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/omg-autopilot/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/omg-autopilot)<a href="https://agentmods.dev/skills/zereight/gitlab-mcp/omg-autopilot"><img src="https://agentmods.dev/badge/skills/zereight/gitlab-mcp/omg-autopilot/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/omg-autopilot"><img src="https://agentmods.dev/badge/skills/zereight/gitlab-mcp/omg-autopilot.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 | $0.00052 | $0.00703 |
| Opus 5.5 | $0.00021 | $0.00281 |
| Sonnet 5.5 | $0.00010 | $0.00141 |
| Haiku 4.5 | $0.00005 | $0.00070 |
Grade A, and why
omg-autopilot 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
2 near-identical copies found in the catalogue:
- omg-autopilot — 100% identical, 0 lines differ
- omg-autopilot — 88% identical, 71 lines differ
How it starts
The opening of the file, as written. The whole thing — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OMG Autopilot
OMG Autopilot takes a brief product idea and autonomously handles the full lifecycle: requirements analysis, technical design, planning, parallel implementation, QA cycling, and multi-perspective validation.
When to Use
- User wants end-to-end autonomous execution from an idea to working code
- Task requires multiple phases: planning, coding, testing, and validation
- User wants hands-off execution
When NOT to Use
- User wants to explore options → use
/plan - Single focused code change → use
/ralphor delegate to @executor - Quick fix or small bug → delegate directly to @executor
Execution Pipeline
Phase 0 - Expansion
Turn the user's idea into a detailed spec.
- If ralplan consensus plan exists (
.omc/plans/ralplan-*.md): Skip Phase 0 AND Phase 1 → jump to Phase 2 - If deep-interview spec exists (
.omc/specs/deep-interview-*.md): Use pre-validated spec, skip to Phase 1 - If input is vague: Offer redirect to
/deep-interviewfor clarification - Otherwise: @analyst extracts requirements, @architect creates technical specification
- Output:
.omc/autopilot/spec.md - Track phase:
omg_write_state(phase="expansion_done")
Phase 1 - Planning
Create an implementation plan from the spec.
- @architect creates plan (direct mode)
- @critic validates plan
- Output:
.omc/plans/autopilot-impl.md - Track:
omg_write_state(phase="planning_done")
Phase 2 - Execution
Implement the plan using parallel execution.
- Route tasks by complexity to @executor
- Run independent tasks in parallel
- Track:
omg_write_state(phase="execution_done")
Phase 3 - QA
Cycle until all tests pass (max 5 cycles).
- Build, lint, test, fix failures
- Stop early if the same error repeats 3 times (fundamental issue)
- Track:
omg_write_state(phase="qa_done")
Phase 4 - Validation
Multi-perspective review in parallel.
- @architect: Functional completeness
- @security-reviewer: Vulnerability check
- @code-reviewer: Quality review
- All must approve; fix and re-validate on rejection
- Track:
omg_write_state(phase="validation_done")
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 · 78 lines · 52 tokens per session scan A 44f80577a87f
omg-autopilot is a skill published in the GitHub repository zereight/gitlab-mcp (2,074 stars, last pushed today), licensed MIT. It adds 52 tokens to every session and 703 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 2026-08-30.
Other skills, from other repositories
gitlab-merge-request-review
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…
gitlab-api-development
Develop, test, gate, and release the GitLab REST and GraphQL connector. Use this skill when changing API mappings, governed MCP tools, authentication, tenant selection, KG ingestion, packaged content, or repository gates.
gitlab-pipelines
CI/CD pipeline review on GitLab via the gitlab-api MCP server — review pipeline results for a project, a group, or the whole instance, drill into the CI jobs of a pipeline, and read a job's log; also trigger a pipeline on a ref. Use when the agent must review CI results across a project/group/organization, check…
gitlab-vulnerabilities
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…