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消息运维

面向ECC的基于证据的实时消息工作流。当用户想要阅读文本或私信、找回近期的一次性验证码、在回复前检查对话线程,或证明实际检查了哪个消息来源时使用。

消息运维 是什么?

该技能(messages-ops)专为实时消息检索场景设计,涵盖iMessage、私信(DM)、近期一次性验证码以及线程检查等任务。它强调基于证据的工作流,要求明确识别消息来源(如本地消息、社交媒体私信或浏览器受限消息界面),并在操作后提供精确的状态报告(如“已读取”、“已找到验证码”、“受阻”等)。主要支持ECC平台,并可与email-ops、connections-optimizer、lead-intelligence和knowledge-ops等其他技能协同工作。其核心价值在于为涉及即时消息的任务提供结构化、可验证的流程,避免与邮件任务混淆,并且始终要求报告实际检查的来源,从而确保结果的可追溯性和准确性。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/messages-ops

在你喜欢的 AI 中提问

打开一个已预加载此 Agent Skill 的新对话。

文档

消息运维 是做什么的?

当任务是实时消息检索时使用:iMessage、私信(DM)、近期一次性验证码,或在后续操作前检查线程。

这不是邮件类工作。如果主要界面是邮箱,请使用 email-ops。

技能栈

在相关时将以下ECC原生技能引入工作流:

  • email-ops — 当消息任务实际上是邮箱操作时
  • connections-optimizer — 当DM线程属于对外网络工作时
  • lead-intelligence — 当实时线程应为目标定位或预热式外联提供信息时
  • knowledge-ops — 当线程内容需要捕获到持久上下文时

使用时机

  • 用户说“读我的消息”、“检查短信”、“查看私信”或“找到验证码”
  • 任务依赖于某个实时线程或已交付到本地消息界面的近期验证码
  • 用户希望证明检查了哪个来源或线程

安全护栏

  • 首先明确来源:
    • 本地消息
    • X / 社交媒体私信
    • 其他浏览器限制的消息界面
  • 未指定来源时,不得声称已检查某线程
  • 如果存在已检查的辅助工具或标准路径,不得擅自进行原始数据库访问
  • 如果认证或MFA阻挡了访问,需报告确切的障碍信息

工作流

1. 确定具体线程

在进行任何操作之前,先明确:

  • 消息界面
  • 发送者/接收者/服务
  • 时间窗口
  • 任务是检索、检查还是回复准备

2. 先读取,后起草

如果任务可能转为对外跟进:

  • 读取最新入站消息
  • 识别未完成的环节
  • 然后按需移交至相应的对外技能

3. 将验证码视为聚焦检索任务

对于一次性验证码:

  • 首先搜索近期的本地消息窗口
  • 尽可能按服务或发送者缩小范围
  • 一旦找到验证码或聚焦搜索已穷尽即停止

4. 报告精确证据

返回:

  • 使用过的来源
  • 可能时提供线程或发送者
  • 时间窗口
  • 精确状态:
    • 已读取
    • 已找到验证码
    • 受阻
    • 等待起草回复

输出格式

来源
- 消息界面
- 发送者/线程/服务

结果
- 消息摘要或验证码
- 时间窗口

状态
- 已读取 / 已找到验证码 / 受阻 / 等待起草回复

常见陷阱

  • 不要混淆邮箱工作和私信/短信工作
  • 未指定来源时,不得声称已检索
  • 当需求是查找近期验证码时,不要浪费时间在广泛搜索上
  • 不要反复重试受阻的认证路径而不报告障碍

验证

  • 响应中应指明消息来源
  • 响应中应包含发送者、服务、线程或明确的受阻信息
  • 最终状态应明确且有边界

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.

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