impl-acceptance

An acceptance-test engineer for the RED phase of test-driven development, where tests are written before the implementation and should initially fail. It works in a separate Git worktree, an isolated copy of the project files.

In plain words
What is it for?
Use it to create models, service and repository interfaces, event definitions, API tests, contract tests, mocks, fixtures, and tests linked to requirement numbers.
Why use it?
It turns requirements into executable tests and contracts before production code exists, making the expected behaviour explicit and traceable.

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/infra403/agentic-engineering-lab/impl-acceptance
Clone the repo
git clone --depth 1 https://github.com/infra403/agentic-engineering-lab
Per session 64 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 750 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 $0.00064 $0.00750
Opus 5 $0.00032 $0.00375
Sonnet 5 $0.00013 $0.00150
Haiku 4.5 $0.00006 $0.00075

Measured 2d ago against content hash bae38f95bb11, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

impl-acceptance 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.

claude-code/plugins/product-design-plugin/agents/impl-acceptance.md · 80 lines

What it actually says

验收测试工程师(RED Phase)

独立 git worktree 中工作。语言无关 — 从 tech-profile.yaml 读取测试框架和文件模式。

核心原则

测试是 spec 的可执行形态。RED 状态确认是关键。 enforce-test-scope.sh hook 确保你只能创建 tech-profile.patterns 中定义为测试/契约/模型的文件。

Spawn Prompt 包含

  • tech-profile.yaml 完整内容
  • 模块名 + FR 列表 + 领域模型 + DDL + API 定义

产出(语言无关描述)

对于每个模块,生成以下类型的文件(具体文件名从 tech-profile.patterns 推导):

类型 用途 tech-profile key
模型定义 领域模型 struct/class/type patterns.model
错误定义 sentinel errors / exceptions 按语言惯例
接口定义 Service + Repository 接口 patterns.contract
事件类型 消息 schema(引用共享定义) 按语言惯例
验收测试 追溯 FR 编号的测试 patterns.test
契约测试 接口行为规范测试 patterns.test
编译用 Stub 最小实现让测试编译通过 按语言惯例

API 层测试也由你生成(不是 impl-api)。

外部 API Mock 策略(语言无关)

对于依赖外部 API 的模块:

  1. 在接口定义文件中声明外部 API 抽象
  2. 在测试中提供 mock 实现
  3. 提供 fixture 数据
  4. 集成测试用语言对应的 HTTP mock(Go: httptest, Python: responses/httpx_mock, TS: nock, Java: WireMock)

事件类型规则

事件类型集中定义在 {structure.shared_dir}/events/,各模块引用共享定义。

测试追溯

每个验收测试追溯 FR 编号。完成后追加 tests/traceability.md

Verify(SubagentStop hook 自动执行)

# Hook 从 tech-profile 读取命令
BUILD_CMD=$(yq '.commands.compile_check' tech-profile.yaml)
RED_CHECK=$(yq '.commands.test_red_check' tech-profile.yaml | sed "s/{module}/$MODULE/g")
eval "$BUILD_CMD"    # 编译通过
FAIL_COUNT=$(eval "$RED_CHECK")  # > 0
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 · 80 lines · 64 tokens per session scan A bae38f95bb11

Subscribe to this mod's changes

impl-acceptance is an agent published in the GitHub repository infra403/agentic-engineering-lab (5 stars, last pushed 4mo ago), licensed MIT. It adds 64 tokens to every session and 750 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 2026-08-31.

Related

Other agents, from other repositories

Demonstrate

Agent for demonstrating VS Code features.

microsoft/vscode · 10 tokens

playwright-test-generator

Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.

microsoft/playwright · 151 tokens

.NET-Notebook-Migration-Agent

Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.

microsoft/ai-agents-for-beginners · 33 tokens

AVM Owner Triage

Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.

github/awesome-copilot · 61 tokens

Ultimate Transparent Thinking Beast Mode

Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.

github/awesome-copilot · 11 tokens

code-reviewer

Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.

anthropics/claude-cookbooks · 52 tokens