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.
git clone --depth 1 https://github.com/shinpr/claude-code-workflowsWrote 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.
[](https://agentmods.dev/agents/shinpr/claude-code-workflows/acceptance-test-generator)<a href="https://agentmods.dev/agents/shinpr/claude-code-workflows/acceptance-test-generator"><img src="https://agentmods.dev/badge/agents/shinpr/claude-code-workflows/acceptance-test-generator/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.
<a href="https://agentmods.dev/agents/shinpr/claude-code-workflows/acceptance-test-generator"><img src="https://agentmods.dev/badge/agents/shinpr/claude-code-workflows/acceptance-test-generator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00068 | $0.04099 |
| Opus 5.5 | $0.00027 | $0.01640 |
| Sonnet 5.5 | $0.00014 | $0.00820 |
| Haiku 4.5 | $0.00007 | $0.00410 |
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 3d 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.
How it starts
The opening of the file, as written. The whole thing — 286 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.mdin 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.
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.
- 3d ago Changed · -14 lines d5f2bc549e60
- 1mo ago Changed · -1 lines 0b780ad49a29
- 1mo ago First seen · 301 lines · 68 tokens per session scan A edb62a791290
acceptance-test-generator is an agent published in the GitHub repository shinpr/claude-code-workflows (698 stars, last pushed 3d ago), licensed MIT. It adds 68 tokens to every session and 4,099 once invoked, about $0.0003 per session on Opus 5.5. 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.
Other agents, from other repositories
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test-master
Testing specialist using the Quality Diamond model — specification-driven tests from acceptance criteria down through contract tests, property-based invariants, and unit tests (invoked in --tdd-first mode).
mobile-tester
Agent "mobile-tester" from akaszubski/autonomous-dev, covering mission, hard gate: appium mcp availability check, hard gate: environment security (sandbox only), hard gate: screenshot data safety and hard gate: 60-second timeout per test case.
ui-tester
E2E browser testing specialist - writes persistent test files using Playwright MCP tools.
specwright-integration-tester
Integration test engineer for non-unit tiers. Writes integration tests, contract tests, and end-to-end tests that exercise real infrastructure at component boundaries. Never writes skip conditions for missing infrastructure.
frontend-verifier
Verifies frontend via browser automation — forms, interactions, visual regressions, responsive layouts. Also fixes issues found. General frontend QA when there's no Figma involved. Only non-Figma design agent. Good for catching functional bugs (broken forms, console errors) alongside visual issues.