Ginkgo is a Go testing framework for writing organized, expressive specifications, including unit, integration, and performance tests. Go developers use it with the Gomega matcher library to describe behavior and run test suites.
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 skills add onsi/ginkgo --skill ordering-and-flakesgit clone --depth 1 https://github.com/onsi/ginkgoWrote 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/onsi/ginkgo/ordering-and-flakes)<a href="https://agentmods.dev/skills/onsi/ginkgo/ordering-and-flakes"><img src="https://agentmods.dev/badge/skills/onsi/ginkgo/ordering-and-flakes/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/onsi/ginkgo/ordering-and-flakes"><img src="https://agentmods.dev/badge/skills/onsi/ginkgo/ordering-and-flakes.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Excessive Agency · line 83 Skill allows unbounded resource consumption (API calls, storage, compute). Without rate limits or quotas, a compromised or misbehaving agent can cause denial-of-service or cost overruns.Fix: Set explicit rate limits, timeouts, and resource quotas for API calls, file operations, and compute. Implement circuit breakers for runaway loops.
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 | $0.00111 | $0.01639 |
| Opus 5 | $0.00056 | $0.00820 |
| Sonnet 5 | $0.00022 | $0.00328 |
| Haiku 4.5 | $0.00011 | $0.00164 |
Grade A, and why
ordering-and-flakes 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 9d 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ordering and flakes
Two topics that share a root cause: specs that aren't truly independent (ginkgo:overview). Ordering decorators let you declare a dependency on purpose; flake controls let you manage or expose one. Docs: https://onsi.github.io/ginkgo/#serial-specs, https://onsi.github.io/ginkgo/#ordered-containers, https://onsi.github.io/ginkgo/#repeating-spec-runs-and-managing-flaky-specs.
Serial: opt out of parallelism
Serial (container or subject) guarantees a spec never runs in parallel with anything else. Under the hood Ginkgo runs serial specs last, on process #1, after all other processes have exited. Reach for it sparingly. OK for benchmarks, resource hogs, or specs that put an external resource into a known-bad state - but not OK as a band-aid for order-dependence or poorly isolated code/specs.
Describe("Something expensive", Serial, func() {
It("is a resource hog that can't share the box", func() { ... })
})
→ ginkgo:decorators, ginkgo:parallelism.
Ordered: run specs in definition order
Ordered (container only) guarantees its child specs run sequentially, in written order, on one process — so they may legitimately mutate shared closure state. They can still parallelize against specs in other containers; only the inner order is pinned.
Describe("checking out a book", Ordered, func() {
var libraryClient *library.Client
var book *books.Book
BeforeAll(func() { // once, before the first spec
libraryClient = library.NewClient()
Expect(libraryClient.Connect()).To(Succeed())
DeferCleanup(libraryClient.Disconnect) // context-aware: behaves like AfterAll
})
It("can fetch a book", func() { book, _ = libraryClient.FetchByTitle("Les Miserables") })
It("can check it out", func() { Expect(library.CheckOut(book)).To(Succeed()) })
It("is then out of stock", func() { ... })
AfterAll(func() { ... }) // once, after the last spec
})
BeforeAll/AfterAllare legal only inside anOrderedcontainer (or a container nested within one). That's the whole point ofOrdered— once-per-group expensive setup.- A failing spec skips the rest of the group by default (then
AfterAllstill runs). Spec independence is gone, so Ginkgo won't pretend the later specs are meaningful. ContinueOnFailure(outermostOrderedonly — error on a nested container) overrides that: keep running later specs after a failure. Use it whenOrderedis just shared setup, not a dependent flow. A failedBeforeAllstill skips everything — the setup is presumed broken.- Nested containers inside an
Orderedcontainer are automaticallyOrdered; there's no way to un-order them.
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.
- 9d ago First seen · 104 lines · 111 tokens per session scan A 493d36508871
ordering-and-flakes is a skill published in the GitHub repository onsi/ginkgo (9,051 stars, last pushed 28d ago), licensed MIT. It adds 111 tokens to every session and 1,639 once invoked, about $0.0006 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.
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