Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/zereight/gitlab-mcp/ultraworknpx skills add zereight/gitlab-mcp --skill ultraworkgit 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/ultrawork)<a href="https://agentmods.dev/skills/zereight/gitlab-mcp/ultrawork"><img src="https://agentmods.dev/badge/skills/zereight/gitlab-mcp/ultrawork.svg" alt="Measured on agentmods" 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 | $0.00035 | $0.00319 |
| Opus 5 | $0.00017 | $0.00160 |
| Sonnet 5 | $0.00007 | $0.00064 |
| Haiku 4.5 | $0.00003 | $0.00032 |
Grade A, and why
ultrawork 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 4d 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
1 near-identical copy found in the catalogue:
- ultrawork — 100% identical, 0 lines differ
What it actually says
Ultrawork
Parallel execution engine that runs multiple agents simultaneously for independent tasks. Component mode -- provides parallelism and smart model routing but not persistence or verification loops.
When to Use
- Multiple independent tasks can run simultaneously
- Need to delegate work to multiple agents at once
When NOT to Use
- Need guaranteed completion with verification → use
/ralph(includes ultrawork) - Full autonomous pipeline → use
/omg-autopilot(includes ralph which includes ultrawork) - Only one sequential task → delegate directly to @executor
Relationship
ralph (persistence wrapper)
└── includes: ultrawork (this skill)
omg-autopilot (autonomous execution)
└── includes: ralph
└── includes: ultrawork (this skill)
Steps
- Classify tasks by independence: parallel vs dependent
- Fire independent tasks simultaneously via @executor
- Run dependent tasks sequentially after prerequisites
- Background long operations (builds, installs, tests)
- Verify when all tasks complete (build passes, tests pass)
Execution Policy
- Fire ALL independent tasks simultaneously
- Run builds/tests in background
- Match task complexity to appropriate model routing
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.
- 4d ago First seen · 44 lines · 35 tokens per session scan A a99a0e12affe
ultrawork is a skill published in the GitHub repository zereight/gitlab-mcp (1,939 stars, last pushed 2d ago), licensed MIT. It adds 35 tokens to every session and 319 once invoked, about $0.0002 per session on Opus 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 skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…