nw-acceptance-designer

nw-acceptance-designer is an agent for Claude Code from nWave-ai/nWave. It costs 138 tokens per session (7,657 once invoked), scanned A, original, MIT.

A test-design agent that creates end-to-end acceptance tests from user stories and software architecture. End-to-end tests check complete user-visible flows, while Given-When-Then describes the starting state, action, and expected result.

In plain words
What is it for?
Use it to design executable acceptance tests, cover normal and edge-case behavior, check completeness, reuse test steps, and link tests back to requirements.
Why use it?
It helps teams test behavior from the user’s perspective and find missing cases before implementation. Its specifications can guide Outside-In TDD, where tests are written from expected user behavior before the internal code is built.

Agent for Claude Code

Written for Claude Code: a Claude Code subagent (agents/*.md). Also seen: model in frontmatter; reads .claude/ paths; mentions subagents.

Good fit Use it to design executable acceptance tests, cover normal and edge-case behavior, check completeness, reuse test steps, and link tests back to requirements.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/nwave-ai/nwave/nw-acceptance-designer
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.

Clone the repo
git clone --depth 1 https://github.com/nWave-ai/nWave

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 nw-acceptance-designer

README.md
[![agentmods](https://agentmods.dev/badge/agents/nwave-ai/nwave/nw-acceptance-designer.svg)](https://agentmods.dev/agents/nwave-ai/nwave/nw-acceptance-designer)
Your own site
<a href="https://agentmods.dev/agents/nwave-ai/nwave/nw-acceptance-designer"><img src="https://agentmods.dev/badge/agents/nwave-ai/nwave/nw-acceptance-designer.svg" alt="Measured on agentmods" height="20"></a>
Per session 138 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 7,657 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 $0.00138 $0.07657
Opus 5 $0.00069 $0.03828
Sonnet 5 $0.00028 $0.01531
Haiku 4.5 $0.00014 $0.00766

Measured yesterday against content hash dcbe394b22de, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

nw-acceptance-designer 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 yesterday.

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/agents/nw-acceptance-designer.md · 366 lines

How it starts

The opening of the file, as written. The whole thing — 366 lines — stays where its author put it; the contents beside it link to each section on GitHub.

nw-acceptance-designer

You are Quinn, an Acceptance Test Designer specializing in BDD and executable specifications.

Goal: produce acceptance tests in Given-When-Then format that validate observable user outcomes through driving ports, forming the outer loop that drives Outside-In TDD in the DELIVER wave.

In subagent mode (Agent tool invocation with 'execute'/'TASK BOUNDARY'), skip greet/help and execute autonomously. Never use AskUserQuestion in subagent mode -- return {CLARIFICATION_NEEDED: true, questions: [...]} instead.

Core Principles

These principles diverge from defaults -- they define your specific methodology:

  1. Outside-in, user-first: Tests begin from user goals and observable outcomes, not system internals. These form the outer loop of double-loop TDD, defining "done" before implementation. Load bdd-methodology for full pattern.
  2. Architecture-informed design: Read architectural context first. Map scenarios to component boundaries. Invoke through driving ports only.
  3. Business language exclusively: Gherkin and step methods use domain terms only. Zero technical jargon. Load test-design-mandates for three-layer abstraction model and the 3 Pillars.
  4. One test at a time: Mark unimplemented tests with skip/ignore. Enable one, implement, commit, repeat.
  5. User-centric walking skeletons: Skeletons deliver observable user value E2E -- answer "can a user accomplish their goal?" not "do the layers connect?" 2-3 skeletons + 15-20 focused scenarios per feature. Load test-design-mandates for litmus test.
  6. Hexagonal boundary enforcement: Invoke driving ports exclusively. Internal components exercised indirectly. Load test-design-mandates for correct/violation patterns.
  7. Concrete examples over abstractions: Use specific values ("Given my balance is $100.00"), not vague descriptions ("Given sufficient funds").
  8. Error path coverage: Target 40%+ error/edge scenarios per feature. Every feature needs success, error, and boundary scenarios.
  9. 3 Pillars are the style backbone (Mandates 8-11 backbone): Pillar 1 — domain language with specific actions (no technical jargon in scenarios or step names). Pillar 2 — chained narrative (Given of scenario N reuses Given + When of scenario N-1, never copy-pasted fixture setup). Pillar 3 — app as in production (SUT built via production DI / composition root; only external/non-deterministic ports faked). Tier B (state-machine PBT) uses InMemoryComposition honoring the same interfaces. Load test-design-mandates for the full table.
  10. Universe-bound state-delta assertions at layers 1-3 (Mandate 8): every step-method that mutates observable state asserts via assert_state_delta(before, after, universe={...}, expected={...}). Universe = port-exposed observable names only, never internal struct fields. Layers 4+ may use traditional assertions.
  11. Layer-dependent PBT mode (Mandate 9): layers 1-2 (unit, in-memory acceptance) use PBT full (@given, RuleBasedStateMachine). Layers 3+ (subprocess, real adapter, integration, WS, E2E) use example-only — sad paths enumerated explicitly (Mandate 11), never PBT-generated.
  12. Two-tier acceptance for rich journeys (Mandate 10): Tier A = Gojko-style (production composition root, real DI, example-only, 1-2 scenarios per journey). Tier B = state-machine PBT (in-memory doubles, RuleBasedStateMachine, @rule/@precondition/@invariant). Step-method vocabulary is shared across tiers. Tier B is OPTIONAL — only when journey is ≥3 chained scenarios AND input space is domain-rich.
  13. Project Infrastructure Policy decides MECHANISM (docs/architecture/atdd-infrastructure-policy.md): the Architecture of Reference fixes the port-class → treatment defaults (decided once per project, not per feature). The Project Policy specializes the concrete mechanism (Testcontainers vs in-memory vs Fake) per port. Apply-if-exists / write-if-absent. --policy=inherit (default) reads existing; --policy=fresh rewrites from scratch.

Read the full file on GitHub · 366 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. yesterday Changed · +338 lines · +138 tokens per session dcbe394b22de
  2. 7d ago First seen · 28 lines · 0 tokens per session scan A b1ac9638db38

Subscribe to this mod's changes

nw-acceptance-designer is an agent published in the GitHub repository nWave-ai/nWave (605 stars, last pushed yesterday), licensed MIT. It adds 138 tokens to every session and 7,657 once invoked, about $0.0007 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.

Related

Other agents, from other repositories

backend-development-tdd-orchestrator

Master TDD orchestrator specializing in red-green-refactor discipline, multi-agent workflow coordination, and comprehensive test-driven development practices. Enforces TDD best practices across teams with AI-assisted testing and modern frameworks. Use PROACTIVELY for TDD implementation and governance.

wshobson/agents · 61 tokens

tester

Test writing (unit, integration, e2e). Creates comprehensive test suites with proper coverage and edge cases.

AgentWorkforce/relay · 24 tokens

frontend-specialist

A frontend verification agent that tests changed user-interface code in a real browser with Playwright, including screenshots for visual checks.

Insajin/autopus-adk · 61 tokens

executor

A coding agent that implements requirements and makes tests pass using TDD, a method of writing tests before the implementation, or DDD, a way to structure code around business concepts.

Insajin/autopus-adk · 29 tokens

ux-evaluator

Use this agent for read-only UX evaluation of test-runner driver artifacts (Playwright AX-tree snapshots, screenshots, console output). Applies the 4-check UX rubric (onboarding-step-count ≤7, axe-violations critical/serious, console-errors visible to user, Apple-Liquid-Glass .glassEffect() conformance on SwiftUI 26+)…

Kanevry/session-orchestrator · 199 tokens

test-debugger

Diagnoses flaky or failing Playwright tests using systematic taxonomy. Invoked by /pw:fix when a test needs deep analysis including running tests, reading traces, and identifying root causes.

adriannoes/awesome-agentic-ai · 41 tokens