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文章写作

撰写文章、指南、博客文章、教程、通讯稿及其他长格式内容,能够根据提供的范例或品牌指南形成独特的声音。当用户需要超过段落长度的精美书面内容,且重视声音一致性、结构和可信度时使用。

文章写作 是什么?

该技能专为需要创建专业、高质量长格式内容的开发者或AI Agent用户设计。它并非生成通用的AI文本,而是专注于产出具有真实人类声音和观点的文章,能有效避免常见的AI写作痕迹。核心优势在于其严格的“声音处理”机制,能通过`brand-voice`工具分析并保持与创始人、运营者或品牌一致的独特风格。此外,它内置了对抗“AI腔调”的规则,例如禁止使用“在当今快速发展的格局下”等陈词滥调,并要求使用证据而非形容词来建立可信度。该技能支持多种平台(如Claude Code、Cursor),提供针对技术指南、观点文章、新闻通讯等不同文体的结构化写作指导,并设有交付前的“质量门”,确保事实有据、逻辑严密且格式匹配目标媒介。

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

在你喜欢的 AI 中提问

打开一个已预加载此 Agent Skill 的新对话。

文档

文章写作 是做什么的?

撰写听起来像一个真实的人、有自己观点的长格式内容,而不是一个把自己磨平了的大语言模型。

何时激活

  • 起草博客文章、散文、发布帖、指南、教程或新闻通讯
  • 将笔记、转录稿或研究材料打磨成文章
  • 根据范例匹配现有的创始人、运营者或品牌声音
  • 收紧已写好的长格式文案中的结构、节奏和论据

核心规则

  1. 用具体的东西开头:成品、例子、输出、轶事、数字、截图或代码。
  2. 先举例,再解释。
  3. 保持句子紧凑,除非源声音故意要求铺陈。
  4. 用证据代替形容词。
  5. 绝不捏造事实、可信度或客户案例。

声音处理

如果用户想要特定的声音,请先运行 brand-voice 并复用其 VOICE PROFILE。 除非用户明确要求,否则不要在此处重复第二次风格分析。

如果没有提供声音参考,默认使用一种尖锐的运营者声音:具体、不感情用事、实用。

禁止模式

删除并重写以下任何内容:

  • "在当今快速发展的格局下"
  • "游戏规则改变者"、"前沿"、"革命性"
  • 作为独立过渡句的“以下是为什么这很重要”
  • 虚假的脆弱性展示
  • 只是为了增加互动而添加的结尾问题
  • 对论证没有帮助的个人简介赘述
  • 延迟切入正题的通用AI开场白

写作过程

  1. 明确受众和目的。
  2. 构建一个硬性大纲,每个章节只有一个任务。
  3. 用证据、成品、冲突或例子开始每个章节。
  4. 仅当下一个句子值得占据空间时才进行扩展。
  5. 删除任何听起来像模板、过度解释或自我赞美的东西。

结构指导

技术指南

  • 以读者能获得什么开头
  • 在主要章节中使用代码、命令、截图或具体的输出
  • 以可操作的要点结束,而不是敷衍的总结

散文 / 观点

  • 从紧张、矛盾或具体的观察开始
  • 每个章节只保持一个论点线索
  • 让观点回答证据

新闻通讯

  • 让第一屏幕做真正的工作
  • 不要用日记填充物作为开头
  • 仅在使用章节标签能提高可扫描性时使用它们

质量门

在交付之前:

  • 事实性主张有提供的来源作为支撑
  • 通用的AI过渡句已经删除
  • 声音与提供的范例或商定的 VOICE PROFILE 一致
  • 每个章节都添加了新的内容
  • 格式与预期媒介相匹配

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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