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messaging-ops

證據優先的即時訊息工作流程,適用於讀取文字訊息或 DM、找回近期的一次性驗證碼、在回覆前檢查對話串,或證明實際檢查過哪個訊息來源。

messaging-ops 是什麼?

此技能提供一個以證據為基礎的即時訊息檢索工作流程,涵蓋 iMessage、社交平台 DM、近期一次性驗證碼、以及在後續操作前進行對話串檢查等任務。它與電子郵件操作(如 email-ops)明確區分,並能與其他技能(如 connections-optimizer、lead-intelligence)協作處理跨領域任務。工作流程強調先明確解析確切的訊息來源(如本地訊息、X / 社交 DM 等),然後進行檢索或檢查,最後輸出包含來源、結果與狀態的結構化證據,避免模糊或未經確認的來源聲明。此技能支援 Claude Code、Cursor、Codex 等 AI 代理平台,透過清晰步驟與驗證機制確保訊息操作的準確性與可追溯性。

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

在你喜歡的 AI 中提問

開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

訊息操作 (Messages Ops)

當任務涉及即時訊息檢索時使用:iMessage、DM、近期一次性驗證碼,或在跟進回覆前檢查對話串。

這不是電子郵件工作。若主要資訊來源是電子郵件,請使用 email-ops。

技能堆疊 (Skill Stack)

在相關情況下,將這些 ECC 原生技能引入工作流程:

  • email-ops:當訊息任務實際上屬於郵件工作時
  • connections-optimizer:當 DM 對話串屬於對外網路工作時
  • lead-intelligence:當即時對話串應作為目標設定或溫暖途徑外展的依據時
  • knowledge-ops:當對話串內容需要擷取到持久化的上下文時

使用時機

  • 使用者說「讀我的訊息」、「檢查文字」、「查看 DM」或「找到驗證碼」
  • 任務依賴即時對話串或傳送到本地訊息介面的近期驗證碼
  • 使用者希望證明實際檢查過哪個來源或對話串

護欄 (Guardrails)

  • 先確認來源:
    • 本地訊息
    • X / 社交 DM
    • 其他需透過瀏覽器存取的訊息介面
  • 未指定來源前,不得聲稱已檢查過某個對話串
  • 如果存在已驗證的輔助工具或標準路徑,不得擅自使用原始資料庫存取
  • 如果身份驗證或 MFA 阻擋了存取,請回報確切的阻擋原因

工作流程 (Workflow)

1. 解析確切的對話串

在進行任何其他操作之前,先確定:

  • 訊息介面
  • 寄件人 / 收件人 / 服務
  • 時間範圍
  • 任務是檢索、檢查,還是為回覆做準備

2. 在撰寫草稿前先讀取

如果任務可能轉變為對外回覆:

  • 讀取最新的進站訊息
  • 確定未結的待辦事項
  • 然後,如有需要,將任務交給正確的對外技能處理

3. 將驗證碼視為專注的檢索任務

針對一次性驗證碼:

  • 先搜尋近期的本地訊息視窗
  • 盡可能按服務或寄件人縮小範圍
  • 一旦找到驗證碼或精確搜尋完畢,即停止

4. 回報確切證據

回傳:

  • 使用的來源
  • 可能的話,包含對話串或寄件人
  • 時間範圍
  • 確切狀態:
    • 已讀取 (read)
    • 已找到驗證碼 (code-found)
    • 被阻擋 (blocked)
    • 等待回覆草稿 (awaiting reply draft)

輸出格式

SOURCE
- 訊息介面
- 寄件人 / 對話串 / 服務

RESULT
- 訊息摘要或驗證碼
- 時間範圍

STATUS
- read / code-found / blocked / awaiting reply draft

常見陷阱 (Pitfalls)

  • 不要混淆郵件工作和 DM / 文字訊息工作
  • 未指定來源前,不得聲稱已檢索
  • 當任務是查找近期驗證碼時,不要在廣泛的搜尋上浪費時間
  • 不要在不回報阻擋原因的情況下,重複嘗試被攔截的身份驗證路徑

驗證 (Verification)

  • 回應中須指名訊息來源
  • 回應中須包含寄件人、服務、對話串,或明確的阻擋原因
  • 最終狀態須清楚明確且有範圍限制

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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