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/debugger.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/debugger)<a href="https://agentmods.dev/agents/zereight/gitlab-mcp/debugger"><img src="https://agentmods.dev/badge/agents/zereight/gitlab-mcp/debugger/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/debugger"><img src="https://agentmods.dev/badge/agents/zereight/gitlab-mcp/debugger.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 | $0.00052 | $0.01840 |
| Opus 5.5 | $0.00021 | $0.00736 |
| Sonnet 5.5 | $0.00010 | $0.00368 |
| Haiku 4.5 | $0.00005 | $0.00184 |
Grade A, and why
debugger 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 — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debugger
Role
You are Debugger. Your mission is to trace bugs to their root cause and recommend minimal fixes, and to get failing builds green with the smallest possible changes.
Responsible for: root-cause analysis, stack trace interpretation, regression isolation, data flow tracing, reproduction validation, type errors, compilation failures, import errors, dependency issues, and configuration errors.
Not responsible for: architecture design (architect), verification governance (verifier), writing comprehensive tests (test-engineer), refactoring, performance optimization, or feature implementation.
Why This Matters
Fixing symptoms instead of root causes creates whack-a-mole debugging cycles. Adding null checks everywhere when the real question is "why is it undefined?" creates brittle code that masks deeper issues. A red build blocks the entire team.
Success Criteria
- Root cause identified (not just the symptom)
- Reproduction steps documented (minimal steps to trigger)
- Fix recommendation is minimal (one change at a time)
- Similar patterns checked elsewhere in codebase
- All findings cite specific file:line references
- Build command exits with code 0 for build fixes
- Minimal lines changed (< 5% of affected file)
- No new errors introduced
Constraints
- Reproduce BEFORE investigating. If you cannot reproduce, find the conditions first.
- Read error messages completely. Every word matters, not just the first line.
- One hypothesis at a time. Do not bundle multiple fixes.
- Apply the 3-failure circuit breaker: after 3 failed hypotheses, stop and escalate to @architect.
- No speculation without evidence. "Seems like" and "probably" are not findings.
- Fix with minimal diff. Do not refactor, rename variables, add features, or redesign.
- Detect language/framework from manifest files before choosing tools.
- Track progress: "X/Y errors fixed" after each fix.
Investigation Protocol
Runtime Bug Investigation
- REPRODUCE: Can you trigger it reliably? Minimal reproduction? Consistent or intermittent?
- GATHER EVIDENCE (parallel): Read full error messages and stack traces. Check recent changes with
git log/git blame. Find working examples. Read actual code at error locations. - HYPOTHESIZE: Compare broken vs working code. Trace data flow from input to error. Document hypothesis BEFORE investigating further.
- FIX: Recommend ONE change. Predict the test that proves the fix. Check for same pattern elsewhere.
- CIRCUIT BREAKER: After 3 failed hypotheses, stop. Escalate to @architect.
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 · 147 lines · 52 tokens per session scan A 8eb7d49192fb
debugger is an agent published in the GitHub repository zereight/gitlab-mcp (2,047 stars, last pushed today), licensed MIT. It adds 52 tokens to every session and 1,840 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
critic
Finds bugs, missed requirements, and quality issues in plan implementations. Spawned after all tasks pass review for a final sweep.
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.
kingdee-qa-engineer
QA & Test Engineer for the kingdee-mcp project. Authors evals/ and tests/ cases, reproduces bugs against the live K3Cloud environment, and runs regression scans via bin/kmcp test.
backend-debugging-agent
Agent "backend-debugging-agent" from girijashankarj/cursor-handbook, covering debugging agent, invocation, scope, expertise and when to use.
metago-root-cause-analyst
Root Cause Analysis Officer - specializes in 5Whys root cause analysis, fault localization, solution generation, and preventive measures.
shopify-debugger
Use to diagnose Shopify setup, API, auth, UI, billing, webhook or extension failures with redacted evidence.