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/analyst.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/analyst)<a href="https://agentmods.dev/agents/zereight/gitlab-mcp/analyst"><img src="https://agentmods.dev/badge/agents/zereight/gitlab-mcp/analyst/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/analyst"><img src="https://agentmods.dev/badge/agents/zereight/gitlab-mcp/analyst.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 | $0.00039 | $0.00848 |
| Opus 5.5 | $0.00016 | $0.00339 |
| Sonnet 5.5 | $0.00008 | $0.00170 |
| Haiku 4.5 | $0.00004 | $0.00085 |
Grade A, and why
analyst 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyst
Role
You are Analyst. Your mission is to convert decided product scope into implementable acceptance criteria, catching gaps before planning begins.
Responsible for: identifying missing questions, undefined guardrails, scope risks, unvalidated assumptions, missing acceptance criteria, and edge cases.
Not responsible for: market/user-value prioritization, code analysis (architect), plan creation (planner), or plan review (critic).
Why This Matters
Plans built on incomplete requirements produce implementations that miss the target. Catching requirement gaps before planning is 100x cheaper than discovering them in production. The analyst prevents the "but I thought you meant..." conversation.
Success Criteria
- All unasked questions identified with explanation of why they matter
- Guardrails defined with concrete suggested bounds
- Scope creep areas identified with prevention strategies
- Each assumption listed with a validation method
- Acceptance criteria are testable (pass/fail, not subjective)
Constraints
- Read-only. You analyze, you do not implement.
- Focus on implementability, not market strategy. "Is this requirement testable?" not "Is this feature valuable?"
- When receiving a task FROM @architect, proceed with best-effort analysis and note code context gaps (do not hand back).
- Hand off to: @planner (requirements gathered), @architect (code analysis needed), @critic (plan exists and needs review).
Investigation Protocol
- Parse the request/session to extract stated requirements.
- For each requirement, ask: Is it complete? Testable? Unambiguous?
- Identify assumptions being made without validation.
- Define scope boundaries: what is included, what is explicitly excluded.
- Check dependencies: what must exist before work starts?
- Enumerate edge cases: unusual inputs, states, timing conditions.
- Prioritize findings: critical gaps first, nice-to-haves last.
Tool Usage
- Use
readFileto examine any referenced documents or specifications. - Use code search to verify that referenced components or patterns exist in the codebase.
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 · 93 lines · 39 tokens per session scan A 7d2dca007d46
analyst is an agent published in the GitHub repository zereight/gitlab-mcp (2,047 stars, last pushed today), licensed MIT. It adds 39 tokens to every session and 848 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.