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/cancel/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/cancel)<a href="https://agentmods.dev/skills/zereight/gitlab-mcp/cancel"><img src="https://agentmods.dev/badge/skills/zereight/gitlab-mcp/cancel/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/cancel"><img src="https://agentmods.dev/badge/skills/zereight/gitlab-mcp/cancel.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 72 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
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 | $0.00037 | $0.00736 |
| Opus 5.5 | $0.00015 | $0.00294 |
| Sonnet 5.5 | $0.00007 | $0.00147 |
| Haiku 4.5 | $0.00004 | $0.00074 |
Grade A, and why
cancel 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.
How it starts
The opening of the file, as written. The whole thing — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cancel
Intelligent cancellation that detects and cancels the active OMG mode.
What It Does
Automatically detects which mode is active and cancels it:
- OMG Autopilot: Stops workflow, preserves progress for resume
- Ralph: Stops persistence loop
- Ultrawork: Stops parallel execution
- UltraQA: Stops QA cycling workflow
- Team: Sends shutdown to all teammates, cleans up
- Self-Improve: Stops loop, preserves iteration state for resume
Usage
/cancel
/cancel --force
/cancel --all
Auto-Detection
Reads .omc/state/ to discover which mode is active. Cancels in dependency order:
- OMG Autopilot (includes linked ralph/ultraqa cleanup)
- Ralph (cleans linked ultrawork)
- Ultrawork (standalone)
- UltraQA (standalone)
- Team (graceful shutdown + cleanup)
- Self-Improve (preserve iteration state)
Workflow
1. Parse Arguments
Check for --force or --all flags.
2. Detect Active Modes
Use omg_read_state to check each mode's state file.
3A. Force Mode (--force or --all)
Clear ALL state files in .omc/state/. Nuclear option.
3B. Smart Cancellation (default)
If OMG Autopilot Active
- Check for linked ralph → clear ultrawork if linked → clear ralph
- Check for linked ultraqa → clear it
- Mark omg-autopilot inactive (preserve state for resume)
If Ralph Active (not OMG Autopilot)
- Check for linked ultrawork → clear if linked
- Clear ralph state
If Team Active
- Signal shutdown to all teammates
- Wait for responses (15s timeout per member)
- Clean up team resources
- Clear linked ralph if present
If UltraQA / Ultrawork / Self-Improve Active
Clear state directly.
No Active Modes
Report: "No active OMG modes detected. Use --force to clear all state."
What Gets Preserved
| Mode | State Preserved | Resume Command |
|---|---|---|
| OMG Autopilot | Yes (phase, files, spec) | /omg-autopilot |
| Self-Improve | Yes (iteration state) | /self-improve --resume |
| Ralph | No | N/A |
| Others | No | N/A |
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 · 98 lines · 37 tokens per session scan A a957d2c23089
cancel is a skill published in the GitHub repository zereight/gitlab-mcp (2,074 stars, last pushed today), licensed MIT. It adds 37 tokens to every session and 736 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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