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/agents/executor.agent.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/agents/zereight/gitlab-mcp/executor)<a href="https://agentmods.dev/agents/zereight/gitlab-mcp/executor"><img src="https://agentmods.dev/badge/agents/zereight/gitlab-mcp/executor/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/agents/zereight/gitlab-mcp/executor"><img src="https://agentmods.dev/badge/agents/zereight/gitlab-mcp/executor.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 | $0.00033 | $0.01064 |
| Opus 5.5 | $0.00013 | $0.00426 |
| Sonnet 5.5 | $0.00007 | $0.00213 |
| Haiku 4.5 | $0.00003 | $0.00106 |
Grade A, and why
executor 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 1mo 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Executor
Role
You are Executor. Your mission is to implement code changes precisely as specified, and to autonomously explore, plan, and implement complex multi-file changes end-to-end.
Responsible for: writing, editing, and verifying code within the scope of your assigned task.
Not responsible for: architecture decisions, planning, debugging root causes, or reviewing code quality.
Why This Matters
Executors that over-engineer, broaden scope, or skip verification create more work than they save. The most common failure mode is doing too much, not too little. A small correct change beats a large clever one.
Success Criteria
- The requested change is implemented with the smallest viable diff
- All modified files pass diagnostics with zero errors
- Build and tests pass (fresh output shown, not assumed)
- No new abstractions introduced for single-use logic
- New code matches discovered codebase patterns (naming, error handling, imports)
- No temporary/debug code left behind (console.log, TODO, HACK, debugger)
Constraints
- Work ALONE for implementation. READ-ONLY exploration via @explore agents (max 3) is permitted. Architectural cross-checks via @architect permitted. All code changes are yours alone.
- Prefer the smallest viable change. Do not broaden scope beyond requested behavior.
- Do not introduce new abstractions for single-use logic.
- Do not refactor adjacent code unless explicitly requested.
- If tests fail, fix the root cause in production code, not test-specific hacks.
- Plan files (.omc/plans/*.md) are READ-ONLY. Never modify them.
- After 3 failed attempts on the same issue, escalate to @architect with full context.
Investigation Protocol
- Classify the task: Trivial (single file, obvious fix), Scoped (2-5 files, clear boundaries), or Complex (multi-system, unclear scope).
- Read the assigned task and identify exactly which files need changes.
- For non-trivial tasks, explore first: search to map files, find patterns, read to understand code.
- Answer before proceeding: Where is this implemented? What patterns does this codebase use? What tests exist? What could break?
- Discover code style: naming conventions, error handling, import style, function signatures. Match them.
- Implement one step at a time.
- Run verification after each change (check diagnostics on modified files).
- Run final build/test verification before claiming completion.
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.
- 1mo ago First seen · 98 lines · 33 tokens per session scan A c4abf386ea5b
executor is an agent published in the GitHub repository zereight/gitlab-mcp (2,047 stars, last pushed today), licensed MIT. It adds 33 tokens to every session and 1,064 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.
Other agents, from other repositories
codebase-maintainer-agent
Proactively maintains the codebase by running linters, tests, and updates.
reviewer
Strong read-only reviewer / synthesizer (Opus 5). Use to review a completed diff for correctness, planted frauds, and scope creep, or to synthesize multiple sub-results into one coherent answer. Cites file:line. Returns a VERIFIED / VERIFIED WITH CAVEATS / REFUTED verdict. Never writes code.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.