linkedin-announcement-generator

What this skill does

# 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.json` file containing the canonical
  textbook statistics (produced by the book-metrics tool). If it is missing,
  generate it first with `bk-generate-book-metrics` (or the
  `python3 "$BK_HOME/src/book-metrics/book-metrics.py" docs` fallback).
- 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 the canonical metrics file `docs/learning-graph/book-metrics.json` — do
**not** parse the human-readable `book-metrics.md` table (brittle) or recount
content yourself. This is the same source the README and case-study skills use,
so the numbers stay consistent across every artifact.

```bash
python3 - <<'PY'
import json, pathlib, sys
p = pathlib.Path("docs/learning-graph/book-metrics.json")
if not p.exists():
    sys.exit("book-metrics.json missing — run bk-generate-book-metrics first")
m = json.loads(p.read_text())["metrics"]
print(json.dumps(m, indent=2))
PY
```

**Core Metrics (keys in the `metrics` object):**

- `chapters` — Number of chapters
- `concepts` — Number of concepts in the learning graph
- `glossaryTerms` — Number of glossary terms
- `faqs` — Number of FAQ questions
- `quizQuestions` — Number of quiz questions
- `diagrams` — Number of diagrams
- `equations` — Number of equations
- `microsims` — Number of MicroSims (interactive simulations)
- `words` — Total word count
- `links` — Number of hyperlinks
- `equivalentPages` — Equivalent printed pages
- `developmentStage` — Author-declared publication stage

If `book-metrics.json` does not exist, regenerate it with
`bk-generate-book-metrics` before reading. The format is defined by
`src/book-metrics/book-metrics.schema.json`.

**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
- **Early Release** (< 70%): Initial chapters available, more coming

Choose appropriate language for the announcement based on status.

### Step 4: Craft the Announcement Structure

Create a LinkedIn post with the following components:

**1. Opening Hook (1-2 sentences)**

Start with an attention-grabbing statement that:
- Announces the textbook completion/release
- Mentions the topic and audience
- Highlights what makes it special

**Examples:**

- "Excited to share a new open educational resource for [AUDIENCE]!"
- "Just published: An AI-generated interactive textbook on [TOPIC]!"
- "Thrilled to announce the completion of [TEXTBOOK NAME]!"

**2. Content Description (2-3 sentences)**

Explain what the textbook covers and its unique features:
- Educational framework (Bloom's Taxonomy, concept dependencies)
- Interactive elements (MicroSims, quizzes)
- Technology stack (MkDocs, p5.js, AI-generated)
- Target audience and prerequisites

**Example:**

```
This intelligent textbook on [TOPIC] is designed for [AUDIENCE]. Built using MkDocs Material and AI-assisted content generation, it incorporates learning graphs, concept dependencies, and interactive MicroSims to make [TOPIC] accessible and engaging.
```

**3. Key Metrics (bulleted list)**

Present impressive statistics to demonstrate scope:

```
📊 By the numbers:
• [X] chapters covering [TOPIC AREAS]
• [Y] concepts in the learning graph
• [Z] interactive MicroSims (p5.js simulations)
• [Q] quiz questions for self-assessment
• [G] glossary terms with ISO 11179-compliant definitions
• [W] total words (~[P] equivalent printed pages)
```

**Formatting tips:**

- Use emoji bullets (📊, 📚, 🎓, ⚡, 🔬) for visual appeal
- Round large numbers (225,182 → 225,000)
- Group related metrics together
- Highlight the most impressive numbers

**4. Technology and AI Disclosure (1-2 sentences)**

Be transparent about AI involvement and technology:

```
Generated using Claude AI skills and the intelligent textbook framework, this open-source project demonstrates how AI can augment educational content creation while maintaining quality and pedagogical rigor.
```

**5. Call to Action (1 sentence)**

Direct readers to the site:

```
Explore the full textbook here: [SITE_URL]
```

**6. Hashtags (8-15 tags)**

Include relevant hashtags for discoverability:

**Standard hashtags:**

- `#AI` / `#ArtificialIntelligence`
- `#GenAI` / `#GenerativeAI`
- `#Education` / `#EdTech` / `#EducationalTechnology`
- `#OpenEducation` / `#OER` (Open Educational Resources)
- `#ELearning` / `#OnlineLearning`

**Content-specific hashtags:**

- `#Textbook` / `#InteractiveTextbook`
- `#MicroSims` / `#Simulations`
- `#Visualizations` / `#DataViz`
- `#Diagrams` / `#Infographics`
- `#Quizzes` / `#Assessment`

**Technology-specific hashtags:**

- `#MkDocs` / `#MaterialDesign`
- `#p5js` / `#JavaScript`
- `#Python`
- `#ClaudeAI` / `#AnthropicClaude`

**Domain-specific hashtags:**

Add 2-4 hashtags specific to the subject matter:
- Math: `#Mathematics`, `#Geometry`, `#Calculus`, `#Algebra`
- Science: `#Physics`, `#Chemistry`, `#Biology`
- CS: `#Programming`, `#ComputerScience`, `#DataScience`
- History: `#History`, `#WorldHistory`, `#AmericanHistory`

**Professional/Academic hashtags:**

- `#LMS` / `#LearningManagementSystem`
- `#CurriculumDesign`
- `#InstructionalDesign`
- `#STEM` / `#STEMeducation`
- `#HigherEd` / `#K12Education`

**Total hashtag count:** Aim for 10-15 hashtags for o