linkedin-mcp: Skill for OpenCode

.opencode/skills/linkedin-mcp/SKILL.md

linkedin-mcp is a skill for OpenCode from EgiStr/linkedin-mcp. It costs 0 tokens per session (1,477 once invoked), scanned A, original, MIT.

A set of instructions that lets AI agents work with LinkedIn through its official API. LinkedIn is a professional social network used for profiles, posts, and a feed.

In plain words
What is it for?
Viewing a profile, managing the feed, creating posts with images, and connecting LinkedIn to Claude Code, OpenCode, or Cursor.
Why use it?
It gives the agent guidance for common LinkedIn tasks and explains the setup needed for login and posting. This avoids treating LinkedIn as an undefined external service.

Skill for OpenCode

Written for OpenCode: installed under .opencode/. Also seen: mentions Claude Code; mentions OpenCode.

This is EgiStr/linkedin-mcp's own configuration. It tells OpenCode how to work on linkedin-mcp itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything linkedin-mcp configures →

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is LINKEDIN_ACCESS_TOKEN=AQX_... node dist/index.js.

Reuse

Borrowing it

Nothing to install: this file belongs to EgiStr/linkedin-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/EgiStr/linkedin-mcp/main/.opencode/skills/linkedin-mcp/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/EgiStr/linkedin-mcp

Made for: OpenCode.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for linkedin-mcp

README.md
[![agentmods](https://agentmods.dev/badge/skills/egistr/linkedin-mcp/linkedin-mcp/github.svg)](https://agentmods.dev/skills/egistr/linkedin-mcp/linkedin-mcp)
Your own site
<a href="https://agentmods.dev/skills/egistr/linkedin-mcp/linkedin-mcp"><img src="https://agentmods.dev/badge/skills/egistr/linkedin-mcp/linkedin-mcp/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for linkedin-mcp

Your own site · 80×15
<a href="https://agentmods.dev/skills/egistr/linkedin-mcp/linkedin-mcp"><img src="https://agentmods.dev/badge/skills/egistr/linkedin-mcp/linkedin-mcp.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,477 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00000 $0.01477
Opus 5 $0.00000 $0.00739
Sonnet 5 $0.00000 $0.00295
Haiku 4.5 $0.00000 $0.00148

Measured 9d ago against content hash 8d2635beb861, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

linkedin-mcp scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.opencode/skills/linkedin-mcp/SKILL.md · 180 lines

How it starts

The opening of the file, as written. The whole thing — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.

LinkedIn MCP Server Skill

Use this skill when the user wants to interact with LinkedIn, post content, view their profile, manage their LinkedIn feed, or integrate LinkedIn with AI agents like Claude Code, OpenCode, or Cursor.

Trigger phrases: "linkedin", "linkedin mcp", "posting linkedin", "buat postingan linkedin", "linkedin profile", "social media", "koneksi linkedin"


Overview

LinkedIn MCP Server is an open-source MCP (Model Context Protocol) server that connects AI agents to LinkedIn through the official LinkedIn REST API. It provides tools for profiles, posts (with image upload), feed, and OAuth PKCE authentication.

GitHub: https://github.com/EgiStr/linkedin-mcp npm: @egistr/linkedin-mcp

Prerequisites

  1. Node.js >= 18 — verify with node --version
  2. LinkedIn Developer App — create at https://www.linkedin.com/developers/apps
    • Add product: Sign In with LinkedIn using OpenID Connect
    • Add product: Share on LinkedIn (untuk fitur posting)
    • Note: Client ID dan Client Secret

Installation

Option A: Quick Run (npx)

LINKEDIN_ACCESS_TOKEN=AQX_... npx @EgiStr/linkedin-mcp

Option B: Install from GitHub

git clone https://github.com/EgiStr/linkedin-mcp.git
cd linkedin-mcp
npm install
npm run build
LINKEDIN_ACCESS_TOKEN=AQX_... node dist/index.js

Option C: Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "linkedin": {
      "command": "npx",
      "args": ["-y", "@EgiStr/linkedin-mcp"],
      "env": {
        "LINKEDIN_ACCESS_TOKEN": "AQX_..."
      }
    }
  }
}

Option D: OpenCode

Add to opencode.json:

{
  "mcpServers": {
    "linkedin": {
      "command": "node",
      "args": ["path/to/linkedin-mcp/dist/index.js"],
      "env": {
        "LINKEDIN_ACCESS_TOKEN": "AQX_..."
      }
    }
  }
}

Authentication

Method 1: Access Token (Quick)

  1. Buka https://www.linkedin.com/developers/tools/oauth/token-generator
  2. Pilih app → centang scopes: openid, profile, email, w_member_social
  3. Generate → copy token
  4. Set sebagai LINKEDIN_ACCESS_TOKEN env var

Read the full file on GitHub · 180 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 180 lines · 0 tokens per session scan A 8d2635beb861

Subscribe to this mod's changes

linkedin-mcp is a skill published in the GitHub repository EgiStr/linkedin-mcp (0 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,477 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens