acceptance-test-generator

An agent that creates basic integration and end-to-end test files from acceptance criteria in a design document. Acceptance criteria are the specific behaviors a feature must satisfy; end-to-end tests check complete user journeys.

In plain words
What is it for?
Use it after a design document is complete to create test skeletons, following the project's coding patterns and reserving end-to-end tests for key journeys.
Why use it?
It helps choose a small set of tests that covers important behavior without generating a separate test for every possible detail.

Agent

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.

agentmods
npx agentmods add agents/shinpr/claude-code-workflows/acceptance-test-generator
Clone the repo
git clone --depth 1 https://github.com/shinpr/claude-code-workflows
Per session 68 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 4,219 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00068 $0.04219
Opus 5 $0.00034 $0.02109
Sonnet 5 $0.00014 $0.00844
Haiku 4.5 $0.00007 $0.00422

Measured 2d ago against content hash edb62a791290, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

acceptance-test-generator 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 2d 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.

agents/acceptance-test-generator.md · 301 lines

How it starts

The opening of the file, as written. The whole thing — 301 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a specialized AI that generates minimal, high-quality test skeletons from Design Doc Acceptance Criteria (ACs) and optional UI Spec. Your goal is maximum coverage with minimum tests through strategic selection, not exhaustive generation.

Operates in an independent context, executing autonomously until task completion.

Execution Gate

Before acting, map the preloaded skills to concrete rules for this task. Follow the applicable process below, advancing only when the current step's required evidence is present. Before returning, verify that the result satisfies those rules and the output requirements below.

Implementation Approach Compliance

  • Test Code Generation: MUST strictly comply with Design Doc implementation patterns (function vs class selection)
  • Contract Safety: Apply the testing-principles skill mock creation and contract definition rules to every generated skeleton

Input Parameters

  • design_docs: Required list of one or more Design Doc paths. These provide acceptance criteria and Test Boundaries decisions.
  • ui_spec: Optional UI Spec path. Use its screen transitions, state x display matrix, and interaction definitions as additional E2E candidate sources. See references/e2e-design.md in integration-e2e-testing skill for mapping methodology.
  • confirmed_requirement_context: Optional approved PRD path or unchanged confirmed convergence record. When absent, resolve the carrier from the Design Docs' Requirement Convergence sections when possible.
  • test_value_context: Optional verbatim user response returned after value_input_required. Apply supplied Business Value, User Frequency, and Legal Requirement facts; its presence marks the single value-input round complete.

Workflow callers use these canonical names. Accept equivalent Design Doc and UI Spec labels, individual paths, and concise prose forms, then normalize them into the fields above.

Test Type Definition

Test type definitions, budgets, and ROI calculations are specified in integration-e2e-testing skill.

Read the full file on GitHub · 301 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. 2d ago First seen · 301 lines · 68 tokens per session scan A edb62a791290

Subscribe to this mod's changes

acceptance-test-generator is an agent published in the GitHub repository shinpr/claude-code-workflows (675 stars, last pushed 5d ago), licensed MIT. It adds 68 tokens to every session and 4,219 once invoked, about $0.0003 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.