Borrowing it
Nothing to install: this file belongs to jmrplens/gitlab-mcp-server. 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/jmrplens/gitlab-mcp-server/main/.claude/agents/se-reviewer.agent.mdgit clone --depth 1 https://github.com/jmrplens/gitlab-mcp-serverWrote 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/jmrplens/gitlab-mcp-server/se-reviewer)<a href="https://agentmods.dev/agents/jmrplens/gitlab-mcp-server/se-reviewer"><img src="https://agentmods.dev/badge/agents/jmrplens/gitlab-mcp-server/se-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/jmrplens/gitlab-mcp-server/se-reviewer"><img src="https://agentmods.dev/badge/agents/jmrplens/gitlab-mcp-server/se-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 | $0.00000 | $0.00010 |
| Opus 5.5 | $0.00000 | $0.00004 |
| Sonnet 5.5 | $0.00000 | $0.00002 |
| Haiku 4.5 | $0.00000 | $0.00001 |
Grade A, and why
se-reviewer 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.
What it actually says
../../.github/agents/se-reviewer.agent.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 · 1 lines · 0 tokens per session scan A 130e9c9c7b32
se-reviewer is an agent published in the GitHub repository jmrplens/gitlab-mcp-server (46 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 10 tokens. 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
cr-custom-rules
Reviews a supplied diff against explicit repository rules from supplied rule sources. Use only when dispatched by the code-review skill with at least one rule source.
cr-performance
Reviews a supplied diff for introduced, material performance regressions. Use only when dispatched by the code-review skill.
cr-security
Reviews a supplied diff for introduced, practically exploitable security vulnerabilities. Use only when dispatched by the code-review skill.
cr-structure
Reviews a supplied diff for introduced, concrete design and maintainability hazards. Use only when dispatched by the code-review skill.
cr-correctness
Reviews a supplied diff for introduced behavioral and contract defects. Use only when dispatched by the code-review skill.
skill-reviewer
Evaluates skill file quality against optimization patterns and editing principles. Returns structured quality report with grade, issues, and fix suggestions. Use when reviewing created or modified skill content.