generate-bdd-specs

generate-bdd-specs is a skill for Claude Code from hmcts/agentic-plugins-marketplace. It costs 39 tokens per session (611 once invoked), scanned A, original, MIT.

A skill for converting approved acceptance criteria into Cucumber/Gherkin feature files. These files describe expected behaviour in readable Given/When/Then scenarios.

In plain words
What is it for?
Use it to create one feature file per story, with backgrounds, normal and failure cases, data-driven scenarios, and tags such as smoke or regression.
Why use it?
It turns a story's requirements into consistent, testable scenarios without mixing business behaviour with implementation details.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the bdd-workflow plugin — 2 skills shipped together

Good fit Use it to create one feature file per story, with backgrounds, normal and failure cases, data-driven scenarios, and tags such as smoke or regression.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/hmcts/agentic-plugins-marketplace/generate-bdd-specs
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 hmcts/agentic-plugins-marketplace --skill generate-bdd-specs
Clone the repo
git clone --depth 1 https://github.com/hmcts/agentic-plugins-marketplace

Made for: Claude Code.

Or install bdd-workflow, the plugin that ships this one along with the rest of its 2 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 generate-bdd-specs

README.md
[![agentmods](https://agentmods.dev/badge/skills/hmcts/agentic-plugins-marketplace/generate-bdd-specs/github.svg)](https://agentmods.dev/skills/hmcts/agentic-plugins-marketplace/generate-bdd-specs)
Your own site
<a href="https://agentmods.dev/skills/hmcts/agentic-plugins-marketplace/generate-bdd-specs"><img src="https://agentmods.dev/badge/skills/hmcts/agentic-plugins-marketplace/generate-bdd-specs/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 generate-bdd-specs

Your own site · 80×15
<a href="https://agentmods.dev/skills/hmcts/agentic-plugins-marketplace/generate-bdd-specs"><img src="https://agentmods.dev/badge/skills/hmcts/agentic-plugins-marketplace/generate-bdd-specs.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 39 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 611 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 $0.00039 $0.00611
Opus 5 $0.00019 $0.00305
Sonnet 5 $0.00008 $0.00122
Haiku 4.5 $0.00004 $0.00061

Measured 9d ago against content hash d2e809c911d2, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

generate-bdd-specs 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.

plugins/skills/bdd-workflow/skills/generate-bdd-specs/SKILL.md · 78 lines

How it starts

The opening of the file, as written. The whole thing — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Generate BDD Specs (Gherkin)

Purpose

Convert approved ACs into well-formed Cucumber/Gherkin feature files.

Format rules

  • One feature file per story (<PROJ-NNN>.feature or equivalent naming)
  • Feature: block contains the user story statement
  • Background: for context shared by all scenarios in the file
  • Scenario: for individual ACs
  • Scenario Outline: + Examples: for data-driven variations
  • Tags on each scenario: @smoke, @regression, @accessibility, @negative

Language rules

  • Gherkin steps must be in business language — no technical implementation detail
  • Given = state setup (not "I click the login button")
  • When = single action (not "I fill in the form and submit it")
  • Then = observable outcome from the actor's perspective
  • Avoid UI selectors, SQL, HTTP verbs, class names in Gherkin

Template

@regression
Feature: [Story title — PROJ-NNN]
  As a [actor]
  I want [goal]
  So that [benefit]

  Background:
    Given the system is in [base state]
    And [shared precondition]

  @smoke
  Scenario: [Happy path — AC-001]
    Given [specific context]
    When [actor performs action]
    Then [expected outcome]
      And [secondary outcome if needed]

  @negative
  Scenario: [Failure mode — AC-002]
    Given [context]
    When [action with invalid input]
    Then [error is surfaced appropriately]
      And [no state change occurred]

  @accessibility
  Scenario: [Accessibility — AC-00N]
    Given [page/component is rendered]
    When [axe-core scan is run]
    Then [zero violations are reported at WCAG 2.1 AA level]

  Scenario Outline: [Data-driven scenario]
    Given [context with <variable>]
    When [action with <input>]
    Then [outcome is <expected>]

    Examples:
      | variable | input | expected |
      | ...      | ...   | ...      |

Tagging convention

Tag Meaning
@smoke Run on every deploy — critical path only
@regression Full regression suite
@negative Error handling, invalid inputs, boundary failures
@accessibility axe-core or manual WCAG check required
@contract Cross-service contract test
@wip In progress — excluded from CI until removed

Read the full file on GitHub · 78 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. 9d ago First seen · 78 lines · 39 tokens per session scan A d2e809c911d2

Subscribe to this mod's changes

generate-bdd-specs is a skill published in the GitHub repository hmcts/agentic-plugins-marketplace (3 stars, last pushed yesterday), licensed MIT. It adds 39 tokens to every session and 611 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 2026-08-31.

Related

Other skills, from other repositories

research-engineer

An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.

davila7/claude-code-templates · 43 tokens

tika-eval-compare

Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".

apache/tika · 50 tokens

neuron-evaluation-engineer

Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…

neuron-core/neuron-ai · 77 tokens

jetson-validate-image

Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.

NVIDIA/skills · 50 tokens

atmos-validation

Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.

cloudposse/atmos · 31 tokens

skill-benchmark

Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.

HoangNguyen0403/agent-skills-standard · 16 tokens