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Google Workspace 操作

將 Google 雲端硬碟、文件、試算表和簡報整合為單一工作流程,用於管理計畫、追蹤器、簡報和共享文件。當使用者需要尋找、摘要、編輯、遷移或清理 Google Workspace 資產,而不想依賴原始工具呼叫時使用。

Google Workspace 操作 是什麼?

這個技能是為了將 Google Workspace 的共享文件、試算表和簡報當作一個完整的系統來操作,而非只是孤立地編輯單一檔案。它能協助開發者和使用者有效率地進行文件管理,例如在 Google 雲端硬碟中尋找正確的檔案、在編輯前先檢查結構以避免錯誤、精準地對文件、試算表或簡報進行修改,並確保整個工作流程的整潔。此技能支援 Google 文件、試算表和簡報等所有主要服務。其核心工作流程包括:先透過雲端硬碟搜尋找到資產,接著檢查其結構,然後進行精準的編輯操作,最後清理無用的重複或過時資產。它非常適合用於尋找並更新計畫文件、整理客戶試算表、匯入並美化簡報,以及清理重複的追蹤器與舊版文件。

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

Google Workspace 操作 是做什麼的?

此技能旨在將共享文件、試算表和簡報視為一個工作系統來操作,而不僅僅是孤立地編輯單一檔案。

使用時機

  • 使用者需要尋找文件、試算表或簡報並就地更新
  • 整合存放在 Google 雲端硬碟中的計畫、追蹤器、筆記或客戶清單
  • 清理或重整共享試算表
  • 匯入、修復或重新格式化 Google 簡報
  • 從文件、試算表或簡報產出摘要以供決策

偏好的工具範圍

使用 Google 雲端硬碟作為入口,然後切換到合適的專用工具:

  • Google 文件:適用於文字密集型文件
  • Google 試算表:適用於表格作業、公式和圖表
  • Google 簡報:適用於簡報、匯入、範本遷移和清理

不要僅根據檔名猜測結構。請先檢查。

工作流程

1. 尋找資產

從雲端硬碟搜尋介面開始,找出:

  • 確切的檔案
  • 同類的資產
  • 可能的重複檔案
  • 最近修改過的版本

如果幾個文件看起來相似,請透過標題、擁有者、修改時間或資料夾來確認。

2. 編輯前先檢查

在進行修改之前:

  • 摘要目前結構
  • 識別分頁、標題或投影片數量
  • 判斷任務是局部清理還是結構性調整

選擇能安全完成工作的最小工具。

3. 精準編輯

  • 對於文件:使用索引感知的編輯,避免模糊重寫
  • 對於試算表:在明確的分頁和範圍上操作
  • 對於簡報:區分內容編輯、視覺清理或範本遷移

如果需求涉及視覺或版面配置,請透過檢查與驗證反覆進行,而非單次大規模盲目更新。

4. 保持工作系統整潔

當檔案屬於較大的工作流程時,也請一併找出:

  • 重複的追蹤器
  • 過時的簡報
  • 舊版文件與正式文件的差異
  • 資產是否應歸檔、合併或重新命名

輸出格式

使用:

資產
- 檔案名稱
- 類型
- 為何這是正確的檔案

當前狀態
- 結構摘要
- 主要問題或阻礙

操作
- 已執行或建議的編輯

後續行動
- 歸檔 / 合併 / 清理重複 / 下一個要更新的檔案

良好使用案例

  • 「找到活躍的規劃文件並濃縮它」
  • 「整理這個客戶試算表,並向我展示流失風險較高的列」
  • 「將這個簡報匯入到 Google 簡報並讓它變得可展示」
  • 「找到當前的追蹤器,而不是過時的重複檔案」

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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