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 clarifygit 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/clarify)<a href="https://agentmods.dev/skills/zte-aicloud/co-omnispec/clarify"><img src="https://agentmods.dev/badge/skills/zte-aicloud/co-omnispec/clarify/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/clarify"><img src="https://agentmods.dev/badge/skills/zte-aicloud/co-omnispec/clarify.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 | $0.00081 | $0.03336 |
| Opus 5 | $0.00041 | $0.01668 |
| Sonnet 5 | $0.00016 | $0.00667 |
| Haiku 4.5 | $0.00008 | $0.00334 |
Grade A, and why
clarify 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 10d 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 — 258 lines — stays where its author put it; the contents beside it link to each section on GitHub.
技能依赖
本技能会调用以下技能:
- /omni-dsdd:eval-specify: 提供四维量规进行规范质量评测
- /omni-dsdd:runlog-record: 记录技能运行日志信息
规范澄清
目标
检测并减少活跃功能规范中的模糊性或缺失的决策点, 并将澄清内容直接记录在规范文件中.
注意: 此澄清工作流应在 /omni-dsdd:design 之前运行(并完成). 如果用户明确表示跳过澄清(例如, 探索性原型), 可以继续, 但必须警告下游返工风险增加.
用户输入
在继续之前, 你必须考虑用户的消息内容(如果不为空).
Workflow 自动决策模式
当由 workflow-orchestrator 派发且 Task prompt 包含 clarify 自动决策指令（prompt_inject: clarify-auto-decision）时，进入自动模式：
- 禁止等待用户输入；禁止使用
AskUserQuestion - 步骤 4 中每个问题：确定推荐/建议答案后立即自动采纳（等价于
recommended/yes/suggested），不得逐个暂停等待确认 - 完成报告须注明 模式: 自动决策（workflow），并列出所有自动采纳的 Q→A
单独调用 /clarify（非 workflow 编排），或由 workflow 编排但未注入自动决策指令（standard / deep 模式）时，走交互式步骤 4（逐题向用户提问，等待用户回复，不得自动采纳）。
设计约定：express 无 clarify 阶段（跳过）；standard / deep 必须手动澄清（逐题问用户）； 只有当 Task prompt 显式包含
clarify-auto-decision指令时才进入上面的自动模式。
执行步骤
0. skill执行开始时间打点记录
开始执行步骤之前，需要进行一些打点记录工作，记录本skill的执行时间到 start_time字段：
- 判断当前操作系统，windows还是linux系统;
- 针对不同操作系统运行脚本获取配置
windows:
Get-Date -Format "yyyy-MM-dd HH:mm:ss"linux:date +"%Y-%m-%d %H:%M:%S" - 将获取的时间记录到
start_time
1. 设置
- 判断当前操作系统, windows 还是 linux 系统;
- 针对不同操作系统从仓库根目录运行脚本一次
windows:
scripts/powershell/check-prerequisites.ps1 --json --paths-onlylinux:scripts/bash/check-prerequisites.sh --json --paths-only - 解析最小 JSON 负载字段:
FEATURE_DIRFEATURE_SPEC- (可选捕获
IMPL_DESIGN、TASKS用于未来的链式流程.)
- 如果 JSON 解析失败, 中止并指示用户重新运行
/omni-dsdd:specify或验证功能分支环境. - 对于参数中包含单引号的情况(如 "I'm Groot"), 使用转义语法: 例如 'I'''m Groot'(或优先使用双引号: "I'm Groot").
2. 结构化模糊性扫描
加载当前规范文件. 使用以下分类法执行扫描, 对每个类别标记状态: 清晰 / 部分 / 缺失. 生成内部覆盖范围图(除非不会提问, 否则不输出原始图).
功能范围与行为:
- 核心用户目标和成功标准
- 明确的超出范围声明
- 用户角色 / 角色区分
领域与数据模型:
- 实体、属性、关系
- 身份和唯一性规则
- 生命周期 / 状态转换
- 数据量 / 规模假设
交互与 UX 流程:
- 关键用户旅程 / 序列
- 错误 / 空白 / 加载状态
- 可访问性或本地化说明
非功能性质量属性:
- 性能(延迟、吞吐量目标)
- 可扩展性(水平 / 垂直、限制)
- 可靠性和可用性(正常运行时间、恢复期望)
- 可观察性(日志、指标、追踪信号)
- 安全性和隐私(身份验证 / 授权、数据保护、威胁假设)
- 合规性 / 监管约束(如有)
集成与外部依赖:
- 外部服务 / API 和故障模式
- 数据导入 / 导出格式
- 协议 / 版本控制假设
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.
- 10d ago First seen · 258 lines · 81 tokens per session scan A 71a4dd6551ff
clarify is a skill published in the GitHub repository ZTE-AICloud/Co-OmniSpec (54 stars, last pushed 1mo ago), licensed MIT. It adds 81 tokens to every session and 3,336 once invoked, about $0.0004 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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