Communitygithub.com

统一通知运维

将通知作为一个ECC本地工作流统一管理，涵盖GitHub、Linear、桌面提醒、钩子和连接的通信界面。当真正的问题在于警报路由、去重、升级或收件箱崩溃时使用。

统一通知运维 是什么？

该技能解决通知系统碎片化的问题，旨在将分散的事件整合到一个统一的运维界面上，实现明确的严重性、所有权、路由和后续行动。它适用于用户在GitHub、Linear、本地钩子、桌面提醒、聊天或邮件等多个平台拥有通知，但缺乏统一策略的场景。技能的核心流程包括事件捕获、分类、路由、去重和附加操作，并提供了默认的严重性模型（关键、高、中、低）来处理不同级别的事项。它强调ECC本地编排，优先使用技能、钩子和代理，而非引入独立的通知产品，最终输出一个包含当前界面、事件模型、路由计划和下一步ECC动作的具体方案。

兼容平台✓Claude Code✓Codex CLI~Cursor✓OpenCode
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/unified-notifications-ops
查看原文→浏览所有技能

在你喜欢的 AI 中提问

打开一个已预加载此 Agent Skill 的新对话。

OpenAIChatGPTClaudeClaudeGeminiGeminiGrokGrokPerplexityPerplexityDeepSeekDeepSeek

文档

统一通知运维 是做什么的？

当真正的问题不是缺少提醒，而是通知系统碎片化时使用此技能。

任务是将分散的事件整合到一个运维界面上，做到：

  • 明确的严重性
  • 明确的所有权
  • 明确的路由
  • 明确的后续行动

何时使用

  • 用户希望在 GitHub、Linear、本地钩子、桌面提醒、聊天或邮件之间建立统一的通知通道
  • CI 失败、审查请求、问题更新和运维事件出现在不同的地方
  • 当前配置产生的不是行动，而是噪音
  • 用户希望将重叠的通知分支或积压方案合并到一个 ECC 本地通道
  • 工作区已有钩子、MCP 或连接工具，但没有一致的通知策略

首选界面

从已有的内容开始：

  • GitHub 问题、PR、审查、评论和 CI
  • Linear 问题/项目状态变更
  • 本地钩子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（如果实际存在）

优先使用 ECC 本地编排，而非建议用户采用独立的通知产品。

不可妥协的规则

  • 绝不暴露令牌、密钥、webhook 密钥或内部标识符
  • 分离：
    • 事件来源
    • 严重性
    • 路由通道
    • 运维操作
  • 当打断成本不明确时，默认使用摘要优先方式
  • 不要将每个事件广播到每个通道
  • 如果真正的解决方法是更好的问题分类、钩子策略或项目流程，请明确说明

事件流水线

将通道视为：

  1. 捕获事件
  2. 分类紧急程度和负责人
  3. 路由到正确的通道
  4. 去重并压缩低信噪波动
  5. 附加下一个运维操作

目标是更少、更好的通知。

默认严重性模型

级别示例默认处理方式
关键默认分支 CI 损坏、安全问题、版本发布受阻、部署失败立即打断
高审查请求、失败的 PR、需负责人处理的交接当日内提醒
中问题状态变更、重要评论、积压变动摘要或队列
低重复成功、常规波动、冗余的生命周期标记抑制或折叠

如果工作区没有严重性模型，在提出自动化方案前先构建一个。

工作流程

1. 盘点当前界面

列出：

  • 事件来源
  • 当前通道
  • 发送提醒的现有钩子/脚本
  • 同一事件的重复路径
  • 重要事项未被呈现的静默失败案例

指出 ECC 已经拥有的内容。

2. 确定哪些值得打断

针对每个事件类别，回答：

  • 谁需要知道？
  • 他们需要多快知道？
  • 这应该打断、批量处理、还是仅记录？

使用以下默认值：

  • 打断：版本发布、CI、安全问题和需负责人处理的事件
  • 摘要：中等信噪比的更新
  • 仅记录：遥测和低信噪比生命周期标记

3. 在添加通道之前先进行去重

查找：

  • 同一个 PR 事件同时出现在 GitHub、Linear 和本地日志中
  • 同一失败的重复钩子通知
  • 应当总结而非直接转发的评论或状态波动
  • 相互重复且未提供更好操作路径的通道

优先使用：

  • 一个权威摘要
  • 一个负责人
  • 一个主通道
  • 一个备用路径

4. 设计 ECC 本地工作流

针对每个真实的通知需求，定义：

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

如果 ECC 已有原语，优先使用：

  • 一个用于运维分类的技能
  • 一个用于自动发送/执行的钩子
  • 一个用于委托分类的代理
  • 仅在真正缺少桥接时才使用 MCP/连接器

5. 返回一个面向行动的设计

最终输出：

  • 保留什么
  • 抑制什么
  • 合并什么
  • ECC 下一步应封装什么

输出格式

当前界面
- 来源
- 通道
- 重复项
- 空白

事件模型
- 关键
- 高
- 中
- 低

路由计划
- 来源 -> 通道
- 原因
- 运维负责人

整合
- 抑制
- 合并
- 权威摘要

下一步 ECC 动作
- 技能 / 钩子 / 代理 / MCP
- 下一步要构建的确切工作流

推荐规则

  • 倾向于一条强的通道，而非多条弱通道
  • 倾向于中等和低信噪比更新的摘要
  • 当信号应自动发送时，倾向于使用钩子
  • 当工作是分类、路由和审查优先的决策时，倾向于使用运维技能
  • 当根本原因是积压/PR 协调而非提醒时，优先使用 project-flow-ops
  • 当用户首先需要来源盘点时，优先使用 workspace-surface-audit
  • 如果桌面提醒就够了，不要创造不必要的外部桥接

好的用例

  • “我们有 GitHub、Linear 和本地钩子提醒，但没有统一的运维流程”
  • “我们的 CI 失败很嘈杂，人们都忽略了”
  • “我想要一个在 Claude、OpenCode 和 Codex 界面间统一的通知策略”
  • “找出哪些应该打断，哪些应该放入摘要”
  • “将重叠的通知 PR 想法合并成一个权威的 ECC 通道”

相关技能

  • workspace-surface-audit
  • project-flow-ops
  • github-ops
  • knowledge-ops
  • 当通知痛点属于账单/客户运维而非工程时，使用 customer-billing-ops

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/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳：このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

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.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

相关技能

steipete/video-frames

Extract frames or short clips from videos using ffmpeg.

community

steipete/summarize

Summarize or transcribe URLs, YouTube/videos, podcasts, articles, transcripts, PDFs, and local files.

community

openclaw/openclaw

Use when testing, fixing, or extending the OpenClaw Control UI GUI with Vitest + Playwright end-to-end checks, mocked Gateway WebSocket flows, mocked dashboard runs, screenshots/videos, or agent-verifiable browser proof.

community

obra/superpowers

Obra superpowers agent skills.

community

affaan-m/ECC

See, Understand, Act on video and audio. See- ingest from local files, URLs, RTSP/live feeds, or live record desktop; return realtime context and playable stream links. Understand- extract frames, build visual/semantic/temporal indexes, and search moments with timestamps and auto-clips. Act- transcode and normalize (codec, fps, resolution, aspect ratio), perform timeline edits (subtitles, text/image overlays, branding, audio overlays, dubbing, translation), generate media assets (image, audio...

community

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.

community