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/skills/ralplan/SKILL.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/skills/zereight/gitlab-mcp/ralplan)<a href="https://agentmods.dev/skills/zereight/gitlab-mcp/ralplan"><img src="https://agentmods.dev/badge/skills/zereight/gitlab-mcp/ralplan/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/zereight/gitlab-mcp/ralplan"><img src="https://agentmods.dev/badge/skills/zereight/gitlab-mcp/ralplan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00028 | $0.00340 |
| Opus 5.5 | $0.00011 | $0.00136 |
| Sonnet 5.5 | $0.00006 | $0.00068 |
| Haiku 4.5 | $0.00003 | $0.00034 |
Grade A, and why
ralplan 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 2mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- ralplan — 100% identical, 0 lines differ
What it actually says
Ralplan (Consensus Planning)
Shorthand for /plan --consensus. Triggers iterative planning with Planner, Architect, and Critic agents until consensus is reached.
Flags
--deliberate: Forces deliberate mode for high-risk work. Adds pre-mortem (3 scenarios) and expanded test planning.
Workflow
- @planner creates initial plan with RALPLAN-DR summary:
- Principles (3-5)
- Decision Drivers (top 3)
- Viable Options (>=2) with pros/cons
- @architect reviews for architectural soundness
- @critic validates quality and testability
- Loop until critic approves (max 5 iterations)
- Final plan includes ADR (Decision, Drivers, Alternatives, Why chosen, Consequences)
Pre-Execution Gate
Vague execution requests (e.g., "ralph improve the app") are redirected through ralplan first to ensure explicit scope, testable criteria, and multi-agent consensus.
Passes gate (specific enough): prompts with file paths, function names, issue numbers, numbered steps, or acceptance criteria.
Gated (needs scoping): prompts with only vague descriptions and no concrete anchors.
After Approval
- Execute via
/team(parallel agents, recommended) or/ralph(sequential with verification)
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.
- 2mo ago First seen · 35 lines · 28 tokens per session scan A e5d50ce77319
ralplan is a skill published in the GitHub repository zereight/gitlab-mcp (2,074 stars, last pushed today), licensed MIT. It adds 28 tokens to every session and 340 once invoked, about $0.0001 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.
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Review and merge GitLab merge requests via the gitlab-api MCP server — by default the MRs assigned to you or where you are a reviewer, optionally scoped to a project, group, or the whole instance. Read an MR, gate it on a green pipeline (pipeline → jobs → job log), then approve, accept/merge, or set…
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Dependency and security-vulnerability review on GitLab via the gitlab-api MCP server — the GitLab counterpart to GitHub Dependabot. Read a project's Dependency List, review a project's or group's security vulnerabilities/findings, and fetch a single vulnerability by ID. Use when the agent must audit a project's…
gitlab-issues
Issue review and tracking on GitLab via the gitlab-api MCP server — review a project's issues, review issues across a whole group/organization, and read, create, update, or close an issue with the domain-typed tool. Use when the agent must triage a project's issues, see every issue affecting a group, open a new issue…
gitlab-merge-request-create
Open a new GitLab merge request against a project via the gitlab-api MCP server — from a source branch into a target branch, with title, description, assignees, reviewers, labels, and remove-source-branch/squash options. Use when the agent must create/open an MR for a project. Do NOT use to review, approve, or merge…