gitlab-mcp is a service that lets AI agents interact with GitLab through the Model Context Protocol, an interface for exposing tools to agent clients. It supports work with projects, merge requests, issues, pipelines, wikis, releases, tags, and other GitLab resources through local or remote connections. The catalogue includes agents, skills, instructions, and an MCP entry for its workflows.
Borrowing it
Nothing to install: this file belongs to zereight/gitlab-mcp. 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/zereight/gitlab-mcp/main/.github/prompts/quick-review.prompt.mdgit clone --depth 1 https://github.com/zereight/gitlab-mcpWrote 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/commands/zereight/gitlab-mcp/quick-review)<a href="https://agentmods.dev/commands/zereight/gitlab-mcp/quick-review"><img src="https://agentmods.dev/badge/commands/zereight/gitlab-mcp/quick-review.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 | $0.00013 | $0.00158 |
| Opus 5 | $0.00006 | $0.00079 |
| Sonnet 5 | $0.00003 | $0.00032 |
| Haiku 4.5 | $0.00001 | $0.00016 |
Grade A, and why
quick-review 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.
What it actually says
Review the recent changes in this repository. Focus on:
- Logic defects — bugs, off-by-one errors, null/undefined risks
- Security issues — injection, auth bypass, secrets exposure
- Performance — N+1 queries, unnecessary re-renders, memory leaks
- Style — naming, consistency with existing codebase patterns
Rate each finding by severity: 🔴 Critical, 🟡 Warning, 🔵 Info.
Use git diff HEAD~1 to identify changes. If no recent commits, review staged changes with git diff --cached.
Keep feedback concise. Skip praise — focus on actionable improvements only.
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.
- today First seen · 18 lines · 13 tokens per session scan A 55db2ea86eb7
quick-review is a command published in the GitHub repository zereight/gitlab-mcp (1,953 stars, last pushed today), licensed MIT. It adds 13 tokens to every session and 158 once invoked, about $0.0001 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 2026-09-06.
Other commands, from other repositories
metrics-analysis
You are analyzing pull request metrics to identify opportunities for improving the development workflow.
Refactoring
Code refactoring prompt for improving code quality.
superpowers-status
Show the complete health of the project's AI Literacy habitat — harness enforcement, agent team, compound learning, model routing, and CI status.
review-and-refactor
You're a senior expert software engineer with extensive experience in maintaining projects over long time and ensuring clean code and best practices.
ADO: Create Pull Request
Create an Azure DevOps pull request with a structured description and linked work items.
change-models
Set, change, or audit model preferences for pipeline agents — interactive 6-mode workflow (global/workspace prefs, per-agent overrides, first-run wizard, catalog refresh).