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

Related MCP server: linkedin-mcp

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