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 malue-ai/dazee-small --skill cross-app-workflowgit clone --depth 1 https://github.com/malue-ai/dazee-smallWrote 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/malue-ai/dazee-small/cross-app-workflow)<a href="https://agentmods.dev/skills/malue-ai/dazee-small/cross-app-workflow"><img src="https://agentmods.dev/badge/skills/malue-ai/dazee-small/cross-app-workflow.svg" alt="Measured on agentmods" 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 | $0.00035 | $0.00860 |
| Opus 5 | $0.00017 | $0.00430 |
| Sonnet 5 | $0.00007 | $0.00172 |
| Haiku 4.5 | $0.00003 | $0.00086 |
Grade A, and why
cross-app-workflow 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 7d 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.
What it actually says
跨应用工作流
将多个本地应用和 Skill 串联成复杂的多步骤工作流，完成端到端的自动化任务。
使用场景
- 用户说「把邮件里的附件提取出来，分析 Excel，生成报告，再回复邮件」
- 用户说「从这 5 个 PDF 里提取数据，汇总到 Excel，做个图表」
- 用户说「把会议纪要整理成行动项，加到日历，通知参会者」
- 用户说「分析这个文件夹的数据，生成 PPT，保存到桌面」
执行方式
这是一个编排型 Skill，本身不执行具体操作，而是指导 Agent 如何将多个 Skill 串联使用。
工作流编排原则
- 理解完整目标：分析用户请求，识别需要的所有步骤
- 拆解任务链：将目标分解为有序的子任务
- 选择合适 Skill：每个子任务匹配最佳 Skill
- 传递中间结果：上一步的输出作为下一步的输入
- 错误恢复：某步失败时尝试替代方案
常见工作流模板
数据分析 → 报告生成
Step 1: [excel-analyzer] 读取并分析数据文件
Step 2: [LLM] 基于分析结果生成洞察和结论
Step 3: [elegant-reports / word-processor] 生成格式化报告
Step 4: [file-manager] 保存到用户指定位置
邮件处理 → 任务分发
Step 1: [himalaya / outlook-cli] 读取邮件内容和附件
Step 2: [LLM] 提取关键信息和行动项
Step 3: [meeting-notes-to-action-items] 结构化行动项
Step 4: [apple-calendar / outlook-cli] 创建日程提醒
Step 5: [himalaya / outlook-cli] 起草并发送回复
内容创作 → 多平台分发
Step 1: [writing-assistant] 撰写长文
Step 2: [humanizer] 去 AI 味润色
Step 3: [content-reformatter] 适配各平台格式
Step 4: [file-manager] 保存各版本到对应文件夹
文献调研 → 报告
Step 1: [paper-search / arxiv-search] 搜索相关论文
Step 2: [deep-doc-reader] 深度阅读关键论文
Step 3: [literature-reviewer] 对比分析多篇文献
Step 4: [word-processor] 生成文献综述报告
中间结果管理
- 每步产生的文件保存到
~/Desktop/xiaodazi_workflow/临时目录 - 工作流完成后提醒用户检查中间文件是否需要保留
- 大数据中间结果写入文件，不全部放入上下文
安全规则
- 每步执行前展示计划：让用户了解接下来的操作
- 敏感操作需确认：发送邮件、删除文件等需用 HITL 确认
- 错误不静默跳过：某步失败时通知用户并提供替代方案
输出规范
- 开始前展示完整工作流计划（步骤列表）
- 每步完成后报告进度
- 全部完成后给出总结：执行了哪些操作、生成了哪些文件
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.
- 7d ago First seen · 91 lines · 35 tokens per session scan A a7a85e09d749
cross-app-workflow is a skill published in the GitHub repository malue-ai/dazee-small (36 stars, last pushed 5mo ago), licensed MIT. It adds 35 tokens to every session and 860 once invoked, about $0.0002 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.
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