overview

overview is a skill for Claude Code, Codex from onsi/ginkgo. It costs 84 tokens per session (1,280 once invoked), scanned A, original, MIT.

A guide to Ginkgo, a Go testing framework that lets you organize tests into nested descriptions and examples. It explains that Ginkgo first builds the test structure, then runs each test.

In plain words
What is it for?
Use it when starting with Ginkgo or deciding how to write test specifications, setup, cleanup, and assertions.
Why use it?
It prevents mistakes caused by confusing test setup with test execution, and explains how to keep tests independent.

Skill for Claude CodeCodex

Part of the ginkgo plugin — 13 skills shipped together

Install

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.

agentmods
npx agentmods add skills/onsi/ginkgo/overview
Any agent
npx skills add onsi/ginkgo --skill overview
Clone the repo
git clone --depth 1 https://github.com/onsi/ginkgo

Made for: Claude Code, Codex.

Or install ginkgo, the plugin that ships this one along with the rest of its 13 skills.

Wrote 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.

agentmods badge for overview

README.md
[![agentmods](https://agentmods.dev/badge/skills/onsi/ginkgo/overview.svg)](https://agentmods.dev/skills/onsi/ginkgo/overview)
Your own site
<a href="https://agentmods.dev/skills/onsi/ginkgo/overview"><img src="https://agentmods.dev/badge/skills/onsi/ginkgo/overview.svg" alt="Measured on agentmods" height="20"></a>
Per session 84 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,280 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00084 $0.01280
Opus 5 $0.00042 $0.00640
Sonnet 5 $0.00017 $0.00256
Haiku 4.5 $0.00008 $0.00128

Measured 4d ago against content hash 9b0de0a29ee3, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

overview 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.

plugins/ginkgo/skills/overview/SKILL.md · 57 lines

How it starts

The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Ginkgo: the mental model

Ginkgo is an expressive BDD-style testing framework for Go, paired with the Gomega matcher library. You build suites out of nested container nodes (Describe/Context/When) and subject nodes (It), with setup nodes (BeforeEach/AfterEach/…) supplying state. You drive it with the ginkgo CLI. Strongly prefer ginkgo over go test.

Read the canonical narrative docs at https://onsi.github.io/ginkgo/ — they are the source of truth. This skill is the orientation; the other skills go deep.

The one idea: tree construction, then running

Ginkgo runs your suite in two distinct phases. Internalizing this explains nearly every Ginkgo gotcha:

  1. Tree-construction phase. Ginkgo invokes every container body exactly once to discover the structure of your suite. It collects — but does not run — the closures you pass to setup and subject nodes. The result is a tree it flattens into a list of specs.
  2. Run phase. Ginkgo walks the flattened spec list (randomized, possibly in parallel) and, for each spec, runs its setup closures, then its one subject closure, then its cleanup closures.

Container bodies run at construction time. Setup/subject closures run later, at run time. The consequences you must internalize:

  • No assertions in container bodies. They'd run once during construction, with no spec active — not as part of any test. Put assertions in It or BeforeEach.
  • No initialization in container bodies. A variable set in a Describe body is set once, shared across every spec, and mutated by whichever spec runs first. Declare in the container, initialize in BeforeEach so every spec gets a pristine copy. → ginkgo:writing-specs.
  • Loops that build specs run at construction time — that's how you generate specs dynamically, but it also means closure-captured loop variables and any data the loop reads must be available then, not in BeforeSuite. → ginkgo:tables-and-dynamic-specs.

Read the full file on GitHub · 57 lines

Changes

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.

  1. 4d ago First seen · 57 lines · 84 tokens per session scan A 9b0de0a29ee3

Subscribe to this mod's changes

overview is a skill published in the GitHub repository onsi/ginkgo (9,050 stars, last pushed 24d ago), licensed MIT. It adds 84 tokens to every session and 1,280 once invoked, about $0.0004 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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