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统一通知运维

将 GitHub、Linear、桌面提醒、Webhook 及连接的通告界面纳入单一 ECC 原生工作流进行通知运维。当真实问题是告警路由、去重、升级或收件箱崩塌时使用。

统一通知运维 是什么？

Unified Notifications Ops 技能旨在解决通知系统碎片化问题，将散落的事件整合为一个统一的运维界面。它支持 GitHub、Linear、本地 Hook、桌面提醒、聊天和电子邮件等平台，通过清晰界定严重等级、负责人、路由和后续操作，帮助团队从噪声中提取可执行信息。该技能包含完整的事件管道：捕获、分类、路由、折叠（去重和低信号抑制）以及附加操作。默认使用四级严重模型（关键、高、中、低），并强调摘要优先于即时打断。输出形式为结构化的路由方案，包括当前清单、事件模型、合并计划和下一步需构建的 ECC 组件。建议在现有工具链上构建，优先使用 ECC 原生编排而非引入第三方通知产品，同时确保不暴露密钥和内部标识符。

兼容平台✓Claude Code✓Codex CLI~Cursor✓OpenCode
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/unified-notifications-ops
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统一通知运维 是做什么的？

当真实问题不是缺少一次提醒，而是通知系统碎片化时，使用此技能。

目标是将散落的事件转化为一个统一运维界面，包含：

  • 清晰的严重等级
  • 清晰的责任人
  • 清晰的路由
  • 清晰的后续操作

何时使用

  • 用户希望跨 GitHub、Linear、本地 Hook、桌面提醒、聊天或邮件建立一个统一通知通道
  • CI 失败、审查请求、问题更新和运维事件抵达在互不相连的位置
  • 当前设置产生的是噪声而非行动
  • 用户希望将重叠的通知分支或积压提案合并到一条 ECC 原生通道中
  • 工作区已有 Hook、MCP 或已连接的工具，但没有连贯的通知策略

首选界面

从已有工具出发：

  • GitHub 的 Issue、PR、审查、评论和 CI
  • Linear 的问题/项目变更
  • 本地 Hook 事件和会话生命周期信号
  • 桌面通知原语
  • 已连接的邮件/聊天界面（如果确实存在）

优先使用 ECC 原生编排，而非让用户采用单独的通知产品。

不可妥协的规则

  • 绝不暴露令牌、密钥、Webhook 密钥或内部标识符
  • 区分以下内容：
    • 事件来源
    • 严重等级
    • 路由通道
    • 运维操作
  • 当中断成本不明确时，默认采用摘要优先方式
  • 不要将每个事件广播到所有通道
  • 如果真正的解决方案是更好的问题分类、Hook 策略或项目流程，请明确说明

事件管道

将通道视为：

  1. 捕获事件
  2. 分类紧急程度和负责人
  3. 路由到正确通道
  4. 折叠重复和低信号干扰
  5. 附加下一步运维操作

目标是更少但更优质的通知。

默认严重模型

等级示例默认处理方式
关键默认分支 CI 失败、安全问题、版本发布受阻、部署失败立即中断
高审查已请求、PR 失败、等待负责人交接当天告警
中Issue 状态变化、重要评论、积压移动摘要或队列
低重复成功、常规干扰、冗余生命周期标记抑制或折叠

如果工作区没有严重模型，请先构建一个，然后再提议自动化方案。

工作流

1. 盘点当前界面

列出：

  • 事件来源
  • 当前通道
  • 已有的发出告警的 Hook/脚本
  • 同一事件的重复路径
  • 重要事项未被发现的静默失败情况

指出 ECC 已拥有哪些部分。

2. 决定哪些事项值得中断

针对每一类事件，回答：

  • 谁需要知道？
  • 他们需要多快知道？
  • 应中断、批处理还是仅记录？

使用以下默认规则：

  • 版本发布、CI、安全及阻塞负责人事件应中断
  • 中等信号更新应以摘要形式呈现
  • 遥测和低信号生命周期标记仅记录

3. 在添加通道之前先折叠重复项

寻找：

  • 同一 PR 事件同时出现在 GitHub、Linear 和本地日志中
  • 同一失败反复触发 Hook 通知
  • 应被汇总而不必逐条转发的评论或状态变化
  • 没有提供更好操作路径的重复通道

优先：

  • 一份规范化摘要
  • 一个负责人
  • 一条主要通道
  • 一条备用路径

4. 设计 ECC 原生工作流

为每个真实通知需求定义：

  • 来源
  • 门控
  • 形态：即时告警、摘要、队列或仅仪表盘
  • 通道
  • 操作

如果 ECC 已有原语，优先使用：

  • 用于运维人员分类的技能
  • 用于自动触发/强制的 Hook
  • 用于委托分类的智能体
  • 仅在确实缺少桥梁时才使用 MCP/连接器

5. 返回以行动为导向的设计

最后输出：

  • 保留什么
  • 抑制什么
  • 合并什么
  • ECC 下一步应包装什么

输出格式

当前界面
- 来源
- 通道
- 重复项
- 缺口

事件模型
- 关键
- 高
- 中
- 低

路由方案
- 来源 -> 通道
- 原因
- 运维负责人

合并方案
- 抑制
- 合并
- 规范化摘要

下一步 ECC 操作
- 技能 / Hook / 智能体 / MCP
- 下一步要构建的确切工作流

推荐规则

  • 优先选择一条强壮的通道，而非多条弱通道
  • 中低信号更新优先使用摘要
  • 当信号应自动发出时，优先使用 Hook
  • 当工作是分类、路由和审查优先决策时，优先使用运维人员技能
  • 当根本原因是积压/PR 协调而非告警时，优先使用 project-flow-ops
  • 当用户首先需要来源清单时，优先使用 workspace-surface-audit
  • 如果桌面通知足够，不要发明不必要的外部桥梁

良好用例

  • “我们有 GitHub、Linear 和本地 Hook 告警，但没有统一的运维流程”
  • “我们的 CI 失败很嘈杂，大家都忽略”
  • “我想要一个跨 Claude、OpenCode 和 Codex 界面的统一通知策略”
  • “弄清楚哪些事项应该中断，哪些应该落入摘要”
  • “将重叠的通知 PR 想法合并到一条规范的 ECC 通道中”

相关技能

  • workspace-surface-audit
  • project-flow-ops
  • github-ops
  • knowledge-ops
  • customer-billing-ops：当通知痛点来自计费/客户运维而非工程时使用

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

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