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affaan-m/内容引擎

为 X、LinkedIn、TikTok、YouTube、新闻通讯以及跨平台复用活动创建原生内容系统。当用户需要社交媒体帖子、话题、脚本、内容日历或将单一源素材无缝适配至多个平台时使用。

内容引擎 是什么?

affaan-m/content-engine 是一个 AI 代理技能,专为开发者和内容创作者设计,用于构建平台原生内容系统。它支持 X (Twitter)、LinkedIn、TikTok、YouTube、新闻通讯等多个主流平台,并能将单一源素材(如博客文章或视频)高效地适配为跨平台复用活动。该技能通过结构化的提示和模板,帮助用户生成社交媒体帖子、讨论串、脚本和内容日历,确保每个平台的内容都符合其独特的最佳实践和格式要求。其核心优势在于消除手动适配的繁琐工作,提升内容一致性与产出效率,从而让创作者能专注于策略和创意。该技能适用于 Cursor、Claude Code 等基于 LLM 的开发环境,通过读取用户的平台目标并提供定制化的输出模板来工作。

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

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

内容引擎 是做什么的?

为 X、LinkedIn、TikTok、YouTube、新闻通讯以及跨平台复用活动创建平台原生内容系统。当用户需要社交媒体帖子、话题、脚本、内容日历或将单一源素材无缝适配至多个平台时使用。

概述

affaan-m/content-engine 帮助你将原始内容转化为专为不同平台优化的社交帖子、脚本和内容策略。它确保你的内容在每个平台上都感觉原生且引人入胜。

用法

当用户表达以下需求时使用此技能:

  • 为社交媒体平台创建帖子、话题或脚本。
  • 从单一源素材(如博客文章、视频或文章)开发跨平台内容活动。
  • 生成内容日历或编辑计划。
  • 将内容从一个平台适配到另一个平台(例如,将 YouTube 视频脚本转化为 LinkedIn 帖子)。

平台支持

  • X (Twitter):短帖、讨论串、投票。
  • LinkedIn:长帖、文章、职业洞察。
  • TikTok:短视频脚本、趋势想法。
  • YouTube:视频剧本、社区帖子、描述。
  • 新闻通讯:电子邮件内容、主题行、预览文本。

工作原理

  1. 用户提供源内容或内容意图。
  2. 你可能需要询问用户目标平台和所需语气/风格。
  3. 该技能利用关于平台规范和内容最佳实践的提示来输出格式正确的原生内容。
  4. 对于跨平台活动,它会建议在保留核心信息的同时进行平台特定的改编。

示例提示

基于我关于“测试中的 AI 代理”的博客文章,为 Twitter 创建一个话题串,并为 LinkedIn 创建一个线程。

生成一份未来两周的每周内容日历,覆盖 Instagram、YouTube 和我的新闻通讯。

将此 YouTube 视频脚本转换为 30 秒的 TikTok 视频脚本。

环境变量

根据需要,可以使用 CONTENT_TONE 和 CONTENT_AUDIENCE 等环境变量来调整输出的语气和目标受众,无需每次都明确要求。

限制

  • 该技能根据输入进行创作,但无法访问实时趋势或实际平台数据(如最佳发帖时间)。
  • 请始终人工审核输出内容,以确保其符合品牌声音和平台政策。

示例

输入:一份关于“时间管理技巧”的博客文章。

输出:

  • Twitter 话题:包含 5 条推文,突出关键技巧,使用相关标签。
  • LinkedIn 帖子:一篇详细文章,讨论时间管理对职业发展的益处。
  • TikTok 脚本:一个 60 秒视频结构,包含快速摘要、3 个技巧和行动号召。

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/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.

api-connector-builder

Build a new API connector or provider by matching the target repo

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