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/ralph/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/ralph)<a href="https://agentmods.dev/skills/zereight/gitlab-mcp/ralph"><img src="https://agentmods.dev/badge/skills/zereight/gitlab-mcp/ralph/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/ralph"><img src="https://agentmods.dev/badge/skills/zereight/gitlab-mcp/ralph.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 | $0.00036 | $0.00688 |
| Opus 5.5 | $0.00014 | $0.00275 |
| Sonnet 5.5 | $0.00007 | $0.00138 |
| Haiku 4.5 | $0.00004 | $0.00069 |
Grade A, and why
ralph 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ralph
Ralph is a PRD-driven persistence loop that keeps working on a task until ALL user stories have passes: true and are reviewer-verified.
When to Use
- Task requires guaranteed completion with verification
- Work may span multiple iterations and needs persistence
- Task benefits from structured PRD-driven execution
When NOT to Use
- Full autonomous pipeline → use
/omg-autopilot - Explore or plan before committing → use
/plan - Quick one-shot fix → delegate to @executor
Flags
--no-prd: Skip PRD generation, work in legacy mode (for trivial fixes)--no-deslop: Skip the mandatory post-review cleanup pass
Steps
1. PRD Setup (first iteration)
- Check if
.omc/prd.jsonexists viaomg_read_prd - If none exists, generate a PRD scaffold with task-specific acceptance criteria
- CRITICAL: Replace generic criteria with specific, testable ones
- Initialize progress tracking
2. Pick Next Story
- Read PRD via
omg_read_prd - Select highest-priority story with
passes: false
3. Implement Current Story
- Delegate to @executor at appropriate complexity level
- If sub-tasks are discovered, add as new stories to PRD
4. Verify Acceptance Criteria
- For EACH criterion, verify with fresh evidence
- Run relevant checks (test, build, lint, typecheck)
- If any criterion NOT met, continue working
5. Mark Story Complete
- Set
passes: trueviaomg_update_story - Record progress in
progress.txt
6. Check PRD Completion
- Call
omg_check_completion - If NOT all complete, loop to Step 2
- If ALL complete, proceed to verification
7. Reviewer Verification
- @verifier checks against specific acceptance criteria from PRD
- @architect reviews for architectural soundness
7.5 Mandatory Cleanup Pass
- Unless
--no-deslop, run/ai-slop-cleaneron changed files only
7.6 Regression Re-verification
- Re-run all tests after cleanup pass
- Only proceed after regression tests pass
8. Completion
- On approval: run
/cancelfor clean exit - On rejection: fix issues, re-verify, loop back
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 · 86 lines · 36 tokens per session scan A 49195a0d126e
ralph is a skill published in the GitHub repository zereight/gitlab-mcp (2,049 stars, last pushed today), licensed MIT. It adds 36 tokens to every session and 688 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.
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