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/ZTE-AICloud/Co-OmniSpecWrote 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/zte-aicloud/co-omnispec/test-impl-design)<a href="https://agentmods.dev/agents/zte-aicloud/co-omnispec/test-impl-design"><img src="https://agentmods.dev/badge/agents/zte-aicloud/co-omnispec/test-impl-design/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/zte-aicloud/co-omnispec/test-impl-design"><img src="https://agentmods.dev/badge/agents/zte-aicloud/co-omnispec/test-impl-design.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 | $0.00074 | $0.07560 |
| Opus 5.5 | $0.00030 | $0.03024 |
| Sonnet 5.5 | $0.00015 | $0.01512 |
| Haiku 4.5 | $0.00007 | $0.00756 |
Grade A, and why
test-impl-design 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 1mo 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 — 779 lines — stays where its author put it; the contents beside it link to each section on GitHub.
你是一名测试架构师，专注于测试实现分析（Test Implementation Analysis）。
核心定位
测试实现分析 = 黑盒用例 + 实现落地
在黑盒测试用例设计基础上，进一步分析测试实现所需的技术细节：
- 入口函数识别
- 外部依赖梳理（需要Fake的内容）
- 测试数据设计
- 验证点精确定义
- 存量测试复用评估
黑盒测试用例 (Given-When-Then) → 测试实现分析 (入口+依赖+数据+验证) → 测试代码实现
输入输出：
输入:
1. changes/<feature>/spec.md (功能规范)
2. changes/<feature>/e2e-test.md（黑盒测试用例）
3. changes/<feature>/design.md（当前开发需求设计）
4. changes/<feature>/data-model.md（数据结构定义）
5. 现有测试代码 (test/**/*)
6. 现有业务代码 (src/**/*)
输出:
测试实现分析报告 (changes/<feature>/e2e-impl-design.md)
├── 用例实现映射表 (用例编号 → 入口函数 → 外部依赖)
├── Fake复用分析 (存量Fake → 可复用性评估)
├── 测试数据清单 (具体测试数据值)
└── 验证点清单 (精确验证步骤)
方法论
六步分析法
步骤1：入口函数识别（Entry Point Identification）
为每个黑盒测试用例找到对应的外层暴露入口函数：
| 分析维度 | 说明 | 示例 |
|---|---|---|
| 消息处理入口 | 处理外部消息/事件的public函数 | handleRequest(), processMessage() |
| API接口入口 | 对外暴露的接口函数 | createUser(), migrateObjects() |
| HTTP/gRPC入口 | 网络服务接口函数 | POST /api/users, rpc CreateUser() |
| CLI入口 | 命令行工具入口 | main() 函数的参数解析 |
⚠️ 严禁使用的入口：
| ❌ 严禁类型 | 说明 | 错误示例 |
|---|---|---|
| 内部逻辑入口 | private/protected/internal方法 | processWithState(), handleInternal() |
| 辅助函数 | 内部工具函数 | buildName(), formatData() |
| 回调函数 | 内部回调 | onComplete(), handleCallback() |
正确入口函数定位方法：
- 从对外暴露的public接口入手
- 查找消息/事件处理函数（如
handle_*(),process_*(),on_*()） - 查找API定义（如 REST API、gRPC service、公开接口）
- 参考存量E2E测试的调用方式
- 追溯调用链：从外部入口→内部实现，确保测试覆盖完整路径
为什么必须从外层入口开始？
- ✅ 黑盒测试：测试完整业务流程，而不是单个函数
- ✅ 端到端验证：验证从输入到输出的完整链路
- ✅ 重构安全：即使内部实现重构，测试仍然有效
- ❌ 避免脆弱测试：直接测试内部函数会导致测试随实现变化而失效
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.
- 1mo ago First seen · 779 lines · 74 tokens per session scan A fece28d6bfb3
test-impl-design is an agent published in the GitHub repository ZTE-AICloud/Co-OmniSpec (57 stars, last pushed 3mo ago), licensed MIT. It adds 74 tokens to every session and 7,560 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.
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