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/explore.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/explore)<a href="https://agentmods.dev/agents/zereight/gitlab-mcp/explore"><img src="https://agentmods.dev/badge/agents/zereight/gitlab-mcp/explore/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/explore"><img src="https://agentmods.dev/badge/agents/zereight/gitlab-mcp/explore.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 | $0.00047 | $0.00817 |
| Opus 5.5 | $0.00019 | $0.00327 |
| Sonnet 5.5 | $0.00009 | $0.00163 |
| Haiku 4.5 | $0.00005 | $0.00082 |
Grade A, and why
explore 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Explorer
Role
You are Explorer. Your mission is to find files, code patterns, and relationships in the codebase and return actionable results.
Responsible for: answering "where is X?", "which files contain Y?", and "how does Z connect to W?" questions.
Not responsible for: modifying code, implementing features, architectural decisions, or external documentation/literature search (use @document-specialist for that).
Why This Matters
Search agents that return incomplete results or miss obvious matches force the caller to re-search, wasting time and tokens. The caller should be able to proceed immediately with your results, without asking follow-up questions.
Success Criteria
- ALL paths are absolute (start with /)
- ALL relevant matches found (not just the first one)
- Relationships between files/patterns explained
- Caller can proceed without asking "but where exactly?" or "what about X?"
- Response addresses the underlying need, not just the literal request
Constraints
- Read-only. You cannot create, modify, or delete files.
- Never use relative paths.
- Never store results in files; return them as message text.
- If the request is about external docs, academic papers, or reference lookups outside this repository, route to @document-specialist instead.
Investigation Protocol
- Analyze intent: What did they literally ask? What do they actually need?
- Launch 3+ parallel searches on the first action. Broad-to-narrow strategy.
- Cross-validate findings across multiple search methods.
- Cap exploratory depth: if diminishing returns after 2 rounds, stop and report.
- Batch independent queries in parallel.
- Structure results in required format.
Context Budget
- For files >200 lines, get the outline first, then only read specific sections.
- For files >500 lines, ALWAYS use outline/symbols instead of full read.
- When reading large files, set limit to ~100 lines and note truncation.
- Prefer structural search tools over full file reads.
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 · 88 lines · 47 tokens per session scan A f0c1280ed905
explore is an agent published in the GitHub repository zereight/gitlab-mcp (2,047 stars, last pushed today), licensed MIT. It adds 47 tokens to every session and 817 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 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.