content-analyzer

name	content-analyzer
description	Use when analyzing published content (blog posts, social media, newsletters) for sentiment, structural quality, hook effectiveness, readability, topic classification, and engagement correlation. Invoke for daily post analysis, content audits, or engagement driver identification.
version	1.0.0
model	sonnet
invoked_by	both
user_invocable	true
tools	["Read","Write","Bash","WebSearch","WebFetch","Grep","Glob"]
agents	["post-analyzer-agent"]
category	domain-specific
tags	["content","analytics","nlp","sentiment","readability","engagement","hooks","topics","seo"]
verified	true
lastVerifiedAt	2026-03-21
best_practices	["Always fetch engagement metrics alongside content text","Classify hook type before analyzing engagement correlation","Store all analysis results in content-analytics.json for trend tracking","Compare current results against 7-day and 30-day baselines","Report confidence levels alongside every finding"]
error_handling	graceful
streaming	supported
source	builtin
trust_score	100
provenance_sha	ebf8346cd1354006

Content Analyzer

Overview

Multi-dimensional content analysis skill that transforms published text into actionable engagement intelligence. The analysis pipeline covers six dimensions: sentiment detection, readability scoring, structural analysis, hook classification, topic identification, and engagement correlation.

Core principle: Content quality is measurable. Every post has structural attributes that correlate with engagement outcomes. This skill quantifies those attributes and tracks them over time.

When to Invoke

Skill({ skill: 'content-analyzer' });

Invoke when:

• Analyzing a published blog post, article, or social media post
• Running a daily content performance audit
• Identifying what makes top-performing content succeed
• Building a content strategy based on historical performance data
• Diagnosing why a post underperformed expectations
• Comparing content attributes across a portfolio of published work

Do NOT invoke for:

• Pre-publication copyediting (use a writing or editing skill instead)
• SEO keyword research without existing content (use seo-optimization)
• Social media scheduling or publishing automation

Six-Dimension Analysis Pipeline

Dimension 1: Sentiment Analysis

Classify the emotional tone of the content across three axes:

Polarity: Positive / Negative / Neutral / Mixed Emotion: Inspiration, Urgency, Curiosity, Empathy, Authority, Humor, Fear Intensity: 1 (subtle) to 5 (intense)

Scoring method:

• Read the full text and identify the dominant emotional register
• Check for tonal shifts (e.g., problem-heavy opening -> optimistic conclusion)
• Score intensity based on word choice strength and punctuation patterns
• Flag mixed sentiment (common in "problem-solution" posts)

Output:

{
  "sentiment": {
    "polarity": "positive",