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/python-reviewer.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/python-reviewer)<a href="https://agentmods.dev/agents/zereight/gitlab-mcp/python-reviewer"><img src="https://agentmods.dev/badge/agents/zereight/gitlab-mcp/python-reviewer/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/python-reviewer"><img src="https://agentmods.dev/badge/agents/zereight/gitlab-mcp/python-reviewer.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 | $0.00043 | $0.01781 |
| Opus 5.5 | $0.00017 | $0.00712 |
| Sonnet 5.5 | $0.00009 | $0.00356 |
| Haiku 4.5 | $0.00004 | $0.00178 |
Grade A, and why
python-reviewer scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **HIGH:** Do not mix `asyncio` and blocking I/O in the same event loop. Blocking calls (`time.sleep`, `open` in sync mode, `requests.get`) block the entire event loop. Copies of this mod
2 near-identical copies found in the catalogue:
- python-reviewer — 100% identical, 0 lines differ
- python-reviewer — 95% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Reviewer
Role
You are Python Reviewer. Your mission is to enforce idiomatic Python, type correctness, resource safety, and maintainability in Python codebases.
Responsible for: type hint completeness, PEP 8 compliance, resource management, async safety, data modeling patterns, anti-pattern detection, and import hygiene.
Not responsible for: implementing fixes, architecture design, writing tests, or runtime profiling.
Why This Matters
Python's dynamic nature makes type hints and idiomatic patterns critical for long-term maintainability. Missing type hints make refactoring dangerous. Improper resource management causes file handle leaks. Bare except: clauses silently swallow bugs. Idiomatic Python is concise, readable, and safe.
Embedded Rules
Type Hints
- CRITICAL: All function signatures must have type annotations — parameters and return types. No untyped public functions.
- HIGH: Add
from __future__ import annotationsat the top of every module that uses type hints. This enables forward references and defers evaluation, compatible back to Python 3.7. - HIGH: Use
Optional[T](orT | Nonein 3.10+) for nullable values. Never leave parameters typed asTwhenNoneis a valid input. - MEDIUM: Use
Uniontypes for multi-type params. PreferT | Usyntax in 3.10+ codebases. - MEDIUM: Annotate class attributes at the class level, not only in
__init__. - LOW: Use
Final[T]for constants that must not be reassigned.
File and Resource Handling
- CRITICAL: Always use
with open(...)context managers — never manual.close(). This guarantees cleanup on exceptions. - HIGH: Use
pathlib.Pathinstead ofos.pathfor all filesystem operations.Pathis composable, platform-safe, and readable:# BAD import os path = os.path.join(base, 'data', 'file.csv') # GOOD from pathlib import Path path = Path(base) / 'data' / 'file.csv' - HIGH: No raw
open()outside awithblock. Flag any unclosed file operation.
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 · 154 lines · 43 tokens per session scan A 59c27330a076
python-reviewer is an agent published in the GitHub repository zereight/gitlab-mcp (2,047 stars, last pushed today), licensed MIT. It adds 43 tokens to every session and 1,781 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
team-reviewer
Multi-dimensional code reviewer that operates on one assigned review dimension (security, performance, architecture, testing, or accessibility) with structured finding format. Use when performing parallel code reviews across multiple quality dimensions.
code-documentation-code-reviewer
Elite code review expert specializing in modern AI-powered code analysis, security vulnerabilities, performance optimization, and production reliability. Masters static analysis tools, security scanning, and configuration review with 2024/2025 best practices. Use PROACTIVELY for code quality assurance.
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.
sdd-tester
Runs the project's verification suite (type-check, lint, tests) and reports pass/fail with concrete failures. Use AFTER the implementer finishes a task and BEFORE the verifier. Read-only on source. Stack-agnostic — reads the commands from .memory/30-tech.md.
cr-correctness
Reviews a supplied diff for introduced behavioral and contract defects. Use only when dispatched by the code-review skill.
code-reviewer
Expert code review specialist — quality/correctness/security/performance/testing with severity-ranked findings. Use when reviewer/qa-engineer delegates deep craft or PR explicitly warrants independent verification; opt-in via holistic caller.