agentmods.dev

gherkin-and-dsl

gherkin-and-dsl is a skill for Claude Code from YuDefine/nuxt-supabase-starter. It costs 78 tokens per session (1,561 once invoked), scanned A, original, MIT.

A skill for writing and reviewing Gherkin scenarios and the matching domain vocabulary. Gherkin is a readable Given/When/Then format for describing tests, while a DSL is a small set of agreed words and meanings used to implement them.

In plain words
What is it for?
Use it to create or refactor feature files, choose between rules, backgrounds, outlines, and data tables, and align scenarios with step definitions. TDD, or test-driven development, can then use these scenarios as test guidance.
Why use it?
It prevents readable scenarios from being too vague for developers or automated test code to implement. It also catches steps whose meaning is missing from the project's vocabulary.

Skill for Claude Code

Written for Claude Code: disable-model-invocation in frontmatter.

not rated 45repo changed today A scan 78 tokens original MIT

Good fit Use it to create or refactor feature files, choose between rules, backgrounds, outlines, and data tables, and align scenarios with step definitions. TDD, or test-driven development, can then use these scenarios as test guidance.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/yudefine/nuxt-supabase-starter/gherkin-and-dsl
View source ↗ YuDefine/nuxt-supabase-starter
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.

Any agent
npx skills add YuDefine/nuxt-supabase-starter --skill gherkin-and-dsl
Clone the repo
git clone --depth 1 https://github.com/YuDefine/nuxt-supabase-starter

Made for: Claude Code.

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 gherkin-and-dsl

README.md
[![agentmods](https://agentmods.dev/badge/skills/yudefine/nuxt-supabase-starter/gherkin-and-dsl/github.svg)](https://agentmods.dev/skills/yudefine/nuxt-supabase-starter/gherkin-and-dsl)
Your own site
<a href="https://agentmods.dev/skills/yudefine/nuxt-supabase-starter/gherkin-and-dsl"><img src="https://agentmods.dev/badge/skills/yudefine/nuxt-supabase-starter/gherkin-and-dsl/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.

agentmods 80×15 button for gherkin-and-dsl

Your own site · 80×15
<a href="https://agentmods.dev/skills/yudefine/nuxt-supabase-starter/gherkin-and-dsl"><img src="https://agentmods.dev/badge/skills/yudefine/nuxt-supabase-starter/gherkin-and-dsl.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 78 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,561 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00078 $0.01561
Opus 5 $0.00039 $0.00781
Sonnet 5 $0.00016 $0.00312
Haiku 4.5 $0.00008 $0.00156

Measured today against content hash 479a5b2a0eb7, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

gherkin-and-dsl 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 today.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/audit_feature_dsl_topology.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

template/.claude/skills/gherkin-and-dsl/SKILL.md · 75 lines

How it starts

The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Gherkin And DSL

把 Gherkin 與 DSL 收斂成兩件事同時成立：

  • PM / 需求方讀得懂 Gherkin
  • AI / 測試作者可以直接把 Gherkin + DSL 落地成 step definitions 與測試程式碼

Quick Start

  1. 先讀目標 feature files、同模組 dsl.md、相關介面根共用 dsl.md、上游 testplan / spec。
  2. 先判斷本輪是「建立」、「檢查」還是「重構」。
  3. 結構與詞彙一起改，不要只改 Gherkin 不改 DSL。
  4. 任一 Gherkin 句型若找不到 DSL 對應，先補 DSL，再回填 feature。
  5. 最終結果必須同時滿足「業務可讀」與「可直接寫測試」。

SOP

Phase 1 -- 收斂輸入與輸出範圍

  1. READ 讀取使用者需求、目標 feature files、同模組 dsl.md、與目標句型相關的介面根共用 dsl.md、上游 testplan / spec，確認本輪介面與處理範圍。
  2. THINK 判斷本輪是新建、覆蓋驗證、句型收斂、結構重組或嚴格度補強，並盤點要修改的 feature 與 DSL 檔案。
  3. WRITE 回報修改範圍與會影響切檔或句型邊界的高影響歧義；若有歧義，先停下確認。

Phase 2 -- 盤點測試案例與覆蓋缺口

  1. READ 讀取 STANDARDS.md 中的「覆蓋與嚴格度標準」與「Gherkin 句型標準」。
  2. THINK 逐一比對每個 test case 的 Arrange / Act / 預期輸出 / 必須維持不變 / 本案例不驗證，判斷目前的 Gherkin 與 DSL 是否已完整覆蓋；若只是有步驟但語意被稀釋，也算缺口。
  3. WRITE 列出缺口：缺少句型、句型過胖、DataTable / Background / Rule 邊界錯置、Then 嚴格度不足、或 Gherkin 有技術語句外露。

Phase 3 -- 收斂 Gherkin 句型

  1. READ 讀取 STANDARDS.md 中的「Gherkin 語言邊界與句型收斂」與「參數 / DataTable 格式」。
  2. THINK 收斂 Given / When / Then 的共用句型：語意相同必須共用；句子過胖就拆成更核心、可重用的步驟；只有多列資料或多欄位摘要時才優先用 DataTable。
  3. WRITE 修改 Gherkin：補 Given / When / Then、補業務相關預設值註解、調整 DataTable、移除不再需要的搬運期註解與技術細節。

Phase 4 -- 收斂 DSL 詞彙表

  1. READ 讀取 STANDARDS.md 中的「DSL 必備欄位」、「後端實作語意」與「前端實作語意」。
  2. READ 若需要建立、移動 DSL row，或判斷句型應由模組或介面根承接，按需讀取 rules/介面功能模組與DSL唯一歸屬判準.md，確認完整契約與唯一歸屬判準。
  3. THINK 對每個 Gherkin 句型判斷既有 row、DataTable 欄位、預設參數與實作語意是否足夠，並依已載入判準決定唯一權威位置。
  4. WRITE 在唯一權威 DSL 檔建立或更新 row，確保句型、參數、DataTable 欄位、預設值與實作語意可直接支援 step definition。

Phase 5 -- 做結構優化

  1. READ 讀取 STANDARDS.md 中的「Feature / Rule / Example」與「Background / Scenario Outline / DataTable 決策」。
  2. THINK 依功能面向切分 feature files，判斷是否要抽 Rule、Background、Scenario Outline；Rule 必須原子化，Example 應描述資料情境而不是重複規則句。
  3. WRITE 重構 feature files：拆檔、抽 Rule、抽 Background、必要時引入 Scenario Outline，並移除被淘汰的單一總表檔。

Read the full file on GitHub · 75 lines

Files

What ships with it

13 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. today Changed · +1 lines 479a5b2a0eb7
  2. yesterday First seen · 74 lines · 78 tokens per session scan A 52cc43ecbef6

Subscribe to this mod's changes

gherkin-and-dsl is a skill published in the GitHub repository YuDefine/nuxt-supabase-starter (45 stars, last pushed yesterday), licensed MIT. It adds 78 tokens to every session and 1,561 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 2026-09-10.

Claude CodeLearning and teachingTDDTestingai-assistednuxtsupabasetemplate
Related

Other skills, from other repositories

Vibe Coding Mastery

The complete operating system for building software with AI. From first prompt to production deployment — prompting frameworks, architecture patterns, testing strategies, debugging playbooks, and production graduation checklists. Works with Claude Code, Cursor, Windsurf, Copilot, and any AI coding tool.

LeoYeAI/openclaw-master-skills · 61 tokens

prd-v07-implementation-loop

Execute implementation within EPICs following test-first development, continuous SoT updates, and code traceability during PRD v0.7 Build Execution. Triggers on requests to start building, implement an epic, begin coding, or when user asks "start building", "implement epic", "coding", "development", "build execution"…

mattgierhart/PRD-driven-context-engineering · 113 tokens

prd-v07-test-planning

Define test cases BEFORE implementation, ensuring every API, business rule, and user journey has verifiable acceptance criteria during PRD v0.7 Build Execution. Triggers on requests to define tests, plan test coverage, create test cases, or when user asks "define tests", "test planning", "what to test?", "test cases"…

mattgierhart/PRD-driven-context-engineering · 125 tokens

Learn what defines effective BDD scenarios

Interactive guidance on writing complete, effective BDD scenarios for story-flow.

Intai/story-flow · 22 tokens

principles

Metric literacy for what this plugin reports: what cyclomatic complexity, cognitive complexity, Halstead difficulty, lines per file, duplication, coverage, CRAP, and type debt mean, and what none of them can tell you. Four reference files carry the definitions and what each collector computes, each bundled reference…

melodic-software/claude-code-plugins · 218 tokens

pair-programming

Skill "pair-programming" from DNYoussef/context-cascade, covering kanitsal cerceve (evidential frame activation), pair programming, kanitsal cerceve (evidential frame activation), when to use this skill and when not to use this skill.

DNYoussef/context-cascade · 0 tokens