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/skills/ai-slop-cleaner/SKILL.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/skills/zereight/gitlab-mcp/ai-slop-cleaner)<a href="https://agentmods.dev/skills/zereight/gitlab-mcp/ai-slop-cleaner"><img src="https://agentmods.dev/badge/skills/zereight/gitlab-mcp/ai-slop-cleaner/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/skills/zereight/gitlab-mcp/ai-slop-cleaner"><img src="https://agentmods.dev/badge/skills/zereight/gitlab-mcp/ai-slop-cleaner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00046 | $0.00333 |
| Opus 5.5 | $0.00018 | $0.00133 |
| Sonnet 5.5 | $0.00009 | $0.00067 |
| Haiku 4.5 | $0.00005 | $0.00033 |
Grade A, and why
ai-slop-cleaner 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 2mo 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- ai-slop-cleaner — 100% identical, 0 lines differ
- ai-slop-cleaner — 100% identical, 0 lines differ
What it actually says
AI Slop Cleaner
Clean AI-generated code slop without changing behavior. Deletion-first workflow with regression safety.
When to Use
- Code that works but feels bloated, repetitive, or over-abstracted
- Duplicate logic, dead code, wrapper layers, boundary leaks
--reviewfor reviewer-only mode (no changes, just findings)
When NOT to Use
- New feature build or product change
- Broad redesign
- Behavior is unclear and untestable
Principles
- Preserve behavior unless explicitly asked for changes
- Delete before refactoring — removal is safest
- Scope-bounded: only clean what was specified
- Regression-safe: verify tests pass after every change
Slop Signals
- Unnecessary wrapper functions
- Duplicated logic across files
- Over-abstracted single-use helpers
- Dead code / unreachable branches
- Excessive comments restating obvious code
- Premature abstractions (used only once)
Workflow
- Identify target files/scope
- Run existing tests (baseline)
- Apply deletions first (dead code, unused imports)
- Simplify remaining code (reduce nesting, merge duplicates)
- Re-run tests after each change
- Report what was cleaned and verification results
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.
- 2mo ago First seen · 45 lines · 46 tokens per session scan A 430910f14205
ai-slop-cleaner is a skill published in the GitHub repository zereight/gitlab-mcp (2,074 stars, last pushed today), licensed MIT. It adds 46 tokens to every session and 333 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.
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Trigger: new issue, bug report, triage, backlog, issue flood, community report, root cause, dead-end, blocked user. Attack issues by root class, never one-by-one; fixes must shrink the system, not grow it.
issue-root-resolution
Trigger: root audit, atacar la raíz, issue roots, backlog roots, mechanism map, deletion-driven fix, resolver issues de raíz, close outdated issues. Audit and resolve issue clusters by verified root cause.
rdd-defect-workflow
Trigger: RDD, receipt-driven development, review authority, receipt/lineage, correction/recovery, delivery gate/kill switch, bounded review defects. Guide work.
project-check
Existing project health scan — audits Infrastructure, Security, Quality, and Harness setup. Read-only. Use when: '/project-check', 'project health check', 'project audit', 'what\'s missing', 'analyze my project', 'check setup'. Ends with /project-init and /setup recommendations. NOT for new projects (use…
full-audit
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