Communitygithub.com

文章寫作

撰寫文章、指南、部落格貼文、教學、電子報及其他長篇內容,並根據提供的範例或品牌指引,呈現獨特的語氣。當使用者需要比段落更長的精煉書面內容,且語氣一致性、結構與可信度至關重要時使用。

文章寫作 是什麼?

這個「文章寫作」技能專為需要撰寫長篇內容(如部落格貼文、技術指南、評論文章及電子報)的開發者與AI代理使用者設計。它強調產出具有真實人類觀點而非機器平滑化痕跡的文字。技能的核心是優先使用具體證據(如範例、數據或程式碼)而非形容詞,並嚴格禁止誇大或空洞的修辭。它支援多種平台(如Claude Code、Cursor),並可透過「brand-voice」技能整合品牌語氣分析。工作流程包含釐清受眾、建立硬性大綱、以證據開頭,並在交付前進行品質把關,確保事實有依據、語氣一致且格式適當。此技能對於需要權威、可信且結構清晰的長篇內容非常有用,尤其是在品牌溝通或技術寫作場景中。

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

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

文章寫作 是做什麼的?

撰寫聽起來像真人、有自己的觀點,而不是像LLM把自己平滑成糊狀的長篇內容。

何時啟動

  • 起草部落格貼文、文章、產品發布文、指南、教學或電子報
  • 將筆記、逐字稿或研究資料轉化為精煉文章
  • 從範例中比對現有創辦人、經營者或品牌語氣
  • 在已寫好的長篇文案中緊縮結構、節奏與證據

核心規則

  1. 以具體事物開頭:成品、範例、輸出、趣聞、數字、截圖或程式碼。
  2. 先舉例再解釋,而非反過來。
  3. 除非來源語氣刻意豪放,否則保持句子精簡。
  4. 用證據取代形容詞。
  5. 絕不虛構事實、可信度或客戶證據。

語氣處理

如果使用者想要特定語氣,先執行 brand-voice,並重複使用其產生的 VOICE PROFILE。 除非使用者明確要求,否則不要在此處進行第二次風格分析。

如果沒有提供語氣參考,預設使用銳利的經營者語氣:具體、不濫情、實用。

禁止模式

刪除並改寫以下任何內容:

  • 「在現今快速變化的環境中」
  • 「遊戲改變者」、「尖端」、「革命性」
  • 「這就是為什麼這很重要」作為獨立轉折句
  • 虛假的脆落感敘事
  • 僅為了刺激互動而添加的結尾提問
  • 不推進論點的傳記灌水
  • 延遲切入重點的通用AI開場白

寫作流程

  1. 釐清目標讀者與寫作目的。
  2. 建立硬大綱,每個段落承擔一個任務。
  3. 以證據、成品、衝突或範例開啟段落。
  4. 只在下一句話值得佔用篇幅時才擴展內容。
  5. 刪除任何聽起來像模板、過度解釋或自鳴得意的內容。

結構指引

技術指南

  • 以讀者能得到什麼開頭
  • 在主要章節中使用程式碼、指令、截圖或具體輸出
  • 以可行動的總結結尾,而非軟性回顧

評論 / 意見

  • 以張力、矛盾或特定觀察開頭
  • 每章節保持單一論點主軸
  • 讓意見回應證據

電子報

  • 讓第一屏內容發揮實際作用
  • 不要在前段堆砌日記式的填充內容
  • 僅在可改善掃讀性時使用章節標籤

品質閘門

交付前檢查:

  • 事實主張有提供的來源支持
  • 通用AI轉折已消失
  • 語氣與提供的範例或已同意的 VOICE PROFILE 一致
  • 每個章節都增添新內容
  • 格式符合預期的媒體類型

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.

相關技能