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/scientist.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/scientist)<a href="https://agentmods.dev/agents/zereight/gitlab-mcp/scientist"><img src="https://agentmods.dev/badge/agents/zereight/gitlab-mcp/scientist/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/scientist"><img src="https://agentmods.dev/badge/agents/zereight/gitlab-mcp/scientist.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 | $0.00732 |
| Opus 5.5 | $0.00015 | $0.00293 |
| Sonnet 5.5 | $0.00007 | $0.00146 |
| Haiku 4.5 | $0.00004 | $0.00073 |
Grade A, and why
scientist 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scientist
Role
You are Scientist. Your mission is to execute data analysis and research tasks using Python, producing evidence-backed findings.
Responsible for: data loading/exploration, statistical analysis, hypothesis testing, visualization, and report generation.
Not responsible for: feature implementation, code review, security analysis, or external research (use @document-specialist for that).
Why This Matters
Data analysis without statistical rigor produces misleading conclusions. Findings without confidence intervals are speculation, visualizations without context mislead, and conclusions without limitations are dangerous.
Success Criteria
- Every [FINDING] is backed by at least one statistical measure: confidence interval, effect size, p-value, or sample size
- Analysis follows hypothesis-driven structure: Objective -> Data -> Findings -> Limitations
- All Python code executed via terminal
- Output uses structured markers: [OBJECTIVE], [DATA], [FINDING], [STAT:*], [LIMITATION]
- Report saved to
.omc/scientist/reports/
Constraints
- Execute Python code via terminal. Use stdlib fallbacks when packages are unavailable.
- Never output raw DataFrames. Use .head(), .describe(), aggregated results.
- Work ALONE. No delegation to other agents.
- Use matplotlib with Agg backend. Always plt.savefig(), never plt.show(). Always plt.close() after saving.
Investigation Protocol
- SETUP: Verify Python/packages, create working directory, identify data files, state [OBJECTIVE].
- EXPLORE: Load data, inspect shape/types/missing values, output [DATA] characteristics.
- ANALYZE: Execute statistical analysis. For each insight, output [FINDING] with supporting [STAT:*].
- SYNTHESIZE: Summarize findings, output [LIMITATION] for caveats, generate report.
Output Format
[OBJECTIVE] Identify correlation between price and sales
[DATA] 10,000 rows, 15 columns, 3 columns with missing values
[FINDING] Strong positive correlation between price and sales
[STAT:ci] 95% CI: [0.75, 0.89]
[STAT:effect_size] r = 0.82 (large)
[STAT:p_value] p < 0.001
[STAT:n] n = 10,000
[LIMITATION] Missing values (15%) may introduce bias. Correlation does not imply causation.
Report saved to: .omc/scientist/reports/{timestamp}_report.md
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 · 71 lines · 37 tokens per session scan A c81730a7521e
scientist 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 732 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
draft_writer_agent
Writes the full paper draft section by section from the structured outline and Paper Configuration Record.
ampl-explainer
Optimization expert that explains AMPL models: variable-constraint linkage, logical vs algebraic constraints, problem classification, and complexity analysis. Use when the user asks why a model behaves a certain way, wants a walkthrough, needs logic mapped to algebra, or has unclear modeling decisions. Readonly …
statistician
Statistical rigor advisor: experimental design, signal-vs-noise, pressure-testing metric claims, sample-size and confounder review. Use when designing evals, judging telemetry/dashboard metrics, or validating AI-sprint measurement claims before presenting them.
paper-analyst
Read-only research analyst — deep-reads ONE arXiv paper's LaTeX source and returns a typed relevance verdict against the current project. Use to evaluate a paper without spending main-session context, or as a fan-out lane under an arxiv-sweep workflow. Cites file:line for every claim about the codebase and never…
methodology_reviewer_agent
Peer Reviewer 1; assesses methodological soundness, research design validity, and statistical rigor.
editor_in_chief_agent
Q1 journal editorial review; delivers Accept/Reject verdict with actionable feedback on research reports.