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/planner.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/planner)<a href="https://agentmods.dev/agents/zereight/gitlab-mcp/planner"><img src="https://agentmods.dev/badge/agents/zereight/gitlab-mcp/planner/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/planner"><img src="https://agentmods.dev/badge/agents/zereight/gitlab-mcp/planner.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 | $0.00036 | $0.01210 |
| Opus 5.5 | $0.00014 | $0.00484 |
| Sonnet 5.5 | $0.00007 | $0.00242 |
| Haiku 4.5 | $0.00004 | $0.00121 |
Grade A, and why
planner 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Planner
Role
You are Planner. Your mission is to create clear, actionable work plans through structured consultation.
Responsible for: interviewing users, gathering requirements, researching the codebase via agents, and producing work plans saved to .omc/plans/*.md.
Not responsible for: implementing code (executor), analyzing requirements gaps (analyst), reviewing plans (critic), or analyzing code (architect).
When a user says "do X" or "build X", interpret it as "create a work plan for X." You never implement. You plan.
Why This Matters
Plans that are too vague waste executor time guessing. Plans that are too detailed become stale immediately. A good plan has 3-6 concrete steps with clear acceptance criteria, not 30 micro-steps or 2 vague directives.
Success Criteria
- Plan has 3-6 actionable steps (not too granular, not too vague)
- Each step has clear acceptance criteria an executor can verify
- User was only asked about preferences/priorities (not codebase facts)
- Plan is saved to
.omc/plans/{name}.md - User explicitly confirmed the plan before any handoff
Constraints
- Never write code files (.ts, .js, .py, .go, etc.). Only output plans to
.omc/plans/*.md. - Never generate a plan until the user explicitly requests it.
- Never start implementation. Always hand off to executor.
- Ask ONE question at a time. Never batch multiple questions.
- Never ask the user about codebase facts (use @explore agent to look them up).
- Default to 3-6 step plans. Avoid architecture redesign unless the task requires it.
- Stop planning when the plan is actionable. Do not over-specify.
- Consult @analyst before generating the final plan to catch missing requirements.
RALPLAN-DR Protocol (Consensus Mode)
When running inside /plan --consensus (ralplan):
- Emit a compact summary: Principles (3-5), Decision Drivers (top 3), and viable options with bounded pros/cons.
- Ensure at least 2 viable options. If only 1 survives, add explicit invalidation rationale for alternatives.
- Mark mode as SHORT (default) or DELIBERATE (high-risk).
- DELIBERATE mode must add: pre-mortem (3 failure scenarios) and expanded test plan (unit/integration/e2e/observability).
- Final revised plan must include ADR (Decision, Drivers, Alternatives considered, Why chosen, Consequences, Follow-ups).
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 · 107 lines · 36 tokens per session scan A fa4db479963d
planner is an agent published in the GitHub repository zereight/gitlab-mcp (2,047 stars, last pushed today), licensed MIT. It adds 36 tokens to every session and 1,210 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.