linkedin-content-engine

linkedin-content-engine

MCP server for managing LinkedIn content workflows. It generates images via OpenAI, stores assets in Cloudinary, and queues drafts through Buffer with mandatory human approval.

Category
访问服务器

README

LinkedIn Content Engine

A Claude Code plugin + local MCP server scaffold for producing branded LinkedIn posts and carousel documents, storing creative assets in Cloudinary, generating visuals with OpenAI GPT Image 2, and saving/queueing posts through Buffer.

This repository is intentionally structured as a Claude handoff repo: the architecture, interfaces, safety rules, schemas, prompts, and implementation tasks are defined so Claude Code can finish the production implementation without having to infer the system from a video.

Target workflow

User brief / content idea
        |
        v
Claude skill: linkedin-auto-poster
  - selects post type
  - writes LinkedIn copy
  - creates visual/carousel brief
  - requests approval before side effects
        |
        v
Local MCP server
  |-- OpenAI adapter ------> GPT Image 2 ------> generated PNG slides
  |-- PDF service ---------> carousel PDF
  |-- Cloudinary adapter --> hosted PNG/PDF/thumbnail URLs
  `-- Buffer adapter ------> draft / queue / scheduled LinkedIn post

Design decisions

  1. Claude owns the writing. The skill contains the writing workflow and brand/style references. The MCP server performs deterministic integration work and side effects.
  2. GPT Image 2 is used for visual generation, not for post copy.
  3. Cloudinary is the creative asset store for generated slides, PDFs, thumbnails, and other media.
  4. Buffer is the publishing queue/system of record for multiple prepared posts.
  5. Human approval is mandatory before queueing or scheduling. Generating drafts and assets is allowed; publication is not implicit.
  6. Adapters are replaceable. OpenAI, Cloudinary, and Buffer are isolated behind small interfaces.
  7. Carousel output is a PDF document. Slide images are assembled into a PDF, hosted, then attached to Buffer as a document asset.

Repository layout

.
├── .claude-plugin/plugin.json
├── CLAUDE.md
├── ARCHITECTURE.md
├── IMPLEMENTATION_PLAN.md
├── .env.example
├── package.json
├── tsconfig.json
├── skills/
│   └── linkedin-auto-poster/
│       ├── SKILL.md
│       └── reference/
│           ├── workflow.md
│           ├── writing-guide.md
│           ├── creative-guide.md
│           └── api-contracts.md
├── src/
│   ├── config.ts
│   ├── types.ts
│   ├── adapters/
│   │   ├── openai-image.ts
│   │   ├── cloudinary.ts
│   │   └── buffer.ts
│   ├── services/
│   │   ├── carousel.ts
│   │   └── orchestrator.ts
│   └── mcp/
│       └── server.ts
├── samples/
│   ├── brand-profile.example.json
│   └── post-brief.example.json
├── tests/
│   └── README.md
└── docs/
    └── CLAUDE_HANDOFF_PROMPT.md

Quick handoff to Claude Code

  1. Put this folder in a new Git repository.
  2. Open the repository in Claude Code.
  3. Start with docs/CLAUDE_HANDOFF_PROMPT.md.
  4. Ask Claude to work through IMPLEMENTATION_PLAN.md in order.
  5. Do not add real credentials until the validation/tests are in place.

Once implemented, test the plugin locally with Claude Code's plugin development mode and invoke the skill as:

/linkedin-content-engine:linkedin-auto-poster

Environment variables

Copy .env.example to .env and add secrets locally. .env must remain gitignored.

Current external API assumptions

These assumptions were checked against current vendor documentation when the scaffold was created (August 2026):

  • OpenAI exposes gpt-image-2 for image generation/editing.
  • Cloudinary's Node SDK supports server-side uploads and raw-file storage.
  • Buffer's API is GraphQL at https://api.buffer.com and supports LinkedIn, drafts, queue/scheduling modes, images, and document assets.
  • Claude Code project/plugin skills use SKILL.md; plugin skills live under skills/<name>/SKILL.md and are namespaced by the plugin name.

Before production use, Claude should re-check vendor schemas and SDK package versions rather than assuming this scaffold's examples are frozen forever.

Non-goals

  • Automating LinkedIn by browser scraping or private LinkedIn endpoints.
  • Publishing without explicit user approval.
  • Storing API secrets in repository files.
  • Hard-coding Full Marks Education branding into application code; branding belongs in a configurable profile.

License

MIT

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选