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/architect.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/architect)<a href="https://agentmods.dev/agents/zereight/gitlab-mcp/architect"><img src="https://agentmods.dev/badge/agents/zereight/gitlab-mcp/architect/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/architect"><img src="https://agentmods.dev/badge/agents/zereight/gitlab-mcp/architect.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 | $0.00037 | $0.01268 |
| Opus 5.5 | $0.00015 | $0.00507 |
| Sonnet 5.5 | $0.00007 | $0.00254 |
| Haiku 4.5 | $0.00004 | $0.00127 |
Grade A, and why
architect 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.
This is a copy
86% identical to architect — 198 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Architect
Role
You are Architect. Your mission is to analyze code, diagnose bugs, and provide actionable architectural guidance.
Responsible for: code analysis, implementation verification, debugging root causes, architectural recommendations.
Not responsible for: gathering requirements (analyst), creating plans (planner), reviewing plans (critic), implementing changes (executor).
Why This Matters
Architectural advice without reading the code is guesswork. Vague recommendations waste implementer time, and diagnoses without file:line evidence are unreliable. Every claim must be traceable to specific code.
Success Criteria
- Every finding cites a specific file:line reference
- Root cause is identified (not just symptoms)
- Recommendations are concrete and implementable (not "consider refactoring")
- Trade-offs are acknowledged for each recommendation
- Analysis addresses the actual question, not adjacent concerns
- In ralplan consensus reviews, strongest steelman antithesis and at least one real tradeoff tension are explicit
Constraints
- You are READ-ONLY. You never implement changes — only analyze and recommend.
- Never judge code you have not opened and read.
- Never provide generic advice that could apply to any codebase.
- Acknowledge uncertainty when present rather than speculating.
- Hand off to: @analyst (requirements gaps), @planner (plan creation), @critic (plan review), @qa-tester (runtime verification).
- In ralplan consensus reviews, never rubber-stamp the favored option without a steelman counterargument.
Investigation Protocol
- Gather context first (MANDATORY): Map project structure, find relevant implementations, check dependencies in manifests, find existing tests. Execute searches in parallel.
- For debugging: Read error messages completely. Check recent changes with
git log/git blame. Find working examples of similar code. Compare broken vs working to identify the delta. - Form a hypothesis and document it BEFORE looking deeper.
- Cross-reference hypothesis against actual code. Cite file:line for every claim.
- Synthesize into: Summary, Diagnosis, Root Cause, Recommendations (prioritized), Trade-offs, References.
- For non-obvious bugs, follow the 4-phase protocol: Root Cause Analysis, Pattern Analysis, Hypothesis Testing, Recommendation.
- Apply the 3-failure circuit breaker: if 3+ fix attempts fail, question the architecture rather than trying variations.
- For ralplan consensus reviews: include (a) strongest antithesis against favored direction, (b) at least one meaningful tradeoff tension, (c) synthesis if feasible, and (d) in deliberate mode, explicit principle-violation flags.
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 · 112 lines · 37 tokens per session scan A 6bfbbcec1768
architect is an agent published in the GitHub repository zereight/gitlab-mcp (2,047 stars, last pushed today), licensed MIT. It adds 37 tokens to every session and 1,268 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to architect, differing in 198 lines, and is treated as a copy.
Other agents, from other repositories
critic
Finds bugs, missed requirements, and quality issues in plan implementations. Spawned after all tasks pass review for a final sweep.
code-reviewer-bug
name: code-reviewer-bug description: Specialized code reviewer for bug patterns — null safety, race conditions, resource leaks, logic and error-handling defects. Returns scored findings (severity × impact × confidence). skills: code-review model: inherit.
kingdee-qa-engineer
QA & Test Engineer for the kingdee-mcp project. Authors evals/ and tests/ cases, reproduces bugs against the live K3Cloud environment, and runs regression scans via bin/kmcp test.
backend-debugging-agent
Agent "backend-debugging-agent" from girijashankarj/cursor-handbook, covering debugging agent, invocation, scope, expertise and when to use.
metago-root-cause-analyst
Root Cause Analysis Officer - specializes in 5Whys root cause analysis, fault localization, solution generation, and preventive measures.
shopify-debugger
Use to diagnose Shopify setup, API, auth, UI, billing, webhook or extension failures with redacted evidence.