linkedin-announcement-generator

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---
name: linkedin-announcement-generator
description: This skill generates professional LinkedIn announcement text for intelligent textbooks by analyzing book metrics, chapter content, and learning resources to create engaging posts with key statistics, hashtags, and links to the published site. Use this skill when you need to create social media announcements about textbook completion or major milestones.
license: MIT
---

# LinkedIn Announcement Generator

## Overview

This skill automates the creation of professional LinkedIn announcements for intelligent textbooks. It analyzes book metrics from the `docs/learning-graph/` directory, gathers statistics about chapters, concepts, and educational resources, and generates engaging announcement text with relevant hashtags and links to the published site.

The announcements are designed to highlight the scope and completeness of the textbook, showcase its educational features, and attract educators, students, and learning professionals to the content.

## When to Use This Skill

Use this skill when:

- Publishing a completed intelligent textbook to GitHub Pages
- Announcing major milestones (e.g., "First 10 chapters complete!")
- Promoting updated or newly added content
- Sharing the textbook with the educational technology community
- Preparing social media posts for course launches
- Creating announcements for conference presentations or workshops
- Building awareness for open educational resources

## Prerequisites

The intelligent textbook project should have:

- A `docs/learning-graph/book-metrics.md` file containing textbook statistics
- A `mkdocs.yml` file with site_name, site_url, and site_description
- Deployed site on GitHub Pages (or another hosting platform)
- Optional: `docs/learning-graph/chapter-metrics.md` for chapter-level details
- Optional: `docs/course-description.md` for audience and topic information

## Workflow

### Step 1: Gather Book Metadata

Extract key information from the project configuration:

1. Read `mkdocs.yml` to get:
   - `site_name` - Title of the textbook
   - `site_url` - Live site URL (typically GitHub Pages)
   - `site_description` - Brief description of the textbook
   - `repo_url` - GitHub repository URL

2. Read `docs/course-description.md` (if it exists) to get:
   - Target audience (grade level, prerequisites)
   - Subject matter/topic
   - Learning objectives
   - Course context

**Example extraction:**

```yaml
site_name: 'Geometry for High School Students'
site_url: 'https://username.github.io/geometry-course/'
site_description: 'An interactive geometry textbook with MicroSims and quizzes'
```

### Step 2: Analyze Book Metrics

Read and parse `docs/learning-graph/book-metrics.md` to extract:

**Core Metrics:**

- Number of chapters
- Number of concepts in learning graph
- Number of glossary terms
- Number of FAQ questions
- Number of quiz questions
- Number of diagrams
- Number of equations
- Number of MicroSims (interactive simulations)
- Total word count
- Number of hyperlinks
- Equivalent printed pages

**Parse the metrics table:**

Look for the table starting with `| Metric Name | Value | Link | Notes |` and extract values from the second column.

**Handle missing metrics gracefully:**

- If diagrams = 0, mention "includes equations and visual elements" instead
- If quiz questions = 0, omit quiz mention
- If MicroSims = 0, mention "comprehensive content" instead

### Step 3: Determine Textbook Completeness

Calculate the completion status based on metrics:

**Indicators of completeness:**

- Chapters ≥ 8: Substantial textbook
- Total words > 30,000: Comprehensive content
- Quiz questions ≥ 50: Well-assessed
- MicroSims ≥ 5: Interactive elements present
- Equivalent pages > 100: Book-length work

**Status categories:**

- **Complete** (100%): All major components present, ready for use
- **Nearly Complete** (90-99%): Most content done, minor additions pending
- **In Progress** (70-89%): Substantial content, ongoing development