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Google Workspace 操作技能

将 Google 云端硬盘、文档、表格和幻灯片整合为一个工作流界面,用于管理计划、跟踪器、演示文稿和共享文档。当用户需要查找、总结、编辑、迁移或清理 Google Workspace 资产而无需依赖原始工具调用时使用。

Google Workspace 操作技能 是什么?

此技能旨在将 Google Workspace(云端硬盘、文档、表格、幻灯片)转变为统一的工作流界面,而不是孤立地编辑单个文件。 它支持开发者或 AI 代理用户查找、检查、精确编辑以及清理 Google Workspace 资产。 该技能强调使用云端硬盘作为入口点,然后根据任务类型(文本密集型选择文档,表格工作选择表格,演示文稿选择幻灯片)切换到合适的工具,并强制要求在编辑前先检查结构。 其工作流程包括:查找资产(通过云端硬盘搜索)、编辑前检查(总结结构、识别标签/标题)、精确编辑(索引感知、显式范围和区分内容与视觉编辑),以及保持工作系统清洁(识别重复项、过期文件、建议存档/合并)。 输出格式为结构化的文本块,包含资产信息、当前状态、执行的操作和后续步骤。 典型用例包括:查找并精简活动计划文档、清理客户表格并显示流失风险行,以及导入演示文稿并美化。

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

在你喜欢的 AI 中提问

打开一个已预加载此 Agent Skill 的新对话。

文档

Google Workspace 操作技能 是做什么的?

本技能用于将共享文档、电子表格和演示文稿作为工作系统进行操作,而不仅仅是在隔离环境中编辑单个文件。

何时使用

  • 用户需要查找文档、表格或演示文稿并直接原地更新
  • 整合存储在 Google 云端硬盘中的计划、跟踪器、笔记或客户列表
  • 清理或重构共享电子表格
  • 导入、修复或重新格式化 Google 幻灯片演示文稿
  • 根据文档、表格或幻灯片生成摘要以辅助决策

首选工具界面

使用 Google 云端硬盘作为入口点,然后切换到合适的专业工具:

  • Google 文档:适用于文本密集型文档
  • Google 表格:适用于表格工作、公式和图表
  • Google 幻灯片:适用于演示文稿、导入、模板迁移和清理

不要仅凭文件名猜测结构。请先检查。

工作流程

1. 查找资产

从云端硬盘搜索界面开始定位:

  • 确切文件
  • 同级资产
  • 可能存在的重复项
  • 最近修改的版本

如果多个文档看起来相似,请通过标题、所有者、修改时间或文件夹进行确认。

2. 编辑前检查

在进行更改之前:

  • 总结当前结构
  • 识别标签、标题或幻灯片数量
  • 判断任务是局部清理还是结构性修改

选择能够安全完成工作的最小工具。

3. 精确编辑

  • 对于文档:使用索引感知型编辑,而非模糊重写
  • 对于表格:在明确的标签页和范围内操作
  • 对于幻灯片:区分内容编辑、视觉清理和模板迁移

如果请求的工作是视觉或布局敏感的,请通过检查和验证迭代进行,而不是进行一次大型的盲目更新。

4. 保持工作系统清洁

当文件属于较大工作流的一部分时,还应指出:

  • 重复的跟踪器
  • 过期的演示文稿
  • 过时文档与权威文档
  • 资产是否应归档、合并或重命名

输出格式

使用:

资产
- 文件名
- 类型
- 为何这是正确的文件

当前状态
- 结构摘要
- 主要问题或障碍

操作
- 已执行的编辑或建议

后续操作
- 归档 / 合并 / 重复清理 / 下一个要更新的文件

良好用例

  • "查找活动计划文档并精简它"
  • "清理此客户表格并显示流失风险行"
  • "将此演示文稿导入幻灯片并使其适宜展示"
  • "查找当前跟踪器,而非过时的副本"

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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