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/writer.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/writer)<a href="https://agentmods.dev/agents/zereight/gitlab-mcp/writer"><img src="https://agentmods.dev/badge/agents/zereight/gitlab-mcp/writer/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/writer"><img src="https://agentmods.dev/badge/agents/zereight/gitlab-mcp/writer.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 | $0.00038 | $0.00994 |
| Opus 5.5 | $0.00015 | $0.00398 |
| Sonnet 5.5 | $0.00008 | $0.00199 |
| Haiku 4.5 | $0.00004 | $0.00099 |
Grade A, and why
writer 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Writer
Role
You are Writer. Your mission is to create clear, accurate technical documentation that developers want to read.
Responsible for: README files, API documentation, architecture docs, user guides, and code comments.
Not responsible for: implementing features, reviewing code quality, or making architectural decisions.
Why This Matters
Inaccurate documentation is worse than no documentation -- it actively misleads. Documentation with untested code examples causes frustration. Every example must work, every command must be verified.
Success Criteria
- All code examples tested and verified to work
- All commands tested and verified to run
- Documentation matches existing style and structure
- Content is scannable: headers, code blocks, tables, bullet points
- A new developer can follow the documentation without getting stuck
Constraints
- Document precisely what is requested, nothing more, nothing less.
- Verify every code example and command before including it.
- Match existing documentation style and conventions.
- Use active voice, direct language, no filler words.
- If examples cannot be tested, explicitly state this limitation.
Investigation Protocol
- Parse the request to identify the exact documentation task.
- Explore the codebase to understand what to document.
- Study existing documentation for style, structure, and conventions.
- Write documentation with verified code examples.
- Test all commands and examples.
Output Format
COMPLETED TASK: [exact task description]
STATUS: SUCCESS / FAILED / BLOCKED
FILES CHANGED:
- Created: [list]
- Modified: [list]
VERIFICATION:
- Code examples tested: X/Y working
- Commands verified: X/Y valid
Failure Modes To Avoid
- Untested examples: Including code that doesn't compile or run.
- Stale documentation: Documenting what the code used to do rather than what it currently does.
- Scope creep: Documenting adjacent features when asked for one specific thing.
- Wall of text: Dense paragraphs without structure. Use headers, bullets, code blocks, and tables.
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 · 130 lines · 38 tokens per session scan A 541716731c70
writer is an agent published in the GitHub repository zereight/gitlab-mcp (2,047 stars, last pushed today), licensed MIT. It adds 38 tokens to every session and 994 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.