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.
npx skills add ZTE-AICloud/Co-OmniSpec --skill e2e-specifygit 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/skills/zte-aicloud/co-omnispec/e2e-specify)<a href="https://agentmods.dev/skills/zte-aicloud/co-omnispec/e2e-specify"><img src="https://agentmods.dev/badge/skills/zte-aicloud/co-omnispec/e2e-specify/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/skills/zte-aicloud/co-omnispec/e2e-specify"><img src="https://agentmods.dev/badge/skills/zte-aicloud/co-omnispec/e2e-specify.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00121 | $0.01531 |
| Opus 5 | $0.00060 | $0.00766 |
| Sonnet 5 | $0.00024 | $0.00306 |
| Haiku 4.5 | $0.00012 | $0.00153 |
Grade A, and why
e2e-specify 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 9d 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 — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
用户输入
$ARGUMENTS
在继续之前, 你必须考虑用户输入(如果不为空).
概述
执行测试分析与设计工作流,使用 MFQ&PPDCS 方法论进行测试分析,然后使用 TCON 方法设计黑盒测试用例。
调用场景:
- 初步设计(在
/specify之后):基于初始规范生成初步测试分析和设计 - 更新设计(在
/clarify之后):基于澄清后的规范更新测试分析和设计
执行流程
1. [ ] 设置环境
- 判断操作系统:Windows 或 Linux
- 运行前置检查脚本:
- Windows:
scripts/powershell/check-prerequisites.ps1 --json - Linux:
scripts/bash/check-prerequisites.sh --json
- Windows:
- 解析输出:
- 提取
FEATURE_DIR(特性目录绝对路径) - 提取
SPEC_FILE(规范文件路径) - 提取
AVAILABLE_DOCS(可用文档列表)
- 提取
重要:所有路径必须是绝对路径。
2. [ ] 确定调用场景
根据调用上下文确定场景类型:
- 初步设计:如果在
/specify之后调用,场景为"初步设计" - 更新设计:如果在
/clarify之后调用,场景为"更新设计"
3. [ ] 启动测试分析设计 Agent
使用 Agent 工具启动 @"test-analysis-design (agent)",传递以下参数:
Agent 参数:
subagent_type: "general-purpose"(使用通用 agent)description: "测试分析与设计 - 生成测试分析和黑盒测试用例"prompt包含以下内容:
# 测试分析与设计任务
## 调用场景
{场景类型}: 初步设计/更新设计
## 输入文件
- spec.md: {SPEC_FILE}
- feature_dir: {FEATURE_DIR}
## 任务要求
1. 读取 spec.md 规范文件
2. 使用 MFQ&PPDCS 方法论进行测试分析
3. 生成测试分析报告(test-analysis.md)
4. 使用 TCON 四步法设计黑盒测试用例
5. 生成黑盒测试用例文档(e2e-test.md)
## 输出位置
- test-analysis.md: {FEATURE_DIR}/test-analysis.md
- e2e-test.md: {FEATURE_DIR}/e2e-test.md
## 重要说明
- 如果是"初步设计"场景:所有 Issues 保持 Open 状态
- 如果是"更新设计"场景:根据澄清结果更新,将相关 Issues 标记为 Resolved
4. [ ] 验证生成文档
等待 agent 完成后,验证生成的文档:
-
检查文档存在性:
- 检查
{FEATURE_DIR}/test-analysis.md是否存在 - 检查
{FEATURE_DIR}/e2e-test.md是否存在
- 检查
-
验证文档内容:
test-analysis.md应包含:- KYM 分析
- TCO 分析
- MFQ 建模结果
- 测试点清单
- Issues 列表(状态应为 Open 或 Resolved)
e2e-test.md应包含:- 用例清单
- 用例详情(Given-When-Then 格式)
- 测试数据设计
- 追溯性矩阵
-
处理验证结果:
- [成功] 验证成功:输出完成报告,继续下一步
- [失败] 验证失败:
- 记录错误信息
- 如果是关键错误(文档未生成、内容为空),报告失败
- 如果是非关键错误(部分内容缺失),记录警告但继续
5. [ ] 报告完成情况
输出完成报告,包括:
-
生成文档:
- test-analysis.md 路径
- e2e-test.md 路径
-
验证结果:
- 文档存在性检查:[成功]/[失败]
- 内容完整性检查:[成功]/[失败]
- 问题列表(如有)
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.
- 9d ago First seen · 167 lines · 121 tokens per session scan A ea01cf6402ab
e2e-specify is a skill published in the GitHub repository ZTE-AICloud/Co-OmniSpec (54 stars, last pushed 1mo ago), licensed MIT. It adds 121 tokens to every session and 1,531 once invoked, about $0.0006 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.
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