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Linkedin Message Agent

Community
kevinbadi
linkedin-message-agent

Check LinkedIn inbox for new/unread messages and auto-respond using AI. Browser automation via CDP. Logs all conversations to database for tracking. Trigger when asked to check LinkedIn messages, respond to LinkedIn DMs, or monitor LinkedIn inbox.

Overview

Publisherkevinbadi
Repositoryai-os-skills
Skill namelinkedin-message-agent
Stars
73
Forks
29
Bundled files
1
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by kevinbadi on GitHub. Read the source before you install it.

Installation

Install the Linkedin Message Agent AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/kevinbadi/ai-os-skills/tree/main/linkedin-message-agent
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/kevinbadi/ai-os-skills.git /tmp/ai-os-skills
mkdir -p .claude/skills
cp -r /tmp/ai-os-skills/linkedin-message-agent .claude/skills/linkedin-message-agent
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Linkedin Message Agent in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Linkedin Message Agent on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Linkedin Message Agent is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

LinkedIn Message Check & Respond Agent

Monitors LinkedIn inbox for unread messages, reads conversation context, generates contextual AI responses, and sends replies. All via CDP browser automation.

Prerequisites

  • Chrome with CDP running (auto-starts via Clawdbot)
  • Logged into LinkedIn (cookies at cookies/linkedin-zeusbycreatoros.json)
  • ws and pg npm packages
  • DATABASE_URL env var for logging

Quick Usage

Check and respond to all unread messages

bash
node skills/linkedin-message-agent/scripts/linkedin-message-agent.js

Check only (don't send responses)

bash
node skills/linkedin-message-agent/scripts/linkedin-message-agent.js --dry-run

Limit number of conversations to process

bash
node skills/linkedin-message-agent/scripts/linkedin-message-agent.js --limit 5

Workflow

  1. Navigate to LinkedIn Messaging inbox
  2. Scan for unread conversations (indicated by bold/unread indicators)
  3. For each unread conversation:
    • Click into the conversation
    • Read the last few messages for context
    • Determine if a response is needed (skip automated/spam)
    • Generate a contextual response as Kev's Assistant (CreatorOS AI employee)
    • Type and send the response
    • Log the conversation to the database
  4. Output summary of all processed conversations

Response Persona

Kev's Assistant responds as a professional AI employee at CreatorOS:

  • Friendly, helpful, concise
  • Interested in AI, automation, startups
  • Can discuss CreatorOS services if relevant
  • Avoids overselling, stays conversational
  • If someone asks to meet/call, suggests they connect with Kevin

CLI Options

FlagDefaultDescription
--dry-runfalseRead messages only, don't respond
--limit10Max conversations to process
--cdp-port18800Chrome CDP port

Database

Logs to social_messages table:

  • platform: 'linkedin'
  • direction: 'inbound' or 'outbound'
  • sender_name, content, conversation context

Safety

  • Max 10 conversations per run (avoid spam detection)
  • 5-10s delays between conversations
  • Never responds to obvious spam/automated messages
  • Dry-run mode to verify before enabling auto-respond

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Linkedin Message Agent AI skill do?

Check LinkedIn inbox for new/unread messages and auto-respond using AI. Browser automation via CDP. Logs all conversations to database for tracking. Trigger when asked to check LinkedIn messages, respond to LinkedIn DMs, or monitor LinkedIn inbox.

Why use Linkedin Message Agent on TypingMind?

Because you install it once and use it with any model. Linkedin Message Agent is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Linkedin Message Agent in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kevinbadi/ai-os-skills/tree/main/linkedin-message-agent. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Linkedin Message Agent?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Linkedin Message Agent?

As many as you like. As long as a model supports skills, you can use Linkedin Message Agent with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Linkedin Message Agent AI skill free?

It is published on GitHub by kevinbadi. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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