setup

Automate LinkedIn Commander onboarding with interviews, post analysis, and data seeding.

8|Updated Mar 12, 2026

Search Tags:#content strategy #onboarding #data analysis #crm #setup #linkedin

Ready to run

npx skills add https://github.com/sabania/linkedin-cli --skill setup-sabania

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: setup
Source: https://github.com/sabania/linkedin-cli/tree/main/plugin/skills/setup
Command: npx skills add https://github.com/sabania/linkedin-cli --skill setup-sabania

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires openpyxl, and includes scripts (resource) components.

What problem does it solve?

This Skill automates the comprehensive setup and initial data seeding for the LinkedIn Commander, ensuring the system starts with real-world context and is immediately operational.

Core Features & Use Cases

Guided Interview: Collects user goals, ICP, content strategy, and competitor information conversationally.
Historical Data Analysis: Analyzes past LinkedIn posts to establish performance baselines and identify initial content patterns.
Contact Seeding: Identifies and scores key engagers from historical posts to seed the 'hot contacts' list.
System Generation: Creates essential configuration files (config.json, CLAUDE.md) and populates data storage (e.g., Excel sheets) with initial findings.
Use Case: A new user launches LinkedIn Commander and runs /setup. The Skill guides them through defining their business objectives, target audience, and content preferences, then analyzes their last 30 posts to understand what resonates, populating the system so the first /auto command provides meaningful insights.

Quick Start

Run the setup skill to begin the deep onboarding process for LinkedIn Commander.

Frequently Asked Questions about setup

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automate onboarding and initial data population for a LinkedIn content strategy tool?▼

LinkedIn onboarding is automated by conducting conversational interviews to define goals and ICP, analyzing historical post performance, and seeding the CRM contact database. This generates configuration files so the system starts warm with actionable data.

What is the best way to establish a performance baseline from historical LinkedIn posts?▼

Establishing a LinkedIn performance baseline is done by analyzing your past posts to identify content patterns and scoring high-value engagers. This historical data analysis populates Excel data stores to provide immediate, meaningful insights.

Do I need openpyxl to generate CRM data stores for LinkedIn contact seeding?▼

Yes, openpyxl is required to generate and populate Excel data stores for CRM contact seeding. This dependency allows the setup process to create configuration files and save initial findings regarding high-value engagers.

Can I define my Ideal Customer Profile and content strategy conversationally during setup?▼

Yes, you can define your Ideal Customer Profile and content strategy conversationally through a guided interview. The setup process collects your business objectives, target audience, and competitor information to configure the system automatically.

Why does a cold CRM database limit automated LinkedIn outreach?▼

A cold CRM database lacks baseline performance data and scored contacts, limiting automated LinkedIn outreach. Seeding the database with high-value engagers ensures the first automated commands yield meaningful insights.