nw-bdd-methodology

nw-bdd-methodology is a skill for Claude Code from nWave-ai/nWave. It costs 36 tokens per session (1,278 once invoked), scanned A, original, MIT.

A guide to Behaviour-Driven Development (BDD), a way to describe software behaviour from the user's point of view using Given-When-Then scenarios. It covers acceptance tests, pytest-bdd, and documentation that stays aligned with the software.

In plain words
What is it for?
Use it to write acceptance scenarios, implement them with pytest-bdd, and avoid common BDD mistakes. It also helps keep user-facing requirements as living documentation.
Why use it?
It helps teams test business outcomes instead of internal code details. It also separates what users need from how developers implement it, reducing unclear requirements and brittle tests.

Skill for Claude Code

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

Needs its repository: it reads a path above its own folder, which exists only inside the repository. The line is scenarios('../features/account.feature').

Good fit Use it to write acceptance scenarios, implement them with pytest-bdd, and avoid common BDD mistakes. It also helps keep user-facing requirements as living documentation.

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Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/nWave-ai/nWave
agentmods
npx agentmods add skills/nwave-ai/nwave/nw-bdd-methodology

Made for: Claude Code.

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Per session 36 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,278 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00036 $0.01278
Opus 5 $0.00018 $0.00639
Sonnet 5 $0.00007 $0.00256
Haiku 4.5 $0.00004 $0.00128

Measured 9d ago against content hash d7b984b8b643, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

nw-bdd-methodology 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.

nWave/skills/nw-bdd-methodology/SKILL.md · 151 lines

How it starts

The opening of the file, as written. The whole thing — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.

BDD Methodology for Acceptance Test Design

Core Philosophy

Test units of behavior, not units of code. Acceptance tests validate business outcomes through public interfaces, decoupled from implementation.

Outside-In Double-Loop TDD

The acceptance-designer creates the outer loop of Outside-In TDD. Development starts from user perspective, drives inward.

Outer loop (acceptance/BDD): Hours to days | User perspective, business language | Defines "done" | Scenarios describe user goals and observable outcomes, not internals | Failing outer-loop test is the starting signal for implementation

Inner loop (unit/TDD): Minutes | Developer perspective, technical terms | Software-crafter owns this loop

Workflow:

  1. Write failing acceptance test from user perspective (outer loop -- outside)
  2. Software-crafter drops to inner loop: unit tests to implement components (inside)
  3. Iterate inner loop until acceptance test passes
  4. Passing acceptance test proves user value delivered
  5. Repeat for next behavior

Outer loop defines WHAT users need (outside). Inner loop drives HOW to build it (inside).

Given-When-Then Structure

Scenario: [Business-focused title describing one behavior]
  Given [preconditions - system state in business terms]
  When [single user action or business event]
  Then [observable business outcome]

Scenario Writing Rules

Rule 1: One scenario, one behavior -- Split multi-behavior scenarios.

Rule 2: Declarative, not imperative -- Business outcomes, not UI interactions. "When I log in with valid credentials" not "When I click Login button and enter email."

Rule 3: Concrete examples, not abstractions -- "Given my account balance is $100.00" not "Given the user has sufficient funds."

Rule 4: Keep scenarios short (3-5 steps) -- Longer means testing multiple behaviors or irrelevant details.

Rule 5: Background for shared Given steps only -- Only Given steps. Actions/validations in scenarios.

Scenario Categorization

Read the full file on GitHub · 151 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 · 151 lines · 36 tokens per session scan A d7b984b8b643

Subscribe to this mod's changes

nw-bdd-methodology is a skill published in the GitHub repository nWave-ai/nWave (610 stars, last pushed 3d ago), licensed MIT. It adds 36 tokens to every session and 1,278 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 2026-08-30.