gherkin-generator-skill

Original：🇺🇸 English

Translated

Generates high-quality Gherkin (BDD) scenarios from functional requirements using a two-agent iterative cycle: a generator agent that creates/modifies the Gherkin and a reviewer agent that validates it and proposes improvements. The cycle repeats automatically until the Gherkin passes review. Use this skill whenever the user mentions: "generate Gherkin", "BDD scenarios", "Gherkin test cases", "Feature/Scenario/Given/When/Then", "requirements to Gherkin", "BDD specifications", or asks to transform functional requirements into behaviour tests. Also applies when the user brings a requirements document and wants test cases, acceptance criteria, or user stories with executable examples.

2installs

Sourceehdez73/agent-skills

Added on2026-03-11

NPX Install

npx skill4agent add ehdez73/agent-skills gherkin-generator-skill

SKILL.md Content

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Skill: Gherkin Generator (BDD)

Converts functional requirements into valid, high-quality Gherkin scenarios through an iterative cycle with two specialised agents.

System Architecture

Functional requirements
        │
        ▼
┌─────────────────────┐
│  gherkin-generator  │  ← Agent 1: generates/modifies the .feature file
│       -agent        │
└────────┬────────────┘
         │ Gherkin draft
         ▼
┌─────────────────────┐
│  gherkin-reviewer   │  ← Agent 2: validates and proposes improvements
│       -agent        │
└────────┬────────────┘
         │
    Approved?
    NO ──────────────────► returns to Agent 1 with feedback
    YES ─────────────────► delivers the final .feature file

Step-by-step workflow

Step 1 — Prepare the requirements

Before invoking the agents, extract from the user's input:

Module / feature name (for the Feature name)
List of functional requirements (numbered or in prose)
Business context (actors, systems involved)
Example data if available

If anything is ambiguous, ask the user before starting.

Step 2 — Invoke the Generator Agent

Read the full instructions in

agents/generator-agent.md

Call the agent with:

INPUT:
  requirements:      <list of requirements>
  context:           <business context>
  previous_feedback: <empty on first iteration>
  previous_gherkin:  <empty on first iteration>
  iteration:         <current iteration number>

The agent returns a Gherkin block in

.feature

format.

Step 3 — Invoke the Reviewer Agent

Read the full instructions in

agents/reviewer-agent.md

Call the agent with:

INPUT:
  gherkin:               <output from the generator agent>
  original_requirements: <user's original requirements>
  iteration:             <current iteration number>

The agent returns an object with:

```
approved: true/false
```
```
score: 0-100
```
```
issues: [list of problems]
```

suggestions: [list of concrete improvements]

annotated_gherkin: <the gherkin with inline comments>

Step 4 — Improvement loop

iteration = 1
maximum   = 5   ← prevents infinite loops

WHILE iteration <= maximum:
  gherkin  = generator_agent(requirements, feedback, previous_gherkin)
  review   = reviewer_agent(gherkin, requirements)

  IF review.approved == true OR review.score >= 85:
    BREAK

  feedback         = review.suggestions + review.issues
  previous_gherkin = gherkin
  iteration       += 1

IF iteration > maximum:
  deliver the best gherkin obtained with a warning note

Step 5 — Delivery

Present to the user:

The final
```
.feature
```
file with syntax highlighting
The reviewer's final score
A summary of iterations performed
(Optional) Save the file if the user requests it

Approval criteria (summary)

The Reviewer Agent approves when all of the following are met:

Criterion	Minimum requirement
Requirements coverage	100% of functional requirements covered
Gherkin syntax	No errors (Feature, Scenario, Given/When/Then)
Scenario independence	No order dependency between scenarios
Step clarity	No technical ambiguities
Example data	Scenario Outline + Examples tables when variants exist
Language	Consistent throughout the file

Implementation notes

Maximum iterations: 5 (configurable by the user)
Language: use the same language as the input requirements
Granularity: one
```
.feature
```
file per module/feature
Tags: add
```
@smoke
```
,
```
@regression
```
,
```
@happy-path
```
,
```
@edge-case
```
as appropriate
Background: use only when ≥3 scenarios share the same Given steps

For implementation details of each agent, read:

```
agents/generator-agent.md
```
```
agents/reviewer-agent.md
```
```
references/gherkin-best-practices.md
```