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 141
    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.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00095 $0.03325
Opus 5 $0.00048 $0.01663
Sonnet 5 $0.00019 $0.00665
Haiku 4.5 $0.00010 $0.00332

Measured today against content hash 756ff9e3be7e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

review-pr 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 today.

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.

.claude/skills/review-pr/SKILL.md · 296 lines

How it starts

The opening of the file, as written. The whole thing — 296 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Reviewing a gcx pull request

Use this skill to review work you did not write. It owns the report: what goes in it, in what order, and when to stop.

It defines no checks of its own. Run the checks where they already live:

  • .claude/skills/integrate-with-gcx/references/self-review.md. Read Evidence discipline first. It sets what a finding may conclude. Then run the triggers that fire for the diff. T12 is the over-engineering rubric. T5 covers code that repeats something the repo already has.
  • AGENTS.md, for the compliance hierarchy. Check all four levels in order.

Authors run the same triggers before they push. Assume they did. Treat what you find as missed, not as dismissed.

Two passes, one set of findings

Run both passes, then combine them:

  1. /code-review, for correctness bugs. Do not pass --comment. The findings must return to you instead of going straight to the PR, or you have nothing left to combine. It signs off with "no --comment argument was provided, so stopping here without posting" — that is it handing findings back, not the end of your review. Do not repeat the line or stop on it.
  2. The triggers above that fire for this diff, plus the compliance hierarchy.

Reconcile the two sets before you report anything. Remove duplicates. Keep whichever version of a finding states the failure more precisely.

The two passes can disagree. One calls a line a bug and the other calls the same line correct. Settle it against the code and report one conclusion. Never report both and leave the author to decide.

The same skill runs on a developer's machine and in the review workflow. A PR gets the same treatment either way.

What blocks a merge here

The severity split matters more than any single finding. It decides what the author must act on. Reserve the blocking tier for four things.

Read the full file on GitHub · 296 lines

Changes

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.

  1. today Changed · +29 lines 756ff9e3be7e
  2. 10d ago First seen · 267 lines · 95 tokens per session scan A 8fa6416d3088

Subscribe to this mod's changes

review-pr is a skill published in the GitHub repository grafana/gcx (594 stars, last pushed today), licensed Apache-2.0. It adds 95 tokens to every session and 3,325 once invoked, about $0.0005 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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